DOI: 10.3897/jucs.156911

Authors: Gamze Cabadag, Ali Degirmenci, Omer Karal

Abstract: Fast Fourier transform (FFT) is a widely used method for frequency estimation in electronic support systems. However, when the intermediate frequency (IF) of the radar signal is not an exact multiple of the FFT resolution, the correct frequency value cannot be obtained in the FFT computation. Therefore, interpolation methods are used to improve the frequency obtained from the FFT result. In this study, 12 different interpolation techniques (Jain, corrected Jain, Quinn, improved Quinn, Jacobsen, Macleod, Ding, Voglewede, mobile industrial (MI), Candan, rectangular-window-based interpolation, and Hanning window based interpolation) used in the literature have been extensively analyzed on radar signals contaminated with Laplace and Gaussian noise at different SNR values. In addition, in order to observe the performance of the techniques in different frequency bands, the bandwidth was changed to between 100 and 1000 MHz, and 100 Monte Carlo simulations were applied for each frequency. From the experimental analysis results, the improved Quinn technique showed the best performance for both noises. In addition to accuracy evaluations, the computational complexity of each interpolation technique was analyzed in terms of floating-point operations (FLOPs). The FLOPs cost of the FFT was uniformly included in all methods to ensure fair comparison. Results showed that while all techniques operate within a similar computational range, methods like Jain and Candan exhibit lower FLOPs costs, whereas the improved Quinn method, despite its higher complexity, achieves the best estimation accuracy.

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DOI: 10.3897/jucs.143527

Authors: Nguyen Thi Hoang Phuong, Phan Minh Nhat, Nguyen Van Hieu

Abstract: Near-infrared (NIR) spectroscopy has emerged as a valuable analytical technique for assessing the composition and quality of various materials. This study proposes NirMACNet, a novel convolutional neural network (CNN) architecture that incorporates a residual-based multi-scale kernel mechanism for enhanced prediction of compositional attributes. The model is evaluated on two distinct NIR spectral datasets, milk and soil, to demonstrate its generalization capability across domains. By leveraging multiscale kernel operations, NirMACNet effectively captures diverse spectral patterns, while its deep architecture facilitates comprehensive feature extraction. To mitigate performance degradation commonly associated with deeper networks, residual learning is employed. Experimental results indicate that NirMACNet consistently outperforms state-of-the-art methods in terms of prediction accuracy. Future work will involve expanding the diversity of training datasets and investigating alternative architectural enhancements to further improve model robustness and applicability.

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DOI: 10.3897/jucs.146407

Authors: Zhichao Chen, Zixi Han, Bingqing Shen, Yuxin Zeng, Min Wang, Hongming Cai, Minqi Wang

Abstract: Abstract: The Integrated Process Planning and Scheduling (IPPS) problem has long been a practical concern in industry, with numerous problem modelling and solving methods to optimize multiple objectives, such as makespan and cost. As yet, existing modelling methods have difficulties in satisfying new modelling requirements for adequately representing real complex manufacturing jobs. This paper proposes a novel computing framework for modelling and solving complex IPPS problems based on a Supplier-Input-Process-yield-Output-Customer-Resource-Control (called SIPyOC-RC) model. This framework includes a SIPyOC-RC modelling method to address the modelling adequacy problem. With the proposed method, complex IPPS problems in real manufacturing scenarios, such as commercial airplane component production and assembly, can be modeled with the key demanding features. Next, the SIPyOC-RC model can be transformed into a Constraint Programming (CP) model, which can then be solved with any CP-solver. Based on an implemented testbed, the modelling adequacy and computation performance of the proposed approach has been evaluated. The experiment results show that it can successfully solve complex IPPS problems without significant computation cost. Moreover, its compatibility with conventional IPPS problems is also discussed. Overall, the outcome of this study contributes both theoretical methodology and practical experience to industrial computing.

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DOI: 10.3897/jucs.141629

Authors: Samet Aymaz

Abstract: oronary artery disease (CAD) is a major global health issue. Early detection plays a crucial role in reducing risk and improving patient outcomes. This study proposes a novel, efficient approach to CAD diagnosis by integrating a histogram-based feature selection method with a specially designed long short-term memory (LSTM) classifier. The method is evaluated on two benchmark datasets: Z-Alizadeh Sani and Cleveland. Imbalanced class distribution, a common challenge in medical datasets, is addressed using the synthetic minority over-sampling technique (SMOTE). The proposed feature selection technique offers a fast and simple alternative to traditional optimization methods like particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), and the whale optimization algorithm (WOA), which typically require extensive parameter tuning and longer processing times. The histogram-based method selects features based on their distribution similarity to a Gaussian profile, aiming to enhance classification performance and computational efficiency. The selected features are then classified using a custom-designed LSTM architecture optimized through Grid Search and validated via k-fold cross-validation (k-fold). The effectiveness of the proposed method is demonstrated by comparing it with other feature selection approaches using metrics such as accuracy, precision, sensitivity, and the F1-score (f-score). Experimental results show that the histogram-based method significantly improves classification accuracy and reduces computational time. This approach offers a promising, low-cost, and scalable solution for CAD diagnosis, especially in resource-constrained settings, and provides valuable contributions to the field of medical data analysis.

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DOI: 10.3897/jucs.130590

Authors: Eugen Valentin Butilă, Mihai Burlacu, Răzvan Gabriel Boboc, Robertas Damaševičius

Abstract: As a relatively new technology that has gone through several iterations in the last decade, virtual reality (VR) applications have been used in a plethora of activities pertaining to various sciences, including anthropology. In this paper, we expound a bibliometric analysis of the reviews and research articles regarding the use of VR applications in anthropology between 2010 and 2023. The analysed publications were obtained from the Scopus database, and Microsoft Excel and VOSViewer were used to analyse the data. Utilizing bibliometric methods, the analysis encompasses a thorough examination of scholarly publications, identifying and scrutinizing prominent journals, prolific authors, affiliated institutions, and key research themes within the realm of VR applications in anthropology. The objective is to provide a systematic and insightful overview of the evolution, current state, and emerging trends in the integration of VR within the anthropo-logical discourse, shedding light on the interdisciplinary nature and impact of this innovative technology on anthropological research and practice.

