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IEEE Conference on Decision and control

作者： Jiawei You Xinliang Dai Yuning Jiang Haoyu Yin Yuanming Shi Colin N. Jones School of Information Science and Technology ShanghaiTech University China EPFL Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland Department of Electrical and Systems Engineering Washington University in St. Louis USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Vehicle-to-grid Simulation Pricing Prediction algorithms Real-time systems Robustness Convergence Load flow Energy storage Predictive control

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The Impact of Recommendation Systems on Opinion Dynamics: Microscopic Versus Macroscopic Effects

The Impact of Recommendation Systems on Opinion Dynamics: Mi...

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IEEE Conference on Decision and control

作者： Nicolas Lanzetti Florian Dörfler Nicolò Pagan Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zürich Zürich Switzerland Department of Informatics Social Computing Group University of Zürich Zürich Switzerland

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users' confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system.

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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

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Computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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Tcs-Tp: Transporter Protein Prediction Based on Multi-Scale Feature Extraction

SSRN

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SSRN 2024年

作者： Yu, Fan Chen, Jiansen Fu, Qingwei Zheng, Qianying College of Physics and Information Engineering Fuzhou University Fuzhou China Department of Nosocomial Infection Control Fujian Medical University Union Hospital Fuzhou China Fujian Key Laboratory of Clinical Laboratory Technology for Precision Medicine Fujian Medical University Fuzhou China

Transporter Proteins play a crucial role in maintaining ionic homeostasis inside and outside the cell, facilitating protein uptake, and enabling cellular communication with the external environment. TPs are also significant targets for medical research and drug development. Accurately predicting novel TPs remains a major challenge in functional genomics. In this study, we propose a TP prediction models, designated as TCS-TP, that utilizes Transformer and Convolutional Neural Networks for multi-scale feature extraction of protein sequences. The Transformer incorporates the GLU activation function, while the CNN features three parallel sub-networks. Support Vector Machines is used as a classifier for TP classification. The test results demonstrate TCS-TP could successful recognition of TPs, with AUROC of 0.89, AUPRC of 0.81, and accuracy of 91.6617%. Upon further comparison, it is determined that TCS-TP outperforms other methods. We hoped that TCS-TP will prove to be a valuable tool for predicting TPs in large-scale genomic projects and contribute to the discovery of new TPs. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects

arXiv

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arXiv 2023年

作者： Lanzetti, Nicolas Dörfler, Florian Pagan, Nicolò The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Zürich8003 Switzerland The Social Computing Group Department of Informatics University of Zürich Zürich8003 Switzerland

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users’ confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system. © 2023, CC BY.

关键词： Recommender systems

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Study on the Improved Water Flow Prediction Based on Classification-Regression Approach for Amphibious Spherical Robots

Study on the Improved Water Flow Prediction Based on Classif...

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IEEE International Conference on Mechatronics and Automation

作者： Jun Leng Shuxiang Guo Chunying Li Sheng Cao School of Control Science and Engineering Shandong University Jinan China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Inspired by the lateral line systems found in aquatic organisms, an artificial lateral line system (ALLS) for underwater robots was developed. This technology was integrated into Autonomous Underwater Vehicles (AUVs) to enhance their environmental awareness and navigation. This paper was focused on improving water flow prediction performance for a amphibious spherical robot (ASR) equipped with a ALLS comprising 12 pressure sensors. To address the challenge of the initial method relying on polynomial fitting based on data from a single sensor, which encounters difficulties when the flow direction falls between sensor angles. Two strategies were explored: increasing the quantity of sensors and implementing a Classification-Regression approach. Experimental simulations using Computational Fluid Dynamics (CFD) software were conducted to investigate the effectiveness of these strategies. Experimental results indicated that the classification-regression method significantly enhanced prediction accuracy compared to the original approach and offered a cost-effective alternative to increasing sensor quantity. This research contributed to the advancement of underwater robotics by enhancing perception capabilities, which were crucial for navigating challenging underwater environments efficiently.

