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Chat, Summary and Diagnosis: A LLM - Enhanced Conversational Agent for Interactive Depression Detection

Chat, Summary and Diagnosis: A LLM - Enhanced Conversational...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Xiaoheng Zhang Weigang Cui Junjie Wang Yang Li Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Depression is a significant mental illness that affects how individuals express their emotions and engage with others, making communication challenging. Most depression assessment tools utilize self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9). these psychometric instruments can be easily adapted to electronic forms. However, this approach cannot provide human-like explanations and interactions, leading to a poor interactivity. Furthermore, we have identified critical limitations in previous prompting methods. they are either constrained to queries using a single identifiable relation, or being agnostic to input contexts, making it difficult to capture variabilities that occur across different inference steps. To solve these issues, we develop a large language models LLM-enhanced conversational agent for depression detection, which makes it more effective and interactive. Specifically, we first explore an iterative knowledge-aware prompter (IKP), a new prompting paradigm which inject specific knowledge from language models progressively for multi-step reasoning, which learns to synthesize prompts conditioned on the current step's contexts. Second, our proposed system introduces a multi-step diagnosis (MSD) approach. Our system not only delivers a diagnosis but also generates a symptom summary through interactive conversations. Our proposed agent enables users to have interactive natural language dialogues with the system, enhancing their personalized comprehension of mental states. Our experiments demonstrate the effectiveness of the iterative knowledge-aware prompter design.

关键词： Control engineering Service robots Large language models Instruments Natural languages Oral communication Depression Iterative methods Next generation networking Context modeling

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FedTMatch: Self-Supervised Federated Semi-Supervised Learning with Dynamic threshold

FedTMatch: Self-Supervised Federated Semi-Supervised Learnin...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qing Zhao Jielei Chu Zhaoyu Li Hua Yu Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China CREC R&D Center for Carbon Peaking and Carbon Neutrality China Railway Eryuan Engineering Group Co. Ltd. Chengdu China China E-port Data Center Chengdu Branch Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Federated semi-supervised learning (FSSL) has emerged as a promising paradigm to address the challenges of data privacy and the scarcity of labeled data. Most existing algorithms adapt the pseudo-labeling and consistency regularization methods in traditional semi-supervised learning (SSL) to exploit raw data from unlabeled clients. However, these methods always use the same high fixed threshold to filter out noisy pseudo-labels for each class, without considering the varying learning conditions and difficulty levels associated with different classes, resulting in a decline in the performance of global model, especially under non-Independent and Identically Distributed (Non-IID) settings. To this end, we propose FedTMatch, a learning framework which adjusts the threshold for each class based on the learning status of each client. Furthermore, we introduce a self-supervised rotation loss in unlabeled clients to increase the utilization of raw data and correct the feature space formed by pseudo-label data to weaken the impact of Non-IID data distribution on global model's performance. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.

关键词： Data privacy Control engineering Service robots Computational modeling Semisupervised learning Predictive models Filtering algorithms Data models Noise measurement Convergence

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Trinary Relationship Interaction Detection

Trinary Relationship Interaction Detection

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Fangqun Gao YunTao Wu Yanduo Zhang Tao Lu Pan Tang Liwei Wang Jifeng Han Wanghui Wu Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei University of Arts and Science Xiangyang China State Key Laboratory of Power Grid Environmental Protection China Electric Power Research Institute Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

In various specialized operational scenarios, particularly those involving tool usage, the capacity to detect relationships between entities is crucial for understanding complex interactions. Traditional methods, which typically focus on unary or binary relationship detections, often fall short in capturing the subtleties present in such environments. this paper introduces the HTOI (Human Tool Object Interaction) model, a novel and pioneering approach to trinary relationship detection that addresses these limitations. the HTOI model has been designed to excel in complex interaction scenarios, including those characterized by the use of tools, as evidenced by its exceptional performance on our TData dataset. this dataset specifically highlights the nuanced actions of electric utility workers during tool-assisted operations, underscoring the model’s ability to detect intricate interactions with higher accuracy and reliability compared to existing HOI (Human Object Interaction) techniques. Our study aims to showcase the HTOI model’s significant advancement in the field of relationship detection, offering a valuable tool for analyzing and interpreting complex operational interactions involving tools.

关键词： Analytical models Mechatronics Accuracy Power industry Reliability robotics and automation

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Parameters optimization design of high-speed reluctance coil launchers

Parameters optimization design of high-speed reluctance coil...