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DOI: 10.3897/jucs.119828

Authors: Seyedeh Aridis Ahadi, Kian Jazayeri, Sahand Tebyani

Abstract: The identification of individuals who indicate suicidal behaviors on social media platforms has become more significant in recent years. The utilization of textual data may help in the development of systems aimed at predicting individuals' mental health. This article proposes an integrated framework for the identification of suicidal thoughts in social media through the implementation of a layered classifier model consisting of a convolutional neural network (CNN) and a long short-term memory (LSTM) model. Various combinations of embedding techniques, activation functions, and solver algorithms are applied to the network. The mixture of these techniques forms 82 distinct methodologies employed, followed by comparing the results obtained. A collection of approximately 60,0000 user posts from 2018 to 2020 was compiled from Reddit for the study. It has resulted in the combination of TF-IDF (word embedding), RReLU (activation function), and Adam (solver algorithm) reaching the highest overall performance. The model achieved impressive accuracy, F1 Score, and AUC of 86%, with precision and recall score of 91% and 82% respectively. It was fitted in just 8.69 seconds, demonstrating its time efficiency as well. This approach has great potential for creating a platform in real life to not only reduce the social impacts of suicidality and mental illness, but also increase social access to mental health resources for all individuals.

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DOI: 10.3897/jucs.114607

Authors: Marija Kuštelega, Renata Mekovec, Ahmed Shareef

Abstract: As technology advances and becomes more extensively used, digital twins are likely to play an increasingly crucial role in defining the future of industry, trade, and society. Despite the stated advantages and potential of digital twin technology, certain research and implementation gaps exist, which have hindered the adoption and advancement of digital twins. This study investigates how current research on the digital twin implementations has been positioned in front of practical challenges focused on privacy and security issues. The research method adopted was a systematic literature review, employing the PRISMA approach. A total of 47 publications were identified and analyzed. The results indicate that the privacy and security challenges for digital twin implementation are complicated and may be divided into six primary groups: (1) data privacy, (2) data security, (3) data management, (4) data infrastructure and standardization, (5) ethical and moral issues, (6) legal and social issues.

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DOI: 10.3897/jucs.116249

Authors: Rupesh Kumar Verma, A. J. Khan, Sunil Kashyap, Manoj Kumar Chande

Abstract: This paper presents a comprehensive survey of the certificateless aggregate signature scheme in terms of their computational performance and security. This signature scheme is of significant interest because of its relevance in various domains such as Internet of Things (IoT), Vehicular Ad-hoc Networks (VANETs), E-Healthcare Medical Systems, and Mobile Healthcare Systems. Further, we point towards potential future directions for research and development in this field and conclude our study. This survey serves as a valuable resource for expanding the horizons of this scheme by envisioning new applications.

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DOI: 10.3897/jucs.111879

Authors: Sahar Idwan, Junaid Ahmed Zubairi, Syed Ali Haider, Wael Etaiwi

Abstract: Traffic management is one of the major factors in growth strategy formulation in urban centers across the globe. The increasing population and, therefore, the increase in the number of vehicles on roads in urban centers cause congested traffic patterns. These patterns typically emerge on intersections in busy city roads at various times during the day, especially during peak hours. A direct consequence of congestion is the increase in commute time and pollution. This paper presents a hierarchical graph-based congestion control (HGCC) method. Congestion values are set and evaluated as a two-level hierarchical graph. The least congestion path algorithm (LCP)is integrated with the HGCC to compute the optimal route between source and destination. The experimental results for a Manhattan-like grid network, together with the paired-sample t-test, show that the proposed method is efficient in achieving good congestion-avoiding routes.

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DOI: 10.3897/jucs.102428

Authors: Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, Jean-Philippe Georges

Abstract: The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.

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DOI: 10.3897/jucs.89703

Authors: Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza

Abstract: Pendulum-Driven Spherical Robots are a type of spherical robot whose motion is achieved by controlling two motors for longitudinal and lateral motion. This configuration makes the robot a non-holonomic system, which impedes it from navigating directly towards a target. In addition, controlling its motion on inclined irregular surfaces is also an issue that has not received much attention. In this work, we addressed these two issues by proposing a methodology to control both motors using PID controllers. However, we propose tuning the controller’s gains using stochastic signals for the longitudinal controller because by varying the motor’s torque, the robot is more susceptible to destabilization in combination with a classical gain tuning methodology for the second controller. Our results indicate that this enables the robot to perform motion on inclined irregular surfaces. We also propose using semicircular trajectories to plan the robot’s motion to reach a target successfully even when moving on inclined irregular surfaces. We have carried out experiments in the Webots simulator, showing that our approach does not overshoot while reaching a settling time of almost 0. These results outperform the Ziegler-Nichols PID controller.

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DOI: 10.3897/jucs.97743

Authors: Xiaojuan Liao, Hui Zhang, Miyuki Koshimura, Rong Huang, Fagen Li

Abstract: Restricted preemption plays a crucial role in reducing total completion time while controlling preemption overhead. A typical version of restricted preemptive models is k-restricted preemptive scheduling, where preemption is only allowed after a task has been continuously processed for at least k units of time. Though solving this problem of minimizing the makespan on parallel machines is NP-hard in general, it is of vital importance to obtain the optimal solution for small-sized problems, as well as for evaluation of heuristics. This paper proposes optimal strategies to the aforementioned problem. Motivated by the dramatic speed-up of Boolean Satisfiability (SAT) solvers, we make the first step towards a study of applying a SAT solver to the k-restricted scheduling problem. We set out to encode the scheduling problem into propositional Boolean logic and determine the optimal makespan by repeatedly calling an off-the-shelf SAT solver. Moreover, we move one step further by encoding the problem into Partial Maximum Satisfiability (PMS), which is an optimized version of SAT, so that the explicit successive calls of the solver can be eliminated. The optimal makespan of the problem and the performance of the proposed methods are studied experimentally. Furthermore, an existing heuristic algorithm is evaluated by the optimization methods.

