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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Wen, Sicheng Xu, Jiajun University of Engineering Science Department of Electronic and Electrical Engineering Shanghai Shanghai China University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350355642

The development of data-driven soft sensors for modeling complex data, particularly in scenarios characterized by strong nonlinearity, high dimensionality, cross-correlation and autocorrelation, remains a significant challenge in process data analysis. Bidirectional Long Short-Term Memory (BiLSTM) networks are highly effective in capturing both forward and backward dependencies within sequences, thereby enhancing the extraction of temporal features from time series data. However, BiLSTM networks may struggle to capture global dependencies, especially in long sequences. To address this limitation, the self-attention mechanism, which excels in modeling global relationships across all positions in the input sequence, can be integrated with BiLSTM by applying weighted attention to the two different directions of BiLSTM, thereby enhancing its performance. This study proposes a novel method called SA-BiLSTM, which combines the strengths of the BiLSTM network and the self- attention mechanism. The self-attention layer captures global dependencies, while the BiLSTM layer extracts comprehensive temporal features in both directions, which are then passed to a fully connected layer for final prediction. Extensive comparisons with traditional methods such as BiLSTM, LSTM with added self-attention mechanism, LSTM, RNN, and SVM validate the superior performance of the proposed SA-BiLSTM method in handling complex time series data. © 2024 IEEE.

关键词： Spatio-temporal data

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Fréchet Edit Distance 40

Fréchet Edit Distance

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Fox, Emily Nayyeri, Amir Perry, Jonathan James Raichel, Benjamin Department of Computer Science University of Texas DallasTX United States School of Electrical Engineering and Computer Science Oregon State University CorvallisOR United States

ISBN: (纸本)9783959773164

We define and investigate the Fréchet edit distance problem. Given two polygonal curves π and σ and a threshhold value δ > 0, we seek the minimum number of edits to σ such that the Fréchet distance between the edited σ and π is at most δ. For the edit operations we consider three cases, namely, deletion of vertices, insertion of vertices, or both. For this basic problem we consider a number of variants. Specifically, we provide polynomial time algorithms for both discrete and continuous Fréchet edit distance variants, as well as hardness results for weak Fréchet edit distance variants. © Emily Fox, Amir Nayyeri, Jonathan James Perry, and Benjamin Raichel.

关键词： Hardness

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Alzheimer's Disease Stage Classification using Blood Data 2

Alzheimer's Disease Stage Classification using Blood Data

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rasheed, Asif Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead University Department of Computer Science Thunder Bay Canada Western University Department of Computer Science London Canada Idaho State University Department of Electrical and Computer Engineering Pocatello United States

ISBN: (纸本)9798331529529

In this work, we aim to evaluate the performance of Machine Learning models in the classification of Alzheimer's patients into disease stages using two feature selection methods proposed in our previous work. The first method identifies relevant features by ranking them according to their dependency on the diagnosis, which is measured using metrics such as Mutual Information, Symmetric Uncertainty and Cramer's V. The second method filters relevant features by applying a threshold to the Euclidean distance between the class means for each feature. Three new approaches are proposed to accommodate multiple classes in the Euclidean distance-based feature selection method. These approaches involve scoring each feature using the minimum, maximum and average of the Euclidean distances between the class means. Using two datasets, we extensively tested all possible combinations of relevant features identified using the two methods, ensuring minimal correlation among features in each panel. The panels achieved accuracies as high as 75.17% and 85.46% on the first and second datasets, respectively. The performances achieved in this work demonstrate the feasibility of using blood-based biomarkers in Alzheimer's disease stage classification and could help in developing accurate low-cost and non-invasive early detection tools that could aid in Alzheimer's disease diagnosis. © 2024 IEEE.

