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The Chinese ‘‘HumanFlesh’’ Web:the first decade and beyond

Chinese Science Bulletin 2014年第26期59卷 3352-3361页

作者： Fei-Yue Wang Daniel Zeng Qingpeng Zhang James A. Hendler Jianping Cao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Management Information Systems The University of Arizona Tucson Department of Systems Engineering and Engineering Management City University of Hong Kong Tetherless World Constellation Department of Computer Science Rensselaer Polytechnic Institute Troy The Research Center for Computational Experiments and Parallel Systems National University of Defense Technology

Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.

关键词：中国人网址肉 Web功能实证分析参与者共同体万维网

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Automating CPPSs Analyses with SysML v2 and Lingua Franca in the Context of Industry 4.0 50

Automating CPPSs Analyses with SysML v2 and Lingua Franca in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Gaiardelli, Sebastiano Wilch, Jan Fummi, Franco Vogel-Heuser, Birgit Industrial Computer Engineering Laboratory Department of Engineering for Innovation Medicine University of Verona Italy Institute of Automation and Information Systems Technical University of Munich Germany

ISBN: (纸本)9781665464543

The design of manufacturing systems requires exploring diverse component configurations for one that best satisfies the requirements of the target system. At the same time, manufacturing systems evolve continuously, increasing their complexity and that of the production line in which they are installed. Model-based System Engineering (MBSE) methodologies and modeling languages like System Modeling Language (SysML) are used to assist system engineers during the entire design process. Yet, MBSE methodologies mainly rely on manually composed models for requirements verification, incurring enormous cost and effort especially in larger, more complex systems. This paper proposes a methodology exploiting the knowledge enclosed in a SysML model to automate the execution of a set of analyses defined over the represented manufacturing system. The proposed approach relies on Lingua Franca (LF) to simulate the specified analysis and analyze the results. Moreover, the methodology supports chains of analyses to represent dependent requirements and tradeoffs imposed upon a manufacturing system. The proposed methodology has been evaluated on a manufacturing system demonstrator. Results show the proposed methodology’s applicability and scalability on diverse configurations of the manufacturing system. © 2024 IEEE.

关键词： Smart manufacturing

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On the reconfiguration of degradable VLSI/WSI arrays

On the reconfiguration of degradable VLSI/WSI arrays

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IEEE International Workshop on Defect and Fault Tolerance in VLSI systems, DFT 1993

作者： Low, C.P. Leong, H.W. Algorithms and Design Automation Laboratory Department of Information Systems and Computer Science National University of Singapore Lower Kent Ridge Road Singapore0511 Singapore

ISBN: (纸本)0818635029

This paper consider the problem of reconfiguring VLSI/WSI arrays via the degradation approach. In this approach, all elements are treated uniformly and no elements are dedicated as spares. The goal is to derive a fault-free subarray T from the defective host array such that the dimensions of T are larger than some specified minimum. This problem has been shown to be NP-complete under various switching and routing constraints. However, we show that a special case of the reconfiguration problem with row bypass and column rerouting capabilities, is solvable in polynomial time using network flows. Using this result, a new fast and efficient reconfiguration algorithm is proposed. Empirical study shows that the new algorithm indeed produce good results in terms of the percentages of harvest and degradation of VLSI/WSI arrays. © 1993 IEEE.

关键词： Polynomial approximation

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Mode switch - Adaptive use of delay-sensitive or energy-aware communication in IEEE 802.15.4-based networks

Mode switch - Adaptive use of delay-sensitive or energy-awar...

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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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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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Fusing R Features and Local Features with Context-Aware Kernels for Action Recognition

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International Journal of computer Vision 2016年第2期118卷 151-171页

作者： Yuan, Chunfeng Wu, Baoxin Li, Xi Hu, Weiming Maybank, Stephen Wang, Fangshi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Systems Birkbeck College London United Kingdom School of Software Engineering Beijing Jiaotong University Beijing China

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition. © 2015, Springer Science+Business Media New York.

关键词： Principal component analysis

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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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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 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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