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DOI: 10.3897/jucs.96377

Authors: Ahmad Abusukhon, Ala Al-Fuqaha, Belal Hawashin

Abstract: Water-pipeline leakage is one of the most common problems that depletes water supplies. Countries like Jordan, which are really experiencing a water deficit, are particularly concerned about this issue. The lack of monitoring tools makes the underground water-pipeline leakage a challenge since the pipelines are invisible. Besides, reducing the amount of time needed to precisely detect and locate the leak is another challenge. If not reduced, the aforementioned element has an effect on cost. A small broken water distribution line costs $64,000 per year. In Jordan, water leakage costs $1.7 million. This expense can be significantly decreased using an effective early water leak detection system. In this paper, we proposed an efficient internet of things system for detecting water-pipeline leakage based on a shielded pipeline, a NodeMCU, a soil moisture sensor, and the Firebase database. We created a baseline system, and then we tested and evaluated the proposed system when various types of soil are used. Furthermore, this paper compared several strategies offered for detecting water-pipeline leaking including the proposed system. The results showed that the proposed system reduced the time required for detecting water-pipeline leakage by 70% and the system hardware cost by 83% compared with the earlier work. It was difficult to compare the total cost of the proposed system with the total cost of previous works since the total cost is not calculated in their systems. Besides, in this paper, we proposed an IoT system for securing the underground water pipelines from adversaries.

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DOI: 10.3897/jucs.96293

Authors: Mitchell Jensen, Khamael Al-Dulaimi, Khairiyah Saeed Abduljabbar, Jasmine Banks

Abstract: To identify autoimmune diseases in humans, analysis of HEp-2 staining patterns at cell level is the gold standard for clinical practice research communities. An automated procedure is a complicated task due to variations in cell densities, sizes, shapes and patterns, overfitting of features, large-scale data volume, stained cells and poor quality of images. Several machine learning methods that analyse and classify HEp-2 cell microscope images currently exist. However, accuracy is still not at the level required for medical applications and computer aided diagnosis due to those challenges. The purpose of this work to automate classification procedure of HEp-2 stained cells from microscopic images and improve the accuracy of computer aided diagnosis. This work proposes Deep Convolutional Neural Networks (DCNNs) technique to classify HEp-2 cell patterns at cell level into six classes based on employing the level-set method via edge detection technique to segment HEp-2 cell shape. The DCNNs are designed to identify cell-shape and fundamental distance features related with HEp-2 cell types. This paper is investigated the effectiveness of our proposed method over benchmarked dataset. The result shows that the proposed method is highly superior comparing with other methods in benchmarked dataset and state-of-the-art methods. The result demonstrates that the proposed method has an excellent adaptability across variations in cell densities, sizes, shapes and patterns, overfitting features, large-scale data volume, and stained cells under different lab environments. The accurate classification of HEp-2 staining pattern at cell level helps increasing the accuracy of computer aided diagnosis for diagnosis process in the future.

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DOI: 10.3897/jucs.82370

Authors: Sercan Yalçın, Musa Eşit, Mehmet İshak Yuce

Abstract: Climate forecasting plays an important role for human life in many areas such as water management, agriculture, natural hazards including drought and flood, tourism, business, and regional investment. Estimating these data is a difficult task as the time series climate parameter values vary monthly and seasonally. Therefore, predicting climate parameters based on learning and artificial intelligence is important to long-term efficient results in these fields. For this purpose, in this study, a time-series based Long Short-Term Memory (LSTM) deep neural network is proposed to predict future climate in Çankırı and Adıyaman cities in Turkey. With the help of this network, the average temperature, relative humidity, and precipitation values, which are known as the most effective climate parameters, have been estimated. An improved Particle Swarm Optimization (PSO) technique is also proposed to optimize input weight values of the LSTM deep network, and reduce the estimation errors. The proposed algorithm is compared with deep models of LSTM variants based on Root Mean Square Error (RMSE), Mean Absolute Deviation (MADE), and Mean Absolute Percentage Error (MAPE) metrics. The proposed adaptive LSTM-PSO and non-adaptive LSTM-PSO models achieved at RMSE 0.98 and 1.05 for temperature, 1.19 and 1.27 for relative humidity, and 4.21 and 4.67 for precipitation estimation, respectively. The RMSE is %7 lower with the proposed adaptive LSTM-PSO method than proposed non-adaptive LSTM-PSO method.

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DOI: 10.3897/jucs.76579

Authors: Natasa Koceska, Saso Koceski

Abstract: With the increased aging population, and declined support from the families, societies will need new tools to ensure the well-being of the elderly. Many of them would prefer living at home, but they will need help and assistance from someone. Technological innovations in the field of robotic systems can be used to enable independent living, to prolong the life of the elderly in their familiar home environments, to maintain the social connections by reducing social isolation and to improve the quality of life in general. In this paper, we present the design and validation of a low-cost mobile robot system that can assist elderly and professional caregivers in everyday activities. The robot structure and its control objectives are described in detail. The developed assistive telepresence robot was tested in simulation and experimentally. On field experiments were conducted in real environment, with potential end users, which is a major advantage of this study. The results of the evaluation were very satisfactory and have shown that participants can operate the robot safely and efficiently. The participants were very satisfied with the performance and features of the robot.

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DOI: 10.3897/jucs.80750

Authors: Dagmara Łapczyńska, Konrad Łapczyński, Anna Burduk, Jose Machado

Abstract: The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

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DOI: 10.3897/jucs.2020.073

Authors: Izabela Rojek, Dariusz Mikołajewski, Piotr Kotlarz, Alžbeta Sapietová

Abstract: This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

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DOI: 10.3897/jucs.2020.065

Authors: Shuangbu Wang, Yu Xia, Lihua You, Jianjun Zhang

Abstract: Curve network reconstruction from a set of unorganized points is an important problem in reverse engineering and computer graphics. In this paper, we propose an automatic method to extract curve segments and reconstruct curve networks from unorganized spatial points. Our proposed method divides reconstruction of curve networks into two steps: 1) detecting nodes of curve segments and 2) reconstructing curve segments. For detection of nodes of curve segments, we present a principal component analysis-based algorithm to obtain candidate nodes from unorganized spatial points and a Euclidean distance-based iterative algorithm to remove peripheral nodes and find the actual nodes. For reconstruction of curve segments, we propose an extraction algorithm to obtain the points on each of curve segments. We present quite a number of examples which use our proposed method to reconstruct curve networks from unorganized spatial points. The results demonstrate the effectiveness of our proposed method and its advantages of good automation and high reconstruction efficiency.

