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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical Sciences. All rights reserved.

关键词： Empirical mode decomposition

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Deep Learning for Disease Identification in Cassava Plants Using RGB Imaging 6th

Deep Learning for Disease Identification in Cassava Plants U...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Sutar, Shivam Mohite, Snehal Kamble, Tushar Kolhar, Shrikrishna Jagtap, Jayant Shastri, Rajveer Joshi, Shubham Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Maharashtra Baramati India Maharashtra Pune India Marik Institute of Computing Artificial Intelligence Robotics and Cybernetics NIMS University Rajasthan Jaipur303121 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819778614

Agriculture is the most critical sector in the world economy. Food security and consistent food supply are the key challenges due to rapid growth in the world population and crop diseases. Crop yield gets significantly hampered due to diseases attacking large crop fields. The precise classification of plant diseases is essential to provide appropriate pesticide supplements. This study applies deep learning (DL) models to identify diseases in cassava plants using red–green–blue (RGB) images. Three models, namely Xception-net, MobileNetV2, and EfficientNetB0, are trained and evaluated on the cassava disease dataset publicly available on Kaggle. Since the dataset is imbalanced, five-fold cross-validation is used to check the consistency of the results. Transfer learning and random weight initialization were used for model training. The Xception-net is the clear winner among the three models, with an accuracy of 87.8% using transfer learning, 81.33% using random weight initialization, and 85% average accuracy using five-fold cross-validation. Further, this work is extended to develop web and mobile applications with DL models deployed at the backend to predict plant diseases from input images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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Experimental performance evaluation on piezo-hydraulic pump using flexurally amplified piezoelectric actuators

Experimental performance evaluation on piezo-hydraulic pump ...

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4th International Conference on Smart and Sustainable Developments in Materials, Manufacturing and Energy Engineering, SME 2022

作者： Nayak, Sandesh Rao, Muralidhara Karkala Taluk Udupi District Karnataka Nitte574110 India Department of Robotics and Artificial Intelligence Karkala Taluk Udupi District Karnataka Nitte574110 India

This paper presents an experimental study on a piezo-hydraulic pump using two different flexurally amplified piezoelectric (FAP) actuators. FAP actuators produce amplified displacement using piezoelectric stack actuators as prime mover. FAP376 and FAP640, having different characteristics are used as pumping actuator to pump the fluid from the piezo-hydraulic pump. The pumping chamber is made of stainless-steel metallic bellows and unidirectional valves are connected to the inlet and outlet port of the piezo-hydraulic pump. Mathematical model is developed for theoretical calculations on the flowrate of piezo-hydraulic pump. Also, pump flowrate is determined experimentally using flowmeter for two different FAP actuators and compared with the respective theoretical values. Further, performance evaluation is done by comparing the flowrate developed by the piezo-hydraulic pump at different actuation frequencies using two different FAP actuators. Copyright © 2023 Elsevier Ltd. All rights reserved.

关键词： Piezoelectric actuators

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Task Allocation Game with the Loss Function 42

Task Allocation Game with the Loss Function

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42nd Chinese Control Conference, CCC 2023

作者： Zhao, Zhengwu Zhang, Chunyan College of Artificial Intelligence Nankai University Department of Automation Tianjin300071 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

ISBN: (纸本)9789887581543

Task allocation problem has drawn a wide attention in the field of artificial intelligence, and we introduce the loss function into task allocation game model to simulate the issue of maintaining a balance between economic development and environmental protection. The article establishes a mathematical model of the game, studies how the task payoff difference varies when changing parameters of the model, and uses the fixation probability to analyze the impact of the loss function on self-organized evolution of task allocation in the group. We draw three main conclusions, firstly synergistic benefit facilitates the formation of division of labor, secondly the loss function has both positive and negative effects on cooperation in the group, and finally the loss function significantly reduces the willingness of individuals to perform task. The paper presents a new application scenario of task allocation game, and provides a novel idea for controlling environmental pollution. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Environmental protection

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Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 450-465页

作者： Lujuan Dang Badong Chen Yulong Huang Yonggang Zhang Haiquan Zhao Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049China IEEE Department of Automation Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering Southwest Jiaotong UniversityChengdu 610000China

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian *** CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian *** address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is *** MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters *** concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first *** MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression *** the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters *** addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence *** enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.

关键词： Cubature Kalman filter(CKF) inertial navigation system(INS)/global positioning system(GPS)integration minimum error entropy with fiducial points(MEEF) non-Gaussian noise

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Urgency Coefficient and Responsibility Coefficient Facilitate the Evolution of Cooperation in Multi-Agent Systems 3

Urgency Coefficient and Responsibility Coefficient Facilitat...

