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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

Frontiers of Computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

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Neural computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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Interpretation of Electroencephalogram using RNNs to Predict Cognitive Disorders 3

Interpretation of Electroencephalogram using RNNs to Predict...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Moovendran, Manikandan Surendran, S. Midhun, C. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India SRM Institute of science and technology Chennai603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

EEG signals are very useful in giving an insightful view of what's going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic behavior of the data. The present paper introduces a new approach to the analysis of EEG signals using Recurrent Neural Networks, with a particular focus on recognizing temporal dependencies that exist in sequential data. By exploiting the RNN property of information retention at different time steps, we demonstrate enhanced accuracy in pattern recognition and classification of cognitive states. Our model is trained over a massive dataset of EEG recordings using the LSTM units to get rid of vanishing gradients frequently encountered in deep-learning models. The results from the experiments are given below and illustrate how the proposed method compares with traditional machine learning techniques in terms of accuracy and robustness. It is a promising tool for all applications in neurological diagnosis, cognitive research, and developing a brain-computer interface. © 2024 IEEE.

关键词： Recurrent neural networks

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Levelised Cost of Hydrogen from a Dedicated Offshore Wind Farm 7

Levelised Cost of Hydrogen from a Dedicated Offshore Wind Fa...

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7th Offshore Energy and Storage Symposium, OSES 2023

作者： Pegler, David L. Rawlinson-Smith, Robert Greaves, Deborah School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9781839539596

The global hydrogen economy is currently at inception. Forecasts suggest that hydrogen could deliver 10% of global energy by 2050. The UK Climate Change Committee (CCC) forecast the UK will consume 220TWh.H2/yr in 2050 of which 95TWh will be derived from electrolysis. UK electrical power demand is expected to double to >600TWhe/yr by 2050, with the majority coming from offshore wind (OSW). Around the UK there are marine regions rich in wind resource, but remote from centres of population. Exploiting these wind resources conventionally would require expensive upgrades to the electrical transmission system;direct production of hydrogen offers an alternative energy vector. Hydrogen production from OSW appears an inevitability, and in certain regions potentially financially advantageous. This report picks up from work prepared by the University of Cork, and presents a 510MW OSW, consisting of 34 fixed bottom, 15MW Wind Turbine Generators (WTG). The primary metric adopted is the Levelised Cost of Hydrogen (LCoH). Sensitivities for electrolyser capacity, Discount Rate (DR), and electrolyser efficiency were examined. The base model returned an LCoH of £5.69/Kg.H2 with a DR equal to 6%. © The Institution of engineering & Technology 2023.

关键词： Climate change

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

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Intelligent Automation & Soft computing 2023年第4期36卷 981-996页

作者： Yao Wang Yubo Wang Zhenhu Ning Sadaqat ur Rehman Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Natural and Computing Science University of AberdeenUK School of Engineering Edith Cowan UniversityPerth6027Australia

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of *** is a wide demand for cooperation between equipment and management institutions in the smart ***,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious ***,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not *** at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is *** most trust models,this model uses self-certiﬁed *** cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certiﬁcate stored in the previous domain *** the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity ***,the simulation results show that CDTM has good anti collusion attack *** success rate of malicious interaction will not increase signiﬁ*** with other model

关键词： Smart city cross-domain trust model self-certiﬁcation trust evaluation

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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Techno-economic analyses of Sulfinol-based absorber-stripper configurations for improving the energy consumption of natural gas sweetening process

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South African Journal of Chemical engineering 2023年 43卷 91-103页

作者： Sahl, Abdulqader Bin Mahmoud, Ahmed Kong, Zong Yang Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching 93350 Malaysia Deptartment of Chemical Engineering and Chemical Technology Faculty of Engineering and Technology University of Gezira Sudan

This work presents a two-step method to reduce the energy consumption (in terms of the reboiler duty) of a natural gas sweetening process via a physical-chemical solvent mixture (Step 1) followed by a techno-economic analysis of 15 different absorber-stripper configurations (Step 2). Initially, an amine-based sweetening process using a H₂S-free sour gas was simulated on Aspen HYSYS and the simulation results were validated against real plant data. In Step 1, different ratios of Sulfinol-M and Sulfinol-D based sweetening process were simulated and the superior physical-chemical solvent mixture that provides the lowest reboiler duty while meeting the sweet gas CO₂ product concentration specification (i.e., less than 2 mol.%) is the Sulfinol-M solvent that contains 38 wt.% methyl diethanolamine (MDEA), 40 wt.% Sulfolane, and 22 wt.% water. This Sulfinol-M based model is then optimized via a parametric analysis to further improve its performance in terms of the reboiler duty prior to Step 2. The optimized Sulfinol-M based model achieved 65% saving in the reboiler duty while fulfilling the CO₂ concentration specification in product gas relative to the base case. In Step 2, four main absorber-stripper configurations, namely absorber intercooling (AI), lean amine stream split flow (LASSF), rich amine stream split flow (RASSF), and mechanical vapor recompression (MVR) along with their 11 possible combinations were simulated in an attempt to decrease the energy consumption of the process and hence, ranked based on their total cost of production (TCOP). The results revealed that the integration of LASSF + RASSF provides the best option as it achieved the lowest TCOP of $4.60 M while fulfilling the CO₂ concentration in product gas and contributed to 6.39% savings in the reboiler duty compared to the optimized conventional Sulfinol-M based model. © 2022

关键词： Aspen HYSYS Energy consumption Natural gas sweetening Sulfinol-D Sulfinol-M Techno-economic analysis

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Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5791-5804页

作者： Cheng, Hailun Huang, Shenjin Cai, Linghan Xu, Yangfan Wang, Runming Zhang, Yongbing Tsinghua University Institute of Biopharmaceutical and Health Engineering Shenzhen International Graduate School Shenzhen518055 China Harbin Institute of Technology Faculty of Computing Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

Computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally employ attention mechanisms to aggregate instance-level features. However, the weakly supervised signal and the imbalanced instance distribution often lead to inaccurate attention localization, compromising the performance and generalization capability of the MIL framework. To address these problems, this paper presents a novel MIL framework called FAMIL that focuses on inaccurate attention and refines them. FAMIL adopts a dual-branch structure and incorporates two innovative online data augmentation strategies: attention-based Mixup (ABMix) and attention-based Masking (ABMask). ABMix emphasizes the significance of positive instances, generalizing Mixup in the MIL scenarios, while ABMask flexibly identifies challenging positive instances to optimize the feature representation. Moreover, these two methods are plug-and-play and can be easily embedded into attention-based MIL methods. Extensive experiments on three public benchmarks demonstrate the superiority of our FAMIL, outperforming current state-of-the-art methods. The test AUC for the binary tumor classification can be up to 92.61% over CAMELYON16. And the AUC over the cancer subtype classification can be up to 93.81% and 98.41% on TCGA-NSCLC and TCGA-RCC datasets, respectively. © 1991-2012 IEEE.

关键词： Image classification

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance 46

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Horvat, M. Jercic, P. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

ISBN: (纸本)9789532331042

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors' intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers' opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey. © 2023 MIPRO Croatian Society.

关键词： Database systems

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