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DOI: 10.3897/jucs.2020.061

Authors: Augusto C. de Castro Barbosa, Carlos De Moura, Jhoab De Negreiros, J. Mesquita de Souza Aguiar

Abstract: This work deals with a question in the mathematical modelling for the temperature evolution in a bar, for a long time linked as an inverse problem. The onedimensional model is the parabolic partial differential equation ut = α uxx, known as the heat diffusion equation. The classic direct problem (DP) involves this equation coupled to a set of constraints: initial and boundary conditions, in such a way as to guarantee existence of a unique solution. The data completion (DC) problem hereby considered may be described as follows: the temperature at one of the bar extreme points is unknown but there is a fixed interior point where it may be measured, for all time. Finite difference algorithms (FDA) were tested to approximate the solution for such a problem. The important point to be emphasized is that FDA may show up distinct performances when applied to either DP or DC, which is due to the way the discrete variables follow up the mesh steps - advancing in time, for the first case, on the space direction, for the other.

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DOI: 10.3897/jucs.2020.020

Authors: Jeppe Rasmussen, Lars Hvam, Katrin Kristjansdottir, Niels Mortensen

Abstract: Product configuration systems (PCSs) are increasingly being used in various industries to manage product knowledge and create the required specifications of customized products. Companies applying PCS face significant challenges in modelling, structuring and documenting the systems. Some of the main challenges related to PCSs are formalising product knowledge conceptually and structuring the product features. The modelling techniques predominantly used to visualise and structure PCSs are the Unified Modelling Language (UML) notations, Generic Bill of Materials (GBOM) and Product Variant Master (PVM), associated with class collaboration cards (CRC-cards). These methods are used to both analyse and model the products and create a basis for implementation to a PCS by using an object-oriented approach. However, the modelling techniques do not consider that most commercial PCSs are not fully object-oriented, but rather, they are expert systems with an inference engine and a knowledge base; therefore, the constructed product models require modifications before implementation in the configuration software. The consequences are that what is supposedly a feasible structure of the product model is not always appropriate for the implementation in standard PCS software. To address this challenge, this paper investigates the best practice in modelling and implementation techniques for PCSs in standard software and alternative structuring methods used in object-oriented software design. The paper proposes a method for a modular design of a PCS in not fully object-oriented standard PCS software using design patterns. The proposed method was tested in a case company that suffered from a poorly structured product model in a not fully object-oriented PCS. The results show that its maintainability can be improved by using design patterns in combination with an agile documentation approach.

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DOI: 10.3217/jucs-025-10-1221

Authors: K. Shankar, Mohamed Elhoseny

Abstract: In wireless communication, Mobile Ad Hoc Network (MANET) consists of a number of mobile nodes which are communicated with each other without any base station. One of the security attacks in MANETs is Packet forwarding misbehaviour attack; this makes MANETs weak by showing message loss behavior. For securing message transmission in MANET, the work proposes Energy Efficient Clustering Protocol (EECP) with Radial Basis Function (RBF) based CH is elected for formed Clusters. Moreover, here some Network measures are considered to detect the malicious nodes and CH model that is speed, mobility, trust and so on. The trust value of the node is computed from the neighbor node which helps in further location to find a malicious node in the network to avail message drop and energy consumption (EC). After detecting malicious nodes, Multi secure Protocols that is Secure Efficient Distance Vector Routing (SEDV) and Secure Link State Routing Protocol (SLSP) with encryption technique used for message security. If the" HELLO" message sending by the sender, its encrypted and decrypted triples in receiver end to get the plain message, this technique is Triple Data Encryption Standard (TDES). Finally, the implementation results are evaluated to analyze the message security level of the proposed system in MANET in terms, of Packet to Delivery Ratio (PDR, Network Life Time (NLT) and some other important Measures.

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DOI: 10.3217/jucs-025-04-0390

Authors: Matias Micheletto, Rodrigo Santos, Javier Orozco

Abstract: In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.

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DOI: 10.3217/jucs-024-12-1776

Authors: J. Kokila, N. Ramasubramanian, Ravindra Thamma

Abstract: Recent processors are shrinking in size due to the advancement of technology. Reliability is an important design parameter along with power, cost, and performance. The processors need to be fault tolerant to counter reliability challenges. This work proposes a dynamic thermal and voltage management (DTVM) system which ensures a reasonable level of fault tolerance. The fault tolerance system (FTS) identifies and subsequently can forecast temporary failures at run-time. The temporary failures are dynamically estimated on SoC FPGAs for a class of heterogeneous applications. The dynamic priority scheduling based on absolute deadline is adopted to improve the nature of FTS. Experimental results indicate that the failure rate reduces by 7.2% with the variation of 2% and 12% in temperature and voltage respectively.

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DOI: 10.3217/jucs-023-05-0505

Authors: Carlos Mello, Marília Saraiva, Diego Menor, Ricardo Nishihara

Abstract: This paper presents a comparison of several video quality metrics, analysing their performance against different types of distortions. Usually, comparisons are made considering a full dataset with few different degradations. We are presenting here a comparison using three very different datasets (VQEG Phase I, LIVE VQA and ReTRIeVED) and a fourth dataset which was generated in a mobile phone network simulator. This was done to check if the video quality metrics can correctly measure the degradations created by variations in the network, very close to real scenarios. The analysis was done with 13 full reference metrics (including Opticom's PEVQ commercial tool) and two no-reference metrics. We have concluded that NTIA's VQM achieved the best results, in most of the cases. It is an open source algorithm that outperformed most of the other techniques, including the licensed PEVQ.

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DOI: 10.3217/jucs-021-12-1654

Authors: Pengfei Sun, Philippe Bon, Simon Collart-Dutilleul

Abstract: Model transformation is an interesting task, which could take advantage of several modelling languages, and meanwhile should respect all the safety requirements. The presented work studies the translation from a valid design solution to a valid implementation, which is a mapping method from coloured Petri nets to abstract B machines. Both modelling languages are well known formal methods in the context of safety requirement engineering. The Petri nets are widely accepted by French railway engineers because of a fine graphic representation and their dynamic analysis properties. The B machine offers verified software development based on B language, which has already been applied in some safety-critical systems. The proposed model translation technique will help to bridge the gap between these two formal methods. This paper shows the systematic process of the translation, which is also illustrated by several case studies. The limitations and future efforts are discussed at the end of the paper.