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3rd International Conference on artificial intelligence and Computer Engineering, ICAICE 2022

作者： Zhao, Zhengwu Zhang, Chunyan Zhang, Jianlei Department of Automation College of Artificial Intelligence Nankai University Tianjin300071 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300071 China

ISBN: (纸本)9781510663473

Task allocation is a significant topic in the field of artificial intelligence, and ideas for solving this problem are often drawn from the widespread phenomenon of division of labor in nature. For the problem that the existing labor division game model does not match the characteristics of the large-scale division of labor scenarios, We propose a new model named division of labor within community game(DOLC). The article establishes the mathematical model of DOLC, then we investigate the effects of selection intensity, urgency coefficient and responsibility coefficient on the evolution of division of labor. We derived the following conclusions: When both urgency and responsibility coefficients are zero, this causes the tragedy of DOLC, while group has the opportunity to maintain the division of labor if one of them is positive. The responsible group has always been cooperative. Relative to urgency coefficient, responsibility coefficient plays a more noticeable role in facilitating the evolution of cooperation in population. DOLC model is well suited to task allocation problem in multi-agent systems, and the findings of this article provide new insights for improving the intelligence of unmanned systems and optimizing task allocation and scheduling. © 2023 SPIE.

关键词： Multi agent systems

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IoMT-Cloud Task Scheduling Using AI

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Computer Modeling in Engineering & Sciences 2023年第5期135卷 1345-1369页

作者： Adedoyin A.Hussain Fadi Al-Turjman Computer Engineering Department and Research Centre for AI and IoT Near East UniversityNicosiaTurkey Computer Engineering Department CyprusWest UniversityGazimagusaTurkey Artificial Intelligence Engineering Department and AI and Robotics Institutes Near East UniversityNicosiaTurkey Research Centre for AI and IoT Faculty of EngineeringUniversity of KyreniaKyreniaTurkey

The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the *** scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on ***,a proposition is being made for a distinct scheduling technique to suitably meet these *** manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is *** proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling *** CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and *** proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of ***,the experimented outcomes show that the HGA reduces cost as well as time profoundly.

关键词： artificial intelligence IoMT hybrid genetic algorithm cloud

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A Robust Pilot Decontamination Scheme in Massive MIMO Systems: Integrating Rateless Orthogonal STBC, Weighted Graph Coloring, and Channel Estimation

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IEEE Access 2025年 13卷 94453-94463页

作者： Hussien, Habib M. Kelem, Zelalem A. Addis Ababa Science and Technology Artificial Intelligence and Robotics Center of Excellence Department of Electrical and Computer Engineering Addis Ababa Ethiopia

Even though Multiple Input Multiple Output (MIMO) systems are one of the greatest innovations in wireless cellular network technology, they still face many challenges as we progress to 6G, hindering their ability to achieve the best throughput. In cellular networks, channel estimations are made using pilot sequences. Deploying these orthogonal time-frequency limited resources leads to pilot contamination, causing incorrect channel estimation due to mixed signals from neighboring cells and vice versa. Previous research has proven that this significantly decreases system performance. To reduce the impact and improve the output of Multiuser MIMO systems, this paper proposes a hybrid model combining Rateless Orthogonal Space-Time Block Codes (ROSTBC), Weighted Graph Coloring Pilot Allocation (WGCPA), and Maximum Likelihood Estimation (MLE). While ROSTBC has the ability to adapt to channel conditions, allowing the system to perform well despite pilot contamination, WGCPA and MLE address the problems of pilot contamination and the accuracy of channel estimation, respectively. The results showed that this hybrid scheme has proven to perform better compared to previously proposed schemes, providing a valuable contribution to reliable Massive MIMO wireless communication. © 2013 IEEE.

关键词： Maximum likelihood estimation

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Adaptive Robust Control Integrated With Gaussian Processes for Quadrotors: Enhanced Accuracy, Fault Tolerance and Anti-Disturbance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3235-3248页

作者： Liang, Weisheng Amer, Abdelhakim Mehndiratta, Mohit Chen, Zheng Yao, Bin Kayacan, Erdal Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China Aarhus University Artificial Intelligence in Robotics Laboratory Department of Electrical and Computer Engineering Aarhus8000 Denmark GIM Robotics Espoo02650 Finland Zhejiang University Ocean College Ocean Research Center of Zhoushan Zhoushan316021 China Purdue University School of Mechanical Engineering West Lafayette47907 United States Department of Electrical Engineering and Information Technology Paderborn33098 Germany

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances. © 2013 IEEE.

关键词： Robust control

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