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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RISK-AWARE REINFORCEMENT LEARNING WITH COHERENT RISK MEASURES AND NON-LINEAR FUNCTION APPROXIMATION 11

RISK-AWARE REINFORCEMENT LEARNING WITH COHERENT RISK MEASURE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Lam, Thanh Verma, Arun Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science MIT United States

We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon Markov decision process with unknown transition and reward functions. In contrast to the risk-neutral RL problem, we consider minimizing the risk of having low rewards, which arise due to the intrinsic randomness of the MDPs and imperfect knowledge of the model. Our work provides a unified framework to analyze the regret of risk-aware RL policy with coherent risk measures in conjunction with non-linear function approximation, which gives the first sub-linear regret bounds in the setting. Finally, we validate our theoretical results via empirical experiments on synthetic and real-world data. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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Automatic Skull Shape Completion of Defective Skulls Using Transformers for Cranial Implant Design 2

Automatic Skull Shape Completion of Defective Skulls Using T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Resmi, S. Singh, Rimjhimpadam Palaniappan, Kannappan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electrical Engineering and Computer Science University of Missouri MO United States

Cranioplasty is a surgical method that restores the aesthetic and protecting function of a damaged skull by implanting material into the damaged *** and accurate design of patient specific cranial implants is very much required in the process of *** time consumption for designing and manufacturing of patient specific cranial implant has become an obstruction for cranioplasty procedures. Hence, a fully automatic and fast design of cranial implant becomes very important. The cranial implant design processmainly comprises of two steps. The former step concentrates on the automatic skull shape completion of defective skulls to fill the gaps and the cracks created in the skull. While the second step computes the difference between defective input and the completed skull for generating the implant. Currently computer aided design is used for the skull shape completion task which is a time consuming process. The application of deep learning techniques may result to faster and accurate skull shape completionwhich can be used for the design of patient specific cranial implants. This work proposes a novel approach combining 3D U-Net with Transformers for the automatic skull shape completion *** are using a vision transformer in the encoder section of the 3D U-Net architecture to consider the volumetric skull reconstruction as sequence- to-sequence prediction problem and to efficaciously grasp the global contextual information. The work also compares its performance with the famous variants of 3D U-Net deep network model namely, 3D U-Net and 3D U-Net with attention. From the values resulted for the dice coefficient metric it is clear that the proposed 3D U-Net with transformer approach performs better than the other two models on test images. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer aided design

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PUF-Based Authentication for the Security of IoT Devices

PUF-Based Authentication for the Security of IoT Devices

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Oun, Ahmed Niamat, Mohammed Ohio Northern University Department of Electrical and Computer Engineering and Computer Science AdaOH45810 United States University of Toledo Department of Electrical Engineering and Computer Science ToledoOH43606 United States

ISBN: (纸本)9781665493765

The Internet of Things (IoT) has become in demand nowadays as many embedded devices are connected to the internet to collect a vast amount of data for processing. A substantial amount of this data between IoT devices is confidential information;therefore, attacks on IoT devices are growing, causing challenges to the security of the IoT devices. Physical Unclonable Functions (PUFs) are proposed as a powerful and lightweight solution to secure IoT devices. PUFs authenticate and generate secure cryptographic keys using manufacturing process variations to protect IoT devices from different attacks;this unique identity is based on physical characteristics. Device authentication is an essential task in IoT. In this paper, we propose a lightweight XOR-ROPUF based authentication scheme for the security of IoT systems. The proposed management scheme carries out the authentication between the verification authority and the IoT devices to ensure data congeniality and integrity, and thus reduces the risk of cyber attacks. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Machine Learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review 4

Machine Learning Applications for Online Partial Discharge D...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Tag, Ameera Refaat, Shady S. Kameli, Sayed Mohammad Saleh, Mohammad Alshaikh Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Texas A&M University Department of Electrical and Computer Engineering College Station United States

ISBN: (纸本)9798350306262

The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. The detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. Therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis. © 2024 IEEE.

关键词： Partial discharges

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FedSTGNN: A Federated Spatio-Temporal Graph Neural Network 15

FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yoon, Heeyong Chon, Kang-Wook Kim, Min-Soo Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Computer Science and Engineering Koreatech Cheonan Korea Republic of School of Computing Kaist Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy. © 2024 IEEE.

关键词： Spatio-temporal data

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