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DOI: 10.3217/jucs-020-14-1926

Authors: Xinyu Li, Xiaoyu Wen, Liang Gao

Abstract: Process planning and scheduling are two of the most important functions in modern manufacturing system. Considering their complementarity, integrating them more tightly can improve the performance and productivity of the whole manufacturing system. Meanwhile, the multi-objective optimization problem is widespread existing in manufacturing system. In this paper, an effective genetic algorithm is proposed to optimize the multi-objective integrated process planning and scheduling (IPPS) problem with various flexibilities in process planning. Three types of flexibilities related to process, sequence and machine are considered. And three objectives including makespan, total machine workload and maximal machine workload are taken into account simultaneously. According to the model and characteristics of multi-objective IPPS, the framework of the proposed algorithm is designed to optimize three objectives simultaneously. Effective genetic operations are employed in the proposed algorithm. Pareto set is set to store and maintain the solutions obtained during the searching procedure, the proposed algorithm could get several Pareto optimal solutions during one searching process. Two experiments are employed to test the performance of the proposed algorithm. The experiment results show that the proposed algorithm can solve multi-objective IPPS problem with various flexibilities in process planning effectively and obtain good solutions.

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DOI: 10.3217/jucs-020-10-1418

Authors: Eugenijus Kurilovas, Anita Juskeviciene, Svetlana Kubilinskiene, Silvija Serikoviene

Abstract: The aim of the paper is to investigate and propose Semantic Web approaches to improving the adaptation quality of Virtual Learning Environments (VLEs). These approaches are the method for semantic search for Web 2.0 tools in VLEs, and the method for curriculum mapping and semantic search for Learning Objects (LOs) in VLEs. In the paper, a special attention is paid to improving the adaptation capabilities of VLE, e.g. its suitability for different learning styles such as VARK. Web 2.0 tools ontology based on VARK model learning activities gives the possibility to develop adaptive learning environment with better access to specific learning content managing tools (i.e. Web 2.0 tools). The learner will only need to enter the name of learning activity into the search system field and the machine offers the appropriate tools to perform this activity. The engine facilitates the search process by optimizing workloads, thereby improving learner's satisfaction and improving the efficiency and effectiveness of the learning process. Presented curriculum mapping approach makes interoperability and LOs semantic search possible by making use of two smaller controlled vocabularies instead of a very large one on competencies which would be more volatile. One could exchange information on competencies in a multi-lingual and multi-cultural environment by: (1) breaking down competencies, and (2) relating these competency components to multilingual controlled vocabularies. The research results have shown that, in order to improve the adaptation quality of VLEs, it is very important to improve semantic search for both LOs and Web 2.0 tools in VLEs.

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DOI: 10.3217/jucs-020-06-0924

Authors: Rinaldo Michelini, Roberto Razzoli

Abstract: The goal of the study is investigating the odd claim of the human civilisation, which modifies the wild natural surrounds by synthetic alterations, defined improvements, bestowing «value added». Indeed, the history seems sanctioning that the «life-quality» on the earth has been expanding, with enhanced chances and increased resources, compared to the native prospects of the wilderness. Only at the millennium turnover, the ecology globalisation shows the impeding threats of over-depletion/pollution, exceeding the extant recovery and reclamation capabilities of the environment. The new imperative turns to be the «sustainable growth», with caginess in defining if the trends can be positive, being conditioned by the empowered recycling, retrieval and renovation measures. In fact, sustainability requires lifecycle supply-chain visibility, resource bookkeeping and revamp planning. The lifecycle starts when the idea of a product is born and lasts until complete disposal after realisation and operation. In the musts' specification/analysis, the crucial policy (global plans, detailed design, assembly plots, etc.) are followed by manufacturing, testing, delivery, diagnostics and operation, advertising, service, maintenance, etc.; then, disassembly and firing are scheduled, requiring reclamation and recovery, via re-cycling (material reprocessing) or re-using (part refurbishing). The present study summarises pilot cues for understanding the product-process agendas, using the TYPUS metrics and the KILT model, prospected by the authors, in previous works.

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DOI: 10.3217/jucs-019-14-2112

Authors: Rafael Pastor, Agustín Caminero, Daniel Sánchez, Roberto Hernández, Salvador Ros, Antonio Robles-Gómez, Llanos Tobarra

Abstract: Society has evolved in such a way that individuals are required to embrace constant improvements in order to be able to perform their jobs properly. Distance education is a solution to this problem, as it allows students to obtain practical knowledge without the space and time constraints of classical face-to-face education, thus allowing them to fit their studies into possibly tight schedules. In order to obtain practical distance education on technical topics, the use of remote laboratories becomes more of a necessity rather than just being an option. To this end, the RELATED framework has been developed in order to permit the structural development of remote laboratories. It presents a structured methodology of remote/virtual lab development and also provides common facilities, such as user management, booking, or basic visualization. In the case that a high number of laboratories and students use RELATED, handling such information becomes a major issue for the proper functionality of RELATED. These issues can be efficiently tackled using cloud technologies. This paper proposes the use of cloud technologies to enhance RELATED and describes the cloud-based architecture that is under development at UNED, including details on its software components and the algorithms needed for resource provisioning.

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DOI: 10.3217/jucs-019-09-1199

Authors: Jaime Guixeres, Javier Saiz, Mariano Alcañiz, Ausias Cebolla, Patricia Escobar, Rosa Baños, Cristina Botella, Juan Lison, Julio Alvarez, Laura Cantero, Empar Lurbe

Abstract: Virtual Reality (VR) could be an interesting tool to combat obesity and sedentariness in children. During the last years a multidisciplinary research team comprised of engineers, psychologists, physiotherapists and paediatricians have been testing these technologies. Throughout the tests, physiological (cardiovascular and metabolic response with biomedical sensors (smart fabrics TIAS) and psychological responses have been collected. The results presented in this paper reflect two main aspects: 1) the feasibility of the monitoring techniques employed and 2) the validity of virtual reality and exergaming technologies as promoters of physical activity and their potential as tools in clinical intervention programs. In the first study (n=90) children, a commercial platform was tested as support tool to aerobic exercise in a treadmill. Results showed a more physiological effort by obese group and limitations to measure effort perception with Borg scale especially in obese group. In second study (n=126) a new VR platform was developed (VREP) and tested as support of aerobic activities, a difference of first study, all the boys completed both conditions (same Aerobic exercise with/without support VR). 59.5% felt that they had to exert more effort in the traditional condition. Regarding to acceptability in both studies the vast majority of the participants liked the idea of combining physical activity with the VR platform as a form of treatment to increase physical activity. The capacity of VR technology to create controllable, multisensory, interactive 3D stimulus environments within which children's performance can be motivated, recorded, and measured, has been tested in these studies, offering clinical assessment and intervention options which are not possible using traditional methods.