关键词： Autonomous underwater vehicles Analytical models Accuracy Navigation Computational fluid dynamics Fitting Robot sensing systems

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Therapeutic Benefits of Xbox Kinect Training on Upper Limb Motor Function in Chronic Stroke Patients 8

Therapeutic Benefits of Xbox Kinect Training on Upper Limb M...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Ul, Ain Qurat Khan, Sara Ishtiaq, Summaiya Alsaied, Alrawda Liu, Tian Wang, Jue Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an China National Engineering Research Center for Healthcare Devices Guangzhou Guangdong China Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi'an China Mukkabir College Gujrat Physiotherapy Department Gujrat Pakistan Shifa Tameer-e-Millat University Department of Physical Therapy Islamabad Pakistan School of Control Science and Engineering Shandong University Laboratory of Motor Control and Rehabilitation Department of Biomedical Engineering Jinan China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Objective: The use Xbox Kinect in stroke rehabilitation is recently increased due to its therapeutic effects. The current study aims to investigate the effect of Xbox based Kinect training for rehabilitation of upper limb function in stroke patients. Methods: A single blinded randomized control experiment was carried out on chronic stroke patients referred from the outpatient rehabilitation department of the Railway General Hospital in Rawalpindi. A total of 56 patients were included in the trial, with 50 completing the intervention (25 in the Xbox Kinect Training Group and 25 in the Exercise Training Group). The outcome measures were measured using the Modified Ashworth Scale (MAS) and the Wolf Motor Function Test (WMFT) before and after the 6-week intervention. The study was approved by Riphah International University's ethical committee, and all participants signed a written informed consent form before taking part. Results: Mean age of participants in Xbox training group was 57.48 10.60 and in exercise training group mean age was 57.68 10.43. MAS had no significant difference across the two group 0.338 however, it was significantly improved in both groups (XKTG © 2022 IEEE.

关键词： Virtual reality

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MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention 22

MRIGAT: Many-to-many Recommendation using Interaction based ...

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3rd International Symposium on Artificial Intelligence for medical Sciences, ISAIMS 2022

作者： Gao, Feng Yuan, Kai Gu, Jinguang Liu, Yun Department of Computer Science and Technology Wuhan University of Science and Technology China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China China Guangxi Eco-engineering Vocational & Technical College China 1st Affiliated Hospital Dept. of Medical Informatics School of Biomedical Engineering & Informatic Nanjing Medical University China

ISBN: (纸本)9781450398442

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively. © 2022 ACM.

关键词： Knowledge graph

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A Novel Transfer Learning Approach for Detecting Partial Shading in Photovoltaic Systems

A Novel Transfer Learning Approach for Detecting Partial Sha...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Ali Teta Maissa Medkour Ahmed Chennana Ammar Chouchane Yassine Himeur Shadi Atalla Wathiq Mansoor El Ouanas Belabbaci Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering University of Djelfa Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In Photovoltaic Systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems.

关键词： Photovoltaic systems Accuracy Scalability Transfer learning Time series analysis Solar energy Big Data Feature extraction Distance measurement Reliability

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Adaptive Spectral Broadening Filter-Based Phased Array Focused Ultrasound Imaging for High-Echo Target Detection

SSRN

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SSRN 2024年

作者： Lou, Cuijuan Zhou, Quan Li, Ranyang Liu, Wufeng Li, Yan Yuchi, Ming Ding, Mingyue Key Laboratory of Grain Information Processing and Control Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Department of Automation School of Automation Wuhan University of Technology Wuhan430070 China Department of Biomedical Engineering School of Life Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education Medical Ultrasound Laboratory Huazhong University of Science and Technology Wuhan430074 China

Noise suppression and resolution improvement are important issues in high-echo target ultrasound imaging detection. To address this problem, this paper proposes a phased array focused ultrasound imaging method based on an adaptive spectral broadening filter. The proposed method first standardizes the received signals after fixed-point transmission by subtracting the mean of the received aperture signals and dividing by the standard deviation of the received aperture signals. Then, spectralbroadening was achieved through covariance and temporal autocorrelation coefficient calculations. Finally, the variance factor proposed for the first time is calculated as a weight from standardized received data after fixed-point transmission focusing to obtain the final scanline data. To evaluate the performance of the proposed method in high-echo target ultrasound imaging detection, experimental phantom images and two views of the interventricular septum including the parasternal long-axisviews and the apical four-chamber views, are reconstructed. In three scenarios, some finer structures of the targets in the images by the proposed method can be more easily identified compared to delay and sum (DAS), square-neighborhood robust signal mean-to-standard-deviation factor (SN-RSMSF), and coherence factor beamforming (CF). For the experimental phantom data, the image resolution measured by -20dB mainlobe width of the proposed method can reach 14.2 percents of DAS, 18.3 percents of SN-RSMSF and 18.3 percents of CF. The proposed method also outperforms the other three methods in terms of noise suppression. The proposed method can be used to detect diseases that involve high-echo targets such as stones, heart valves, and calcifications. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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