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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Optimal convergence rates of totally asynchronous optimization

Optimal convergence rates of totally asynchronous optimizati...

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IEEE Conference on Decision and control

作者： Xuyang Wu Sindri Magnússon Hamid Reza Feyzmahdavian Mikael Johansson Division of Decision and Control Systems School of EECS KTH Royal Institute of Technology Stockholm Sweden Department of Computer and System Science Stockholm University Stockholm Sweden ABB Cooperate Research Västerås Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information delays and several important algorithms lack guarantees when they operate under total asynchrony. In this paper, we derive explicit convergence rates for the proximal incremental aggregated gradient (PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under a specific model of total asynchrony, and show that the derived rates are order-optimal. The convergence bounds provide an insightful understanding of how the growth rate of the delays deteriorates the convergence times of the algorithms. Our theoretical findings are demonstrated by a numerical example.

关键词： Machine learning algorithms Computational modeling Machine learning Numerical models Delays Resource management Indexes

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Comparative study for different types of mppt algorithms using direct control method 1

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2nd International Conference on Electrical and Electronics Engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： controllers

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Trajectory Planning for Autonomous Vehicles combining Nonlinear Optimal control and Supervised Learning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15608-15614页

作者： Lukas Markolf Jan Eilbrecht Olaf Stursberg Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Germany

This paper considers computationally efficient planning of reference trajectories for autonomous on-road vehicles in a cooperative setting. The basic element of the approach is the notion of so-called maneuvers, which allow to cast the nonlinear and non-convex planning task into a highly structured optimal control problem. This can be solved quite efficiently, but not fast enough for online operation when considering nonlinear vehicle models. Therefore, the approach proposed in this paper aims at approximating solutions using a supervised learning approach: First, training data are generated by solving optimal control problems and are then used to train a neural network. As is demonstrated for a cooperative overtaking maneuver, this approach shows good performance, while (contrasting approaches like reinforcement learning) requiring only low training effort.

关键词： Nonlinear optimal automotive control automated driving machine learning intelligent control neural networks

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Efficient Solution of Distributed MILP in control of Networked systems

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IFAC-PapersOnLine 2020年第2期53卷 6723-6729页

作者： Zonglin Liu Olaf Stursberg Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Germany

This paper considers the distributed solution of Mixed-Integer Linear Programming (MILP) problems, a class of problems which is of interest, e.g., in optimization-based control of networked systems involving hybrid dynamics. For a larger number of subsystems, the high combinatorial complexity arising from the integer variables usually prohibits the use of centralized solution schemes, and thus requires distributed computational approaches. The proposed approach is inspired by results based on the Shapley-Folkman-Starr theorem, but it relaxes some conservative assumptions in order to enhance the computational efficiency. Numerical experiments for different MILP problems confirm the advantage of the proposed method with respect to computation times.

关键词： Optimization distributed control computational methods optimal control networked systems hybrid systems complexity integer programming

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Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

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Security and Communication Networks 2023年第1期2023卷

作者： Shankar, Gauri Ai-Farhani, Liwa H. Anitha Christy Angelin, P. Singh, Parvinder Alqahtani, Abdullah Singh, Abha Kaur, Gaganpreet Samori, Issah Abubakari Chandigarh University Mohali Punjab Mohali140413 India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia PSNA College of Engineering and Technology Tamilnadu Dindigul624622 India Central University of Punjab Bathinda Punjab 151001 India Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Basic Science College of Science and Theoretical Study Dammam-Female Branch Saudi Electronic University Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Engineering Sciences University of Ghana Accra Ghana

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user's biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article's main findings are that the Ed25519 algorithm can be used in blockchain. © 2023 Gauri Shankar et al.

关键词： Digital forensics

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Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface

Dynamic time warping-based transfer learning for improving c...

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作者： Azab, Ahmed M. Ahmadi, Hamed Mihaylova, Lyudmila Arvaneh, Mahnaz Department of Automatic Control and System Engineering Sheffield University Sheffield United Kingdom School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

Objective. Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. Approach. The proposed framework combines the subject-specific covariance matrix (Σ55) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix (ΣDTW) estimated using previous subjects trials. In the proposed ΣDTW, the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. Main results. The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Significance. Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class. © 2020 IOP Publishing Ltd.

关键词： Covariance matrix

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RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

arXiv

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arXiv 2023年

作者： Nie, Chang Wang, Guangming Liu, Zhe Cavalli, Luca Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Department of Computer Science ETH Zürich Switzerland Microsoft Mixed Reality AI Zürich Lab Switzerland

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://***/IRMVLab/RLSAC. © 2023, CC BY.

关键词： Reinforcement learning

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Optimal convergence rates of totally asynchronous optimization

arXiv

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arXiv 2022年

作者： Wu, Xuyang Magnńsson, Sindri Feyzmahdavian, Hamid Reza Johansson, Mikael The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden ABB Cooperate Research VästeråsSE-721 78 Sweden

关键词： Machine learning

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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