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DOI: 10.3217/jucs-019-01-0002

Authors: Philippe Bon, Simon Collart-Dutilleul

Abstract: In the context of safety requirement engineering, model transformation is a task of interest. Indeed, it allows us to keep all the requirements while switching from one point of view to another. The presented work assumes that a valid solution has been found and proposes an approach in order to build a valid implementation. As some fine dynamic properties are integrated into the specification, high-level Petri nets are used to specify and verify the solution. Then, considering an industrial railway context, the transformation of the Petri net model in order to provide an input to a B process is considered. This last consideration leads to a proposition of a systematic direct transformation of the Petri net model into abstract B machines. The approach is illustrated by a theoretical railway example. The limitations of this approach are discussed at the end of the paper and some prospects are detailed.

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DOI: 10.3217/jucs-018-18-2516

Authors: David Griol, José Molina, Araceli De Miguel, Zoraida Callejas

Abstract: Social Networking has been a global consumer phenomenon during the last few years. Online communities are changing the way people behave, share and interact within their daily lives. Most of such communities are mainly focused on sharing contents and communicating using a traditional web interface. However, social virtual worlds are computer-simulated environments that the users can "inhabit" and in which they can interact and create objects. Education is one of the most interesting applications of virtual worlds, as their flexibility can be exploited in order to create heterogeneous groups from all over the world who can collaborate synchronously in different virtual spaces. In this paper, we highlight the potential of virtual worlds as an educative tool and propose a model to create learning environments within Second Life or OpenSimulator combining the Moodle learning management system, embodied conversational metabots, and programmable 3D objects. We have implemented the proposal in a learning system for several subjects of the Computer Science degree in our university and show that it fostered engagement and collaboration and helped the students to better understand complex concepts.

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DOI: 10.3217/jucs-018-09-1218

Authors: Xiaojun Qian, Xiaoping Che

Abstract: This paper elaborates the challenges in searching imposed by the burgeoning fieldof Internet of Things (IoT). Firstly it overviews the evolution of the new field to its predecessors: searching in the mobile computing, ubiquitous computing and information retrieve. Then,it identifies five research thrusts: architecture design, search locality, real-time, scalability and divulging information. It also sketches several presumptive IoT scenarios, and uses them to iden-tify key capabilities missing in today's systems. On top of these challenging issues, we report our undertaking work – a security-enhanced search engine for Internet of Things based on El-liptic Curve Cryptography (ECC) security protocol. We also report our preliminary experimental results.

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DOI: 10.3217/jucs-018-07-0937

Authors: Maurizio Palesi, Rafael Tornero, Juan Orduñna, Vincenzo Catania, Daniela Panno

Abstract: Mainstream electronic designs are realized by Systems-on-Chips (SoCs) that push the limits of integration. The advancement of manufacturing technologies in terms of integration leads us to SoCs with many (e.g., 10-1000) digital units (e.g., processor cores, controllers, storage, application-specific units) that need to be interconnected in an efficient and reliable way. The Network-on-Chip (NoC) design paradigm emerged recently as a promising alternative to classical bus-based communication architectures. Aside from better predictability and lower power consumption, the NoC approach offers greater scalability compared to previous solutions for on-chip communication. The design flow of NoCs include several key issues. Among other parameters, the decision of where cores have to be topologically mapped and also the routing algorithm represent two highly correlated design problems that must be carefully solved for any given application in order to optimize different performance metrics. The strong correlation between the different parameters often makes that the optimization of a given performance metric has a negative effect on a different performance metric. In this paper we propose a new strategy that simultaneously refines the mapping and the routing function to determine the Pareto optimal configurations which optimize average communication delay and routing robustness. The proposed strategy has been applied on both synthetic and real traffic scenarios. The obtained results show how the solutions found by the proposed approach outperforms those provided by other approaches proposed in literature, in terms of both performance and fault tolerance.

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DOI: 10.3217/jucs-018-07-0901

Authors: Freddy Bolanos, Fredy Rivera, Nader Bagherzadeh

Abstract: Network-on-Chip (NoC) is a growing and promising communication paradigm for Multiprocessor-System-On-Chip (MPSoC) design, because of its scalability and performance features. In designing such systems, mapping and scheduling are becoming critical stages, because of the increase of both size of the network and application's complexity. Some reported solutions solve each issue independently. However, a conjoint approach for solving mapping and scheduling allows to take into account both computation and communication objectives simultaneously. This paper shows a mapping and scheduling solution, which is based on a Population-Based Incremental Learning (PBIL) algorithm. The simulation results suggest that our PBIL approach is able to find optimal mapping and scheduling, in a multi-objective fashion. A 2-D heterogeneous mesh was used as target architecture for implementation, although the PBIL representation is suited to deal with more complex architectures, such as 3-D meshes.

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DOI: 10.3217/jucs-018-06-0816

Authors: Raimundas Matulevičius, Haralambos Mouratidis, Nicolas Mayer, Eric Dubois, Patrick Heymans

Abstract: The need to consider security from the early stages of the development process of information systems has been argued by academics and industrialists alike, and security risk management has been recognised as one of the most prominent techniques for eliciting security requirements. However, although existing security modelling languages provide some means to model security aspects, they do not contain concrete constructs to address vulnerable system assets, their risks, and risk treatments. Furthermore, security languages do not provide a crosscutting viewpoint relating all three - assets, risks and risk treatments - together. This is problematic since, for a security analyst, it is difficult to detect what the potential security flaws could be, and how they need to be fixed. In this paper, we extend the Secure Tropos language, an agentand goal-oriented security modelling language to support modelling of security risks. Based on previous work, where we had observed some inadequacies of this language to model security risks, this paper suggests improvements of Secure Tropos semantics and syntax. On the syntax level we extend the concrete and abstract syntax of the language, so that it covers the security risk management domain. On the semantic level, we illustrate how language constructs need to be improved to address the three different levels of security risk management. The suggested improvements are illustrated with the aid of a running example, called eSAP, from the healthcare domain.

