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IEEE Education Engineering (EDUCON)

作者： André Schäfer Rainer Brück Bruno Kleinert Harald Schmidt Dietmar Fey Steffen Büchner Steffen Jaschke Sigrid Schubert Institute of Microsystems Technology University of Siegen Siegen Germany Chair of Computer Science 3 University of Erlangen-Nürnberg Erlangen Germany Didactics of Informatics and E-Learning University of Siegen Siegen Germany

In this paper, we describe the results of an expert survey conducted within the project “Competence development with embedded micro- and nanosystems”. This survey was designed in order to refine our previously derived normative competence structure model for developers of embedded micro-and nanosystems towards an empirically refined competence structure model. Today the size of structural parts of nanosystems has scaled down to as few as a couple of molecules. This results in a lot of challenges regarding permanent and transient faults. Therefore bottom-up development techniques for nanostructured systems are now included into our competence model to prepare future developers to the specific challenges of designing at the nano scale. The evaluation of the content validity of the empirically refined competence structure model was accomplished by the presented expert rating.

关键词： Embedded systems Hardware computer science Educational institutions Mathematics

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Waking OA's "slumbering giant": The university's mandate to mandate open access

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New Review of Information Networking 2008年第1期14卷 51-68页

作者： Harnad, Stevan Canada Research Chair in Cognitive Sciences School of Electronics and Computer Science Universite du Québec À Montréal Canada Canada Research Chair Institute of Cognitive Sciences Universite du Québec À Montréal CP 8888 Succursale Centre-Ville Montreal QC H3C 3P8 Canada

Universities (the universal research-providers) as well as research funders (public and private) are beginning to make it part of their mandates to ensure not only that researchers conduct and publish peer-reviewed research (publish or perish), but that they also make it available online, free for all. This is called Open Access (OA), and it maximizes the uptake, impact, and progress of research by making it accessible to all potential users worldwide, not just those users whose universities can afford to subscribe to the journal in which it is published. Researchers can provide OA to their published journal articles by self-archiving them in their own university's online repository. Students and junior faculty-the next generation of research providers and consumers-are in a position to help accelerate the adoption of OA self-archiving mandates by their universities, ushering in the era of universal OA. © 2008 Taylor & Francis.

关键词： Publishing

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Application of improved model free control method for nonlinear system

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ICIC Express Letters, Part B: Applications 2013年第6期4卷 1691-1696页

作者： Lin, Yan Song, Yang Zhang, Mingxia Qin, Pinle Department of Naval Architecture and Ocean Engineering Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian 116024 China Department of Computer Science North University of China No. 3 Xueyuan Road Taiyuan 030051 China

The improvement designs for the model-free learning adaptive control (MFLAC) based on multi-innovation (MI) and chaotic genetic algorithms (CGA) are presented in this paper. The application of MI and CGA are to overcome the limitation of the conventional MFLAC design, which cannot guarantee the satisfactory control performance when the controlled system has the characteristic of strong time-varied nonlinearity. Numerical results for the different nonlinear systems are shown to illustrate the effectiveness of the improved designs. © 2013 ICIC International.

关键词： Genetic algorithms

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TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET

arXiv

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arXiv 2020年

作者： Ye, Mao Guan, Lin Quddus, Mohammed Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Architecture Building and Civil Engineering Loughborough University LoughboroughLE11 3TU United Kingdom

Vehicle-to-everything (V2X) communication in vehicular ad hoc network (VANET) has emerged as a crucial component in advanced Intelligent Transport System (ITS) for information transmission and vehicular communication. One of the vital research challenges in VANET is the design and implementation of novel network routing protocols which bring reliable end-to-end connectivity and efficient packet transmission to V2X communication. The organically changing nature of road traffic vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packets delivery. Therefore, position-based routing protocols tend to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. Therefore, a novel target-driven and mobility prediction (TDMP) based routing protocol is developed in this paper for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. To implement an effective routing protocol, TDMP primarily involves the destination target of a driver for the mobility prediction and Received Signal Strength Indicator (RSSI) for the inter-vehicular link-status estimation. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, the proposed TDMP is able to enhance the packet transmission with the consideration of the estimation of inter-vehicular link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout. Based on the extensive simulations carried out on operational road environments with varying configurations and complexity, the experimental results show better performance in terms of improvin

关键词： Vehicular ad hoc networks

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Non-conventional processing of noisy signal in the adaptive control of hydraulic differential servo cylinders 3

Non-conventional processing of noisy signal in the adaptive ...

