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2010 IEEE 7th International Conference on Mobile Adhoc and Sensor systems, MASS 2010

作者： Chen, Feng Tong, Xiaoyu Ngai, Edith Dressler, Falko Computer Networks and Communication Systems University of Erlangen Germany Department of Information Technology Uppsala University Sweden Siemens AG Industry Automation Division Germany

ISBN: (纸本)9781424474882

We propose "mode switch", an adaptive load-sensitive solution that supports both an energy-efficient operation mode for transmitting normal sensor data and an QoS-aware low-latency mode for high priority emergency messages. Typically, sensor networks are considered to operate efficiently w.r.t. energy consumption. A good example is the IEEE 802.15.4 protocol, which has mainly been designed for this purpose. The standard IEEE 802.15.4 beacon-enabled cluster-tree topology supports energy-efficient operation for low-rate data transmissions. However, in the case of detected alarms, high-priority emergency messages need to be transmitted with low-latency and guaranteed delivery. In this case, the cluster-tree topology appears to be inefficient and cannot provide the needed support. In this paper, we show how energy-efficiency and delay-sensitivity can be combined by developing an adaptive solution that completely switches between the fundamentally different operation modes beacon-enabled cluster-tree mode and non-beacon-enabled mesh mode. The key challenge is to provide a fast mode switch that also keeps track of necessary topology information. We present a protocol for this mode switch and demonstrate, based on extensive simulations, that the performance demands can be fulfilled with only reasonable overhead even for mid-size networks. Our simulation results clearly indicate that the system achieves the objected QoS and energy-efficiency with "mode switch" in real-time. ©2010 IEEE.

关键词： Energy efficiency

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An Intelligent Factory automation System with Multivariate Time Series Algorithm for Chip Probing Process

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IEEE Robotics and automation Letters 2023年第9期8卷 5464-5471页

作者： Hsieh, Yu-Ming Lin, Chin-Yi Wilch, Jan Vogel-Heuser, Birgit Lin, Yu-Chen Lin, Yu-Chuan Hung, Min-Hsiung Cheng, Fan-Tien National Cheng Kung University Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan701401 Taiwan National Cheng Kung University Institute of Manufacturing Information and Systems Tainan701401 Taiwan Technical University of Munich Institute of Automation and Information Systems Garching80333 Germany Chinese Culture University Department of Computer Science and Information Engineering Taipei11114 Taiwan

Chip-probing is the key process for IC manufacturing to its ensure quality. As the number of tests increases, the test quality and the test yield will be affected because the needles on the probe card of the tester will be contaminated by external objects or worn out. Whether a needle polish of the probe card is required can be determined through real-time monitoring on various detection indicators such as resistivity and yield. However, both resistivity and yield are lagging indicators, and excessively frequent needle polishes will increase the processing time and reduce the test throughput. The so-called Intelligent Factory automation (iFA) system platform, realized by integrating several intelligent services including Intelligent Predictive Maintenance (IPM), was proposed to accomplish the goal of Zero-Defect Manufacturing. However, the current remaining useful life (RUL) prediction algorithm in IPM is a univariate time series prediction. The RUL prediction may not be accurate enough if only one variable is adopted to describe the dynamic changes of the time series. A supervisory architecture for chip probing process based on iFA is proposed in this letter. The Multivariate Version of Time Series Prediction (TSPMVA) in this architecture can use the vector autoregression model to improve the accuracy of RUL prediction. Experimental results reveal that the proposed supervisory framework with TSPMVA can not only monitor the tester's health status efficiently but also improve the accuracy of needle's RUL prediction by extracting multiple features. © 2016 IEEE.

关键词： Probes

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A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

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Journal of Artificial Intelligence and Technology 2021年第1期1卷 74-82页

作者： Daiyu Jiang Gang Hu Guanqiu Qi Neal Mazur College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222 USA

As one chemical composition,nicotine content has an important influence on the quality of tobacco *** and nondestructive quantitative analysis of nicotine is an important task in the tobacco ***-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely *** this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud *** 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy *** consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information *** computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and *** results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other *** research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.

关键词： nicotine tobacco leaves near-infrared spectroscopy fully convolutional network cloud computing

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Three-dimensional path planning for underwater vehicles based on an improved ant colony optimization algorithm

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Journal of Engineering Science and Technology Review 2015年第5期8卷 24-33页

作者： Yang, L. Li, K.S. Zhang, W.S. Wang, Y. Chen, Y. Zheng, L.F. College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Computer science and information systems Bradley University PeoriaIL61625 United States

Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony optimization (IACO) algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality, and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and feasible in underwater vehicle 3D path planning than the basic ACO model. © 2015 Kavala Institute of Technology.

关键词： Motion planning

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Coronavirus Infection Segmentation Detection Using UNET Deep Learning Architecture 18

Coronavirus Infection Segmentation Detection Using UNET Deep...

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18th IEEE India Council International Conference, INDICON 2021

作者： Saxena, Naman Chahal, Ekam Singh Sinha, Adwitiya Chand, Satish Indian Institute of Science Department of Computer Science Automation Bengaluru India Jaypee Institute of Information Technology Department of Computer Science Eng. and Information Technology UP Noida India Jawaharlal Nehru University School of Computer Systems Sciences New Delhi India

ISBN: (纸本)9781665441759

The high potency and spread of the coronavirus pandemic has rapidly swept over a global scale, causing a large number of deaths and devastation. Its mutants have exaggerated the situation further, which has become a serious concern and a challenge for scientists, especially medical practitioners, to devise some advanced remedial actions. This paper intends to address this by developing a model based on deep learning for segmenting the affected regions in the lungs using CT-scan images. We propose a novel segmentation model based on UNet using Xception-Net in the encoder stage to detect covid-19 infection in CT-scans with two main aspects. It combines the local residual connections in the decoder unit of UNet with the typical global residual connections that lead to better performance. Also, the encoder component uses a pre-trained state-of-the-art feature extraction model that helps the system converge to the optimal value precisely due to the pre-trained weights. We apply a contrast-limited version of the adaptive histogram equalization in the data preparation stage to make the frequency of image pixels uniformly distributed. This decreases the biasedness in the model towards specific sections of CT-scans images. Our proposed model outperformed some existing counterparts, including TV-UNet, Inf-Net, ED-CNN. © 2021 IEEE.