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DOI: 10.3217/jucs-017-14-1887

Authors: Liping Gao, Tun Lu

Abstract: In order to support the real-time collaboration between geographically distributed designers, the single-user application AutoCAD is required to be transformed transparently into groupware system by adopting fully replicated architecture. As the core issue to maintain the consistency of the distributed replicas, traditional consistency maintenance algorithms (such as Operation Transformation and Address Space Transformation algorithms), however, support only linear data model, and may lead to low algorithm efficiency and small operation types when adapted to the collaborative design field. In this paper, a novel layered document model is proposed to abstract the document model of AutoCAD, and the AST algorithm is adapted according to the model to achieve transparent and real-time collaboration. Moreover, the Update conflicts resolution based on child-precedence strategy, and the database listening technique used to grasp the semantics of interface operations to realize operation adaptation are also presented. Efficiency analysis of the Layer-AST algorithm is given, showing the improved performance of the algorithm. Finally, the system architecture of Co-AutoCAD using this strategy is detailed to guide the application.

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DOI: 10.3217/jucs-017-01-0081

Authors: Rakesh Rampalli, Angarai Ramakrishnan

Abstract: This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

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DOI: 10.3217/jucs-016-06-0891

Authors: Wensheng Shen, Changjiang Zhang, Jun Zhang, Xiaoqian Ma

Abstract: This paper presents a nonlinear solver based on the Newton-Krylov methods, where the Newton equations are solved by Krylov-subspace type approaches. We focus on the solution of unsteady systems, in which the temporal terms are discretized by the backward Euler method using finite difference. To save computational cost, an adaptive time stepping is used to minimize the number of time steps. The developed program can be applied to solve any nonlinear equations, provided the users could supply the discrete form of the equations. In particular, the nonlinear solver is implemented to solve unsteady reacting flows.

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DOI: 10.3217/jucs-016-01-0082

Authors: Cengiz Kahraman, Selcuk Cebi, İhsan Kaya

Abstract: Renewable energy is a source of energy derived from natural resources such as sunlight, wind, water, tides, hot dry rocks, magma, hot water springs, fire wood, animal manure, and crop residues and waste. These renewable energy technologies are called with its source such as solar power, wind power, hydropower, geothermal and biomass. Energy resources are so important in perspective of economics and politics for all countries. Hence, the selection of the best alternative for any country takes an important role for energy investments. In this paper, axiomatic design (AD) methodology is suggested for the selection among renewable energy alternatives under fuzzy environment. AD methodology works under fuzziness which evaluates the alternatives under objective or subjective criteria with respect to the functional requirements obtained from experts. In the application of the proposed methodologies the most appropriate renewable energy alternative is determined for Turkey under fuzziness.

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DOI: 10.3217/jucs-016-01-0062

Authors: Francisco Cabrerizo, Rubén Heradio, Ignacio Pérez, Enrique Herrera-Viedma

Abstract: In group decision making situations, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved and, as a result, they may present incomplete information. In this paper, we present a new selection process to deal with incomplete fuzzy linguistic information. As part of it, we use an iterative procedure to estimate the missing information. This procedure is guided by the additive consistency property and only uses the preference values provided by the experts. In addition, the additive consistency property is also used to measure the level of consistency of the information provided by the experts. The main novelties of this selection process are both the possibility to manage decision situations under incomplete fuzzy linguistic information and the importance of the experts' preferences in the aggregation processes is modeled by means of the experts' consistency.

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DOI: 10.3217/jucs-015-17-3231

Authors: Achraf Telmoudi, Lotfi Nabli, Radhi Hiri

Abstract: In this article a new Control-Supervisory architecture of Flexible Manufacturing Systems (FMS) is presented. We are interested particularly in construction and modelling of FMS robust control of flow-shop type to time constraints. Other than the control of production system, the goal is to observe and interpreted the robustness of resources and of manufacturing system. The P-time Petri Nets which is used for modeling of the time constraints. A methodology of construction of a robust control system generating the margins of passive and active robustness is elaborated. The redundancy of the robustness of the elementary parameters between passive and active leads us to define the ways ensuring the total robus tness of the system. To do so, a set of definitions lemmas and theorems are developed and affirmed by examples of applications.

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DOI: 10.3217/jucs-015-09-1907

Authors: Weidong Li, Liang Gao, Xinyu Li

Abstract: Manufacturing planning is crucial for the quality and efficiency of product development. Process planning and scheduling are the most important and challenging tasks in manufacturing planning. These two processes are usually arranged in a sequential way. Recently, a significant trend is to make the processes to work more concurrently and cooperatively to achieve a globally optimal result. In this paper, several intelligent strategies have been developed to build up Cooperative Process Planning and Scheduling (CPPS). Three Game Theory-based strategies, i.e., Pareto strategy, Nash strategy and Stackelberg strategy, have been introduced to analyze the cooperative integration of the two processes in a systematic way. To address the multiple constraints in CPPS, a fuzzy logic-based Analytical Hierarchical Process (AHP) technique has been applied. Modern heuristic algorithms, including Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GAs), have been developed and applied to CPPS to identify optimal or near-optimal solutions from the vast search space efficiently. Experiments have been conducted and results show the objectives of the research have been achieved.

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DOI: 10.3217/jucs-015-09-1746

Authors: Sven Tackenberg, Bernhard Kausch, Sönke Duckwitz, Christopher Schlick, Selvinaz Karahancer

Abstract: The complexity of process engineering projects in the chemical industry — resulting from the large number of activities to be accounted for as well as the required actors and resources — and the number of projects running simultaneously within an enterprise are rapidly increasing. In order to stay competitive, the factors relevant to the success of project planning and execution, e.g. the project budget or duration, must be accurately predicted and controlled. For this reason, a novel simulation approach for development projects is introduced and validated. A formal description of a development project and of an activity-oriented simulation model is given. This "meta model" is able to describe the influencing factors of a development project as well as their interrelations during the course of a project. On the basis of the meta model, an activity-oriented simulation model is developed in cooperation with enterprises from the chemical industry. The simulation model enables the automatic creation and prospective benchmarking of complex, detailed project plans. The dynamics of such a development project are represented as a stochastic Petri net, including Java functions. Organizational factors of a development project such as task scheduling, the limited availability of actors and tools or uncertainty regarding the effort required to solve a task can be systematically studied through simulation experiments. The results of these experiments assist project managers in understanding the influence of the quantity and characteristics of actors and resources on project performance. In the validation study, a chemical process design project in a large enterprise is considered and the external validity of the stochastic project model is analyzed.