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3rd IEEE International Symposium on Intelligent Signal Processing, WISP 2003

作者： Tar, József K. Rudas, Imre J. Szeghegyi, Ágnes Kozlowski, Krzysztof Budapest Polytechnic John von Neumann Faculty of Informatics Népszínház utca 8 BudapestH-1081 Hungary Budapest Polytechnic Károly Keleti Faculty of Economics Népszínház utca 8 BudapestH-1081 Hungary Poznań University of Technology Chair of Control Robotics and Computer Science Ul. Piotrowo 3a Poznań60-965 Poland

ISBN: (纸本)0780378644

Hydraulic differential electric servo cylinders are strongly non-linear, coupled multivariable electromechanical tools applicable for driving e.g. manipulators. The primary controllable physical agent in such systems is the time-derivative of the pressure of the working fluid in the appropriate chambers of the cylinder. Besides the nonlinearities of hydrodynamical origin discontinous ones described by the Stribeck model originate from the friction between the cylinder and the piston. This model contains the terms of the viscous, the static, and the adhesive contributions of friction. Whenever the velocity of the piston changes its sign alternating friction force of considerable amplitude appears. Such behavior means serious difficulty in feedback-based continuous path (CP) dynamic control whenever a nominal trajectory asymptotically approaching a zero velocity segment is needed: the system itself generates a noise-like acceleration signal to be used in the control. Since hydraulic drives have considerable advantages in comparison with electric ones it would be desirable to extend their application to dynamic CP control, too. For this purpose a special adaptive non-linear control dealing with the system-generated noisy signals was elaborated. The method is rather based on a novel branch of soft computing slightly supported by a uonconventional noise filtering. In the present paper the capabilities of the improved controller are illustrated via simulation. ©2003 IEEE.

关键词： Cylinders (shapes)

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Formally specifying and verifying mobile agents - Model checking mobility: The MobiOZ approach

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International Journal of Agent-Oriented Software Engineering 2008年第4期2卷 449-474页

作者： Taguchi, Kenji Dong, Jin Song Information Systems Architecture Research Division Grace Center National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo 101 8430 Japan Computer Science Department School of Computing National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore

The notion of the mobile agent has been around for over a decade in order to capture the new form of computation in communication networks. A mobile agent is a computing entity which can move around different hosts on the network, carrying its state and procedures. Mobile Object-Z (MobiOZ), which is designed to provide a practical means to the specifiers who are working on mobile agent applications, is an extended notation of Object-Z, with mobile and communication primitives for specifying and verifying mobile agent applications. In this paper, we will give an overview of the MobiOZ notation and present its semantic foundation, then demonstrate how the specifications in MobiOZ can be simulated and verified by SPIN, a model checker, by translating MobiOZ specifications into PROcess MEta LAnguage (PROMELA), a process-based formal specification language of SPIN. © 2008, Inderscience Publishers.

关键词： Formal specification

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Edge-based Mining of Frequent Subgraphs from Graph Streams

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Procedia computer science 2015年 60卷 573-582页

作者： Alfredo Cuzzocrea Zhao Han Fan Jiang Carson K. Leung Hao Zhang Department of Engineering and Architecture (DIA) University of Trieste & ICAR-CNR Via A. Valerio 6/1 34127 Trieste (TS) Italy Department of Computer Science University of Manitoba Winnipeg MB R3T 2N2 Canada

In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams.