关键词： Coronavirus

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Tuning the position of a fuzzy cognitive map attractor using backpropagation through time

Tuning the position of a fuzzy cognitive map attractor using...

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7th International Conference on Integrated Modeling and Analysis in Applied Control and automation, IMAACA 2013, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2013

作者： Gregor, Michal Groumpos, Peter P. Department of Control and Information Systems Faculty of Electrical Engineering University of Žilina Slovakia Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (纸本)9781629934884

The paper proposes a new learning method for fuzzy cognitive maps, which makes it possible to encode an attractor into the map. The method is based on the principle of backpropagation through time known from the theory of artificial neural networks. Simulation results are presented to show how well the method performs. It is shown that the results are superior to those achieved using Hebbian learning approaches such as nonlinear Hebbian learning. Some lines for possible future research and development are given.

关键词： Fuzzy Cognitive Maps

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ZeitGeist: A Generic Tool Supporting the Dissemination of Time Series Data Following FAIR Principles 15

ZeitGeist: A Generic Tool Supporting the Dissemination of Ti...

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15th International Conference on Knowledge Management and information systems, KMIS 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Schmidt, Andreas Koubaa, Mohamad Anis Schweikert, Jan Stucky, Karl-Uwe Süß, Wolfgang Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science and Business Information Systems University of Applied Sciences Karlsruhe Germany

ISBN: (纸本)9789897586712

An important point for the widespread dissemination of FAIR-data is the lowest possible entry barrier for preparing and providing data to other scientists according to the FAIR criteria. If scientists have to manually extract, transform and annotate the data according to the FAIR criteria and then export it to make it available to the public, this requires a significant investment of time that does not primarily reward the scientist who prepares and provides the data. The Energy Lab at KIT is running a large cluster of an Influx database management system with energy related time series data being stored in a variety of individual databases over periods of up to 15 years. In order to increase the willingness to make data available to the scientific public, we develop a tool that greatly supports and automates the publication and annotation process of time series data stored in Influx databases. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Database systems

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The Architecture of Intelligent Agent-based Educational System for Training Students in a Technical University 5

The Architecture of Intelligent Agent-based Educational Syst...

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5th International Conference on information Technologies in Engineering Education, Inforino 2020

作者： Shvetsov, Anatoly Rzheutskaya, Svetlana Sergushicheva, Anna Sukonschikov, Alexey Andrianov, Igor Kochkin, Dmitry Vologda State University Dept. of Information Systems and Technologies Vologda Russia Vologda State University Dept. of Automation and Computer Engineering Vologda Russia

ISBN: (纸本)9781728148106

The article presents the development, implementation and approbation of a multi-level architecture of the intelligent agent-based educational system for training of technical specialists. The proposed architecture combines openness, flexibility and good scalability. This article shows how these quality indicators were achieved through a combination of multi-agent and service-oriented approaches. It describes the purpose and components of each level, presents some key aspects of the implementation and shows the results of pedagogical experiments. © 2020 IEEE.

关键词： Intelligent agents

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The model-free learning enhanced motion control of DC motor

The model-free learning enhanced motion control of DC motor

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International Conference on Electrical Machines and systems, ICEMS 2007

作者： Rongmin, Cao Zhongsheng, Hou Wei, Zhang Department of Computer and Automation Beijing Institute of Machinery Industry China Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University China

ISBN: (纸本)8986510081

This paper presents an approach towards learning enhanced motion control of DC motor, suitable for applications involving repeated iterations of motion trajectories. The overall structure of the control consists of a feedback and a feed-forward components. The model-free learning adaptive feedback control (MFLAC) provides for the main system stabilization and an iterative learning control (ILC) algorithm is proposed to serve as a feedforward compensation to nonlinear and unknown dynamics and disturbances, thereby enhancing the improvement achievable with PID or MFLAC alone. It serves as the basis for simulation study of the proposed control scheme. A comparison of the performance achieved with traditional PID and MFLAC is also provided to highlight the advantages of the additional intelligent feedforward mode.

关键词： DC motors

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Performance of feasible Markov chain-based predictors for nonlinear systems 4

Performance of feasible Markov chain-based predictors for no...

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4th European Control Conference, ECC 1997

作者： Gao, H. Kárný, M. Sláma, M. Department of Adaptive Systems Institute of Information Theory and Automation Czech Academy of Sciences Czech Republic Institute of Computer Science Czech Academy of Sciences Czech Republic

ISBN: (纸本)9783952426906

A non-traditional adaptive predictor is successfully compared with a neural network. It combines several simple Markov chain-based predictors gained from Bayesian estimation with forgetting. It can describe non-linear, stochastic digitized dynamic systems with finite memory and slowly varying parameters. Its complexity is linear in the number of used models m and the number of input levels mu, and quadratic in the number of output levels my. This is in sharp contrast with the corresponding full Markov predictor whose complexity is (my mu)m+1.

关键词： Stochastic systems

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