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DOI: 10.3217/jucs-013-02-0224

Authors: Iwan Tabakow

Abstract: This paper describes a method of using Petri net P-invariants in system diagnosis. To model this process a net oriented fault classification is presented. Hence, the considered discrete event system is modelled by a live, bounded, and reversible place-transition Petri net. The notions of D-partition of the set of places P of a given place-transition net N and net k-distinguishability are first introduced. Next these two notions are extended to the set of all vertices, i.e. places and transitions of N. So the problem of fault identification of the vertices of N is transformed as a problem of fault identification of the places of a new net N´ called a net simulator of N. Any transition in N´ is assumed to be fault-free. Then the corresponding net place invariants are computed. The system k-distinguishability measure is obtained in a unique way from the place-invariant matrix. For a large value of k, the system model is extended by using some set of additional places called test points and at the same time preserving the original net properties. To obtain a 1-distinguishable net the notion of a marked graph component is used. It is shown a sufficient condition for 1-distinguishability of an arbitrary place-transition net and a corresponding algorithm is presented. Next two different diagnosis test strategies are discussed, i.e. combinational and sequential fault diagnosis. Corresponding (single) place and transition fault models are introduced. The complexity of the proposed method depends on the effectivity of the existing algorithms for computation of the P-cover, i.e. the set of P-invariants covering N. The proposed approach can be extended for higher level Petri nets, e.g such as coloured nets or also to design self-diagnosable circuit realisations of Boolean interpreted Petri nets. Several examples are given.

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DOI: 10.3217/jucs-012-09-1393

Authors: Tibor Tóth, Ferenc Erdélyi

Abstract: Introduction of the multi-degree linear education system consisting of BSc, MSc and PhD programs in the Hungarian higher education, according to the goals and requirements of the Bologna Process, necessitates reviewing, enhancing and changing the three-level subject-structures consisting of fundamental, foundation enlarging and special subjects. In the course of the last 50 years the quantity of the number of subject areas and the number of engineering branches have been increasing in technological faculties around the world and they have already reached the limits of rationality. Recently there is an increasing demand for integrated and interdisciplinary special branches such as Systems Engineering. After having surveyed the paradigm changes in the progress of engineering sciences, the paper gives a brief summary on the concept, formation and significance of Systems Engineering. The paper also deals with Production Information Engineering as a characteristic field of Systems Engineering, which offers important application possibilities for IT-based system integration.

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DOI: 10.3217/jucs-012-09-1254

Authors: László Horváth, Imre Rudas

Abstract: Higher education courses are increasingly created as student organized collections of interrelated modules. At the same time, frequent change of subject matter and knowledge in its background must be handled. Above and other factors created and recognized a need for efficient computer based course management. Conventional computer aided teaching methods are not suitable to organize, manage, and communicate the comprehensive course information any more. The authors considered an analogy with well- organized computer descriptions of interrelated objects in the form of comprehensive integrated models in product engineering. Modeling and management of information serve engineering activities during lifecycle of product. Relevant advanced characteristics of integrated product descriptions are process orientation, definition of application oriented building elements called as features, and assistance of decisions by knowledge representations. The authors considered higher education course as one kind of product and proposed a course model. They focused to integrating student, teacher, and institute demand driven characteristics of modeling. Model is developed for application by course procedures. While conventional virtual education systems concentrate to computer mediated distance education, the authors considered arbitrary mix of campus and distance styles of education. In this paper, the authors first give an introduction in their approach to classroom modeling by a comparison of conventional distance education, conventional virtual classroom, and the proposed model based virtual classroom. Next, functional elements of the proposed course modeling and components of virtual classroom are explained. Conflicts as consequences of inappropriate capability or breaking of human intent are analyzed. Following this, initial conditions for definition of course module and construction of course module using modification by features are detailed. Finally, future work for implementation of the modeling in an experimental system composed by professional product lifecycle management (PLM) system, configurable virtual teaching environment, its virtual classroom extension and virtual classroom extension to the engineering modeling system is concluded.

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DOI: 10.3217/jucs-010-09-1123

Authors: Maziar Goudarzi, Shaahin Hessabi, Alan Mycroft

Abstract: We present an embedded_system design flow, discuss its details, and demonstrate its advantages. We adopt the object_oriented methodology for the system_level model because software dominates hardware in embedded systems and the object_oriented methodology is already established for software design and reuse. As the building_block of system implementation, we synthesise application_specific processors that are reusable, through programming, for several related applications. This addresses the high cost and risk of manufacturing specialised hardware tailored to only a single application. Both the processor and its software are generated from the model of the system by the synthesis and compilation procedures provided. We observe that the key point in object_oriented methodology is the class library, and hence, we implement methods of the class library as the instruction_set of the processor. This allows the processor to be synthesised just once, but, by programming, to be reused several times and specialised to new applications that use the same class library. An important point here is that the processor allows its instructions to be selectively overridden by software routines, this not only allows augmentation of processor capabilities in software, but also enables a structured approach to make software patches to faulty or outdated hardware. A case study illustrates application of the methodology to various applications modelled on top of a common basis class library, and moreover, demonstrates new application_specific opportunities in power management (and area_management for FPGA implementations) resulting from the structure of the processor based on de_activation of unused features.

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DOI: 10.3217/jucs-005-12-0833

Authors: Wonsik Lee, Sunghan Lee, Beom Lee, Young Lee

Abstract: In this paper, a genetic algorithm based method is proposed to solve the problem of minimizing the PCB assembly time for multi-head surface mounting machines. By grouping the reels and by clustering the components the multi-head problem is transformed into a single_head one and then the single-head method is simply applied to the multi-head case. To implement the genetic algorithm, a partial link concept is proposed for genetic operations. Computer simulation results show that the proposed algorithm is superior to the heuristic algorithm that is currently used in industry.

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