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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic Engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer Chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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An Interactive Annotation Method Based on Incremental Learning 2

An Interactive Annotation Method Based on Incremental Learni...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Duan, Xiusheng Sun, Guohua Cao, Jingya Zhang, Yuyang Yang, Jingchao Han, Duyu Hebei Polytechnic Institute Department of Artificial Intelligence and Big Data Shijiazhuang China Hebei Provincial University Road Traffic Perception Intelligent Application Technology Research and Development Center Shijiazhuang China Shijiazhuang Tiedao University School of Mechanical Engineering Shijiazhuang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Hebei Jiaotong Vocational and Technical College Shijiazhuang China

ISBN: (纸本)9798350331417

In recent years, military science and technology has been developed rapidly, and new equipments has been equipped in the army. Augmented Reality (AR) technology provides the possibility to solve the problem of equipments operation training. But in the training process, One of the essential technologies is how to location and identify the operation keys. Obviously, the sample set of equipments operation keys is the basis of key recognition, and a certain scale of sample set is an indispensable element to ensure the performance of the recognition model. On the basis of manually establishing a small sample set, this paper puts forward a mechanism based on interactive automatic sample labeling and sample set expanding, the target recognition model was updated by incremental learning method at the same time. © 2023 IEEE.

关键词： data annotation incremental learning interaction sample augmentation

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Big data and AI in Financial Risk Assessment: The New Scorecard Approach

Big Data and AI in Financial Risk Assessment: The New Scorec...

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Information Technology,Big data and artificial intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Hualin Li Kai Chen Suyi Yang Dequan Zhang Chenchen Zhou Kaidong Li Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center China Shu Yi Xin Credit Management Co LTD China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

This paper presents a framework for assessing financial risk across industries by integrating rule-based modeling, the Analytic Hierarchy Process (AHP), and artificial intelligence (AI) techniques. The framework utilizes diverse data sources, including financial, tax, and judicial records, as well as financial reports from listed companies, to improve enterprise credit *** process begins with data preparation, which involves collecting and preprocessing data to ensure its quality and consistency. A rule-based preprocessing algorithm is developed to standardize the data for scoring. In the second phase, scoring is done using rule-based modeling informed by expert advice and AHP, which prioritizes and weights factors impacting industrial risk. This generates a customized scorecard for each industry. The third phase focuses on training AI models on the dataset. These models automatically learn to assign weights to indicators and generate industry scores by identifying complex patterns between the data and industry performance. The framework concludes with cross-validation to confirm the accuracy and reliability of the results. This approach provides insights into industry-wide scoring, supports continuous monitoring of industry performance, and enhances financial risk management decision-making. The proposed framework offers a robust and reliable tool for assessing financial risk across various industries.

关键词： Industries Training Accuracy Profitability Soft sensors Big data data models Stability analysis Reliability artificial intelligence

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SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

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IEEE Transactions on Consumer Electronics 2023年第3期69卷 250-260页

作者： Chen, Tsai-Min Tsai, Yuan-Hong Tseng, Huan-Hsin Liu, Kai-Chun Chen, Jhih-Yu Huang, Chih-Han Li, Guo-Yuan Shen, Chun-Yen Tsao, Yu National Taiwan University and Academia Sinica Graduate Program of Data Science Taipei106319 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Taiwan Artificial Intelligence Academy Foundation Program of Taiwan AI Academy New Taipei22065 Taiwan Artificial Intelligence Foundation Technology Development Center New Taipei24158 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan National Taiwan University Department of Mathematics Taipei10617 Taiwan

A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances. © 1975-2011 IEEE.

关键词： Electrocardiography

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A Multi-Signal Perception Network for Textile Composition Identification 48

A Multi-Signal Perception Network for Textile Composition Id...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Peng, Bo He, Liren Wu, Dong Chi, Mingmin Chen, Jintao Shanghai Key Laboratory of Data Science Fudan University School of Computer Science Shanghai China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center Zhongshan China Shanghai Fabric Eyes Artificial Intelligence Technology Co. Ltd Shanghai China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China

ISBN: (纸本)9781728163277

Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-Signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches. © 2023 IEEE.

关键词： Computer vision

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： data reliability

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research on DMP-based gait generation and control method for prosthetic limbs 12

Research on DMP-based gait generation and control method for...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Liu, Meiqi Xuan, Bokai Liu, Yifei Geng, Yanli School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300400 China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education Tianjin300131 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The gait profile of the prosthetic joint and the control of the motion trajectory of the prosthesis are key factors affecting the adaptation of the prosthesis to different motion states. Prosthetic systems that are controlled based on predefined gait curves suffer from poor environmental adaptation and control effects. In this paper, a dynamic movement primitives based learning strategy is proposed to address this problem, which provides motion track information through prediction models under different load conditions and controls the prosthetic system using sliding mode variable structure control. First, the human walking lower limb motion analysis is modeled to obtain the angle and moment data of the lower limb joints;then, the DMP(dynamic movement primitives) is used to generate the joint angle and joint moment distribution curve learning trajectories of the human body under different loads;finally, the dynamics model of the prosthetic test platform is derived using the Lie group Lie algebra, and the sliding mode control is performed based on the dynamics model. The experimental results show that the gait trajectory of the DMP-based prosthetic limb has the same trend with primary curve the control of the prosthetic limb based on the sliding mode control has ideal angle and angular velocity curve tracking effect within a certain error range. © 2022 IEEE.

关键词： Sliding mode control

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Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System

arXiv

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arXiv 2024年

作者： Islek, Irem Oguducu, Sule Gunduz Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey ITU AI Research and Application Center Istanbul Turkey

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously;different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics

arXiv

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arXiv 2023年

作者： Budan, Gokhan Damiani, Francesca Kurtulus, Can Ure, Nazim Kemal Eatron Technologies Ltd. Research and Development Warwick CV34 6UW United Kingdom Istanbul Technical University Artificial Intelligence and Data Science Application Research Center Istanbul Turkey

Model identification of battery dynamics is a central problem in energy research;many energy management systems and design processes rely on accurate battery models for efficiency optimization. The standard methodology for battery modelling is traditional design of experiments (DoE), where the battery dynamics are excited with many different current profiles and the measured outputs are used to estimate the system dynamics. However, although it is possible to obtain useful models with the traditional approach, the process is time consuming and expensive because of the need to sweep many different current-profile configurations. In the present work, a novel DoE approach is developed based on deep reinforcement learning, which alters the configuration of the experiments on the fly based on the statistics of past experiments. Instead of sticking to a library of predefined current profiles, the proposed approach modifies the current profiles dynamically by updating the output space covered by past measurements, hence only the current profiles that are informative for future experiments are applied. Simulations and real experiments are used to show that the proposed approach gives models that are as accurate as those obtained with traditional DoE but by using 85% less resources. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce

SSRN

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SSRN 2023年

作者： Cakir, Altan Gurkan, Mert Physics Engineering Faculty of Science and Letters Istanbul Technical University Istanbul34469 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34469 Turkey Istanbul34415 Turkey

This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query-document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10\% compared to baseline models. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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