关键词： Knowledge discovery and data mining frequent patterns frequent subgraphs graph structured data data streams

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Intelligent knowledge-based system to improve injection molding process

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Journal of Industrial Information Integration 2022年 25卷

作者： Khosravani, Mohammad Reza Nasiri, Sara Reinicke, Tamara Chair of Product Development University of Siegen Paul-Bonatz-Str. 9-11 Siegen57068 Germany Martinshardt 19 Siegen57074 Germany Department of Electrical Engineering and Computer Science University of Siegen Hoelderlinstr. 3 Siegen57076 Germany

The successful implementation of Industry 4.0 requires a comprehensive research, industrial innovation, strategic plans, and integration of engineering concepts. In this study, we described the development of a knowledge-based system to improve the traditional injection molding process using information integration. This system aims to support injection molding industries by applying advanced solutions to optimize production and increase productivity. In this context, based on the current data-driven process, new systems, technologies, and best practices are recommended to users. This work focuses on (a) simulation and generative design for optimization of manufacturing, (b) additive manufacturing, and (c) virtual reality. In this research, after a short overview of the knowledge-driven process of injection molding, the proposed knowledge-based system is presented. Then, we explained the use of the mentioned technologies in the injection molding production line. The current work, indicates that a production process can be significantly changed and revolutionized by intelligent solutions and smart devices. For instance, utilizing 3D printing in fabrication of injection molding clamps leads to 95% and 85% reductions in cost and time, respectively. Further research and experiments can improve and enrich the existing affordable and proper solutions of resulting from the proposed knowledge-based system. © 2021 Elsevier Inc.

关键词： Knowledge based systems

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Approximation to Expected Support of Frequent Itemsets in Mining Probabilistic Sets of Uncertain Data

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Procedia computer science 2015年 60卷 613-622页

作者： Alfredo Cuzzocrea Carson K. Leung Richard Kyle MacKinnon Department of Engineering and Architecture (DIA) University of Trieste & ICAR-CNR Via A. Valerio 6/1 34127 Trieste (TS) Italy Department of Computer Science University of Manitoba Winnipeg MB R3T 2N2 Canada

Knowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data.

关键词： Knowledge discovery and data mining expected support frequent patterns uncertain data upper bounds

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Predicting Short-Term Bike-Sharing Demand at Station Level: A Deep Graph-Based Multi-Task Spatiotemporal Approach

SSRN

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SSRN 2024年

作者： Bentahar, Jamal Nejadshamsi, Shayan Wang, Chun Eicker, Ursula Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Canada Excellence Research Chair Next Generation Cities Gina Cody School of Engineering and Computer Science Concordia University MontrealQCH3G 1M8 Canada

Bike-sharing systems, popular for eco-friendly travel, face challenges with supply-demand imbalances, resulting in user dissatisfaction and resource inefficiency. Accurately predicting passenger demand is important for optimizing the efficiency of bike-sharing services. Deep learning methods have extensively explored the intricate spatiotemporal dynamics of bike-sharing demand. However, current approaches often overlook analyzing demand at the station level, instead focusing on broader geographical areas and grid-based assessments. They primarily rely on geographical proximity for spatial correlation and tend to predict only bike check-in demand, neglecting the correlation of check-in and check-out demand. To this end, this paper introduces a deep graph-based learning architecture, namely the Multi-Task Spatial-Temporal Graph-based Neural Network (MSTG-Net), for accurate prediction of hourly bike-sharing demand across the city's bike stations, aiming to address the challenges above. Initially, we analyze historical data and identify three key historical features for each time interval: closeness, period, and trend. Subsequently, we create three separate streams, each dedicated to one of these features, and design components to capture both spatial and temporal dependencies within each stream. For capturing spatial information, a graph convolution operator is used along with an adjacency graph generated from the pairwise similarity of stations' historical demand. We employ a modified Long Short-Term Memory (m-LSTM) recurrent block to learn temporal dependencies and facilitate multi-task learning. This component enables the extraction of hidden pairwise demand correlations by treating the prediction tasks of bike check-in and check-out demands as interrelated. Moreover, we incorporate the effects of global features, such as meteorological information, to account for broader-scale changes in our analysis. Finally, the short-term bike-sharing check-in and check-out dema

关键词： Long short-term memory

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