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IEEE International Conference on Industrial Technology (ICIT)

作者： Igor Pardal Latini Wilson Eduardo Barioni Marco Teixeira Flavio Neves-Jr Lucia Valeria Ramos de Arruda CPGEI-UTFPR Curitiba Parana Brazil Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI) Pontificia Universidade Catolica do Parana (PUCPR) Curitiba Parana Brazil PPGla-PUCPR Curitiba Parana Brazil

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The growing need for automation of factories and their processes determines a constant increase in the use of robots to carry out various tasks. Multiple robots can do different tasks simultaneously, speeding up processes. This practice raises the question of how many robots are needed to service the entire process, given all the limitations of the operation. One of these limitations is due to the navigation systems used by the several robots in the environment. Line-followers robots, for example, can only meet some tasks but are cheaper than free-movement ones. In this work, we develop an optimization model for task allocation among the available robots to minimize execution delays using mixed integer linear programming, considering their navigation systems. With the model, it is possible to obtain the best task allocation for each robot and the order to perform such tasks, thus avoiding delays as much as possible and getting zero delays for the presented case, that emulates the process of a vehicle assembler factory line.

关键词： Service robots Navigation Search problems Production facilities Delays Mixed integer linear programming Batteries

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and computer Security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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Electrochemical Study of Cu2+ Sensor Based on Zif-67/Mwcnts/Nafion

SSRN

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SSRN 2023年

作者： Ding, Lifeng Song, Yuru Li, Qiang Guo, Jinqing Zhang, Jie Wang, Qi Dong, Hongmei Song, Yongbo Song, Zhengwei Zhao, Xiaohong Niu, Yulan Department of Chemistry and Chemical Engineering Taiyuan Institute of Technology Taiyuan030008 China School of Chemical Engineering and Technology North University of China Taiyuan030051 China MicroNano System Research Center College of Information and Computer Key Laboratory of Advanced Tran sducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan University of Technology Taiyuan030024 China

In this work, a ZIF-67/MWCNT/Nafion sensor platform was constructed based on the good adsorption capacity of ZIF-67, the electrical conductivity of multiwalled carbon nanotubes (MWCNTs) and the excellent chemical stability of Nafion for the detection of Cu2+ in water. Meanwhile, the modified materials were characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FT-IR). Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and square wave stripping voltammetry (SWSV) were used to characterize and test ZIF-67/MWCNT/Nafion/GCE. The results show that ZIF-67/MWCNT/Nafion/GCE has high sensitivity (57.5 μA/μM) and a low limit of detection (4.3 nM) for the electrochemical detection of Cu2+ ions in an electrochemical sensing system. Finally, the modified electrode has good repeatability, reproductivity, anti-interference and stability and can be applied to test domestic water. © 2023, The Authors. All rights reserved.

关键词： Electrochemical sensors

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Abstraction-Based Verification of Approximate Pre-Opacity for control systems

arXiv

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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Unsupervised Quantitative Assessment of Furnace Combustion Process Based on Flame Images and Att-Scae Model

SSRN

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SSRN 2023年

作者： Liu, Yizhuo Lv, You Fang, Fang Qiu, Tian Liu, Jizhen Fang, Xinglong School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Changping District Beijing102206 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

Accurate and timely assessment of the flame combustion process in the furnace of coal-fired power plants is crucial for the secure and economic operation of the units. This paper proposes an unsupervised quantitative assessment method based on flame images for assessing the combustion process of a flame to address the problems that most of the current methods are highly dependent on human experience and difficult to classify the state in detail. Attention mechanism is introduced into the stacked convolutional autoencoder (SCAE) feature extraction network to obtain the latent features of the flame images. Principal component analysis (PCA) is used to reduce the dimensionality of the sparse latent feature matrix for obtaining 1D combustion features. The extracted 1D features are smoothed and normalized, and a combustion state index is obtained to reflect the degree of combustion in the furnace. The variance of the combustion state index within a sliding window is used to determine the fluctuation of the flame state as the combustion stability result. The flame images of a 660 MW coal-fired power plant under different combustion conditions and the variables related to the combustion process in the distributed control system (DCS) are captured to verify the effectiveness of the proposed method. The MSE, PNSR, and SSIM of the flame images reconstructed by the feature extraction network and the original images are 1.082×10-4，39.97dB and 0.9854, with higher accuracy compared to other autoencoder (AE) networks. 12 of the 14 absolute values of the correlation coefficients between the variables related to the combustion process and the extracted combustion state index exceed 0.7. Results suggested that the proposed method can reflect the combustion process in the furnace, which verifies the validity and reliability. © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

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Condition Monitoring and Fault Diagnosis of Coal Mill Based on Mechanism Modeling Simulation and D-S Evidence Fusion

Condition Monitoring and Fault Diagnosis of Coal Mill Based ...

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第34届中国控制与决策会议

作者： Tian Qiu Yuchen Yang Yue Guo Xiaofeng Chen Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University School of Control and Computer Engineering North China Electric Power University North China Electric Power Research Institute Co. Ltd

The coal mill is the key equipment of powdering system in coal-fired power plants,whose working state has an important influence on the combustion stability of boiler as well as the operation ***,owing to the lack of real fault data,it is difficult and not available enough to support and achieve the research of data-driven fault diagnosis ***-type medium-speed coal mill was selected as the research object in this ***,the existing mechanism model had been further improved to make it more effective and reasonable,and we used it to respectively simulate the typical operating data of the normal condition and three fault *** paper transformed the problem of fault diagnosis into the measurement of similarity between the testing and the typical fault samples,taking the numerical distance and changing trends into account,and adopted the concept of similarity function and transformed the results into the expression and calculation of probability level,which was calculated as the diagnostic evidence of individual parameters under the same time scale.D-S evidence theory was employed to fuse the individual evidence between multiple parameters evidence,getting the fusion of multiparametric evidence as to the final diagnosis ***,the experiments verified the high accuracy of the proposed diagnostic method,and the similarity function has high rationality and feasibility.

关键词： Coal Mill system Process Fault Dynamic Mathematical Model Similarity Function D-S Evidence Fusion Theory Fault Diagnosis

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Attention-Driven Graph Convolutional Networks for Deadline-Constrained Virtual Machine Task Allocation in Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ahmad Ullah, Inam Singh, Sushil Kumar Jiang, Weiwei Alturise, Fahad Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen 518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Beijing University of Posts and Telecommunications Beijing China Qassim University College of Computer Department of Cybersecurity Buraydah Saudi Arabia

Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： attention consumer electronics Edge computing load balancing machine learning resource utilization virtual machine

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Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical systems

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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An Efficient Implementation for Bayesian Manifold Regularization Method

An Efficient Implementation for Bayesian Manifold Regulariza...

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IEEE Conference on Decision and control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

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