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Pulsed Power conference, international conference on High-Power Particle Beams and international Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： Yadong Zhang Ao Zhou Zhiqiang Sun State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and Automation Wuhan University Wuhan China Guangzhou GuoYao Technology Co. Ltd Guangzhou China

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

Muzzle speed and efficiency are two important factors that constrain the development of reluctance coil launchers. In order to improve the muzzle speed of the reluctance coil launcher, the number of stages of the launcher is bound to increase, which leads to the complication of the simulation model, long simulation time consuming and low optimisation efficiency. Aiming at the above problems, this paper proposes a fast optimisation method for high-speed reluctance coil launchers based on genetic algorithm and machine learning. Firstly, based on a 7-stage reluctance coil launcher, determine that the wire diameter and the number of coil layers are the optimization objects; based on machine learning, the wire diameter and the number of coil layers are the optimization objects. Based on machine learning, the fast calculation model is trained with the wire diameter and the number of coil layers as inputs and the muzzle speed as output; the fast calculation model is invoked with the genetic algorithm, and the maximum muzzle speed of the launcher is taken as the fitness function, so as to achieve the fast optimisation of the parameters of the high-speed reluctance coil launcher stage by stage. the results show that after optimisation, an muzzle speed of 113.59m/s can be achieved under the condition of 32-stage launcher with an efficiency of 9.96%.

关键词： Coils Training Particle beams Optimization methods Machine learning Predictive models Finite element analysis Wire Electromagnetics Genetic algorithms

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Comparison of Different Flux-Weakening Strategies of AC-Excited Hybrid Excitation Synchronous Motor

Comparison of Different Flux-Weakening Strategies of AC-Exci...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Y. W. Deng K. Wang J. Li T. Wang College of Automation Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

Compared with permanent magnet synchronous motor (PMSM), hybrid excitation synchronous motor (HESM) adds a set of electric excitation windings, so the controllable excitation current is introduced, which increases the control freedom of the system, and the magnetic field adjustment is convenient and reliable. It is suitable for aerospace, electric vehicles and other occasions. In this paper, a new type of AC-excited hybrid excitation synchronous motor (AC-EHESM) is studied. Firstly, according to the characteristics of the motor, the mathematical model of the motor is built, and the operation limits of the motor are analyzed. On this basis, three different flux-weakening control methods are compared and studied. these three control strategies can widen the operating area of the motor to a certain extent, and do not depend on the motor parameters, and can be applied to other HESM.

关键词： Simulation Windings Air gaps Synchronous motors Permanent magnet motors Mathematical models Hybrid power systems

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). this framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. the shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. this approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Enhancing Photovoltaic Array Performance under Partial Shading through Dynamic Reconfiguration and Layer Equalization Algorithm

Enhancing Photovoltaic Array Performance under Partial Shadi...

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international Carpathian Control conference (ICCC)

作者： Fatimah Ameen Attila Trohák Abdulrahman Siddiq Rabab Benotsmane Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary Dept. of Electronic and Control Eng. Technical College Northern Technical University Kirkuk Iraq

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Partial shading (PS) significantly impacts the performance of photovoltaic (PV) systems, leading to reduced efficiency. Traditional mitigation methods involve bypass diodes or PV module reconfiguration. Accordingly, the highest and lowest layer-based exchange (HLLBE) algorithm was proposed. the HLLBE system employs a Switching Block Array (SBA) for flexible reconfiguration without rewiring. By sorting and swapping irradiance readings, the HLLBE algorithm achieves uniform current distribution across PV array layers. Unlike static configurations, HLLBE dynamically adapts to shade patterns, offering an efficient solution. Simulations on a 3 × 3 PV array demonstrated HLLBE's effectiveness in enhancing output power and reducing multiple peaks compared to conventional total-cross-tied (TCT) configurations. Additionally, HLLBE minimizes operating switches through a switching matrix during dynamic reconfiguration (DR).

关键词： Photovoltaic systems Adaptation models Heuristic algorithms Switches Current distribution Control systems Solar system

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Design Method of Variable-Flux Machines for Improving Torque Density

Design Method of Variable-Flux Machines for Improving Torque...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Faliang Liu Mingqiao Wang Yong Liu Zaiping Zheng Ping Zheng Jie Fu School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Beijing Institute of Precision Mechatronics and Controls Beijing China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In order to improving the torque density of the variable-flux machine, design methods for both series and parallel configurations are proposed based on the theoretical analysis. After the traditional rare-earth permanent-magnet (PM) synchronous machines (PMSM) is converted into the VFM, two typical configurations of the VFMs are given. To determine what kinds of low-coercive-force (LCF) magnets possessing torque improving capability, the simplified equivalent magnetic circuits for both configurations are established. the torque improving method for the series-configuration VFM is deduced based on the magnetic circuit. the output capability of series-configuration VFM with different LCF magnets are analyzed, which validate the accuracy of the deduced method. then, by assuming the magnetic reluctance of the teeth and yoke is much smaller than other reluctances, the contour maps of air-gap flux density for the parallel-configuration VFMs are investigated. Based on the proposed methods, the torque density of the VFMs with series-, parallel- and hybrid-configuration can be improved.

关键词： Magnetic flux Torque Design methodology Simulation Air gaps Mathematical models Synchronous machines

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A Field-Weakening Scheme with Predictive Current Error for PMSM Modulated Model Predictive Control

A Field-Weakening Scheme with Predictive Current Error for P...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Qinghua Dong Yong Yu Bo Wang Minghe Tian Dianguo Xu School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In this paper, a field-weakening scheme on modulated model predictive control (M2PC) using predictive current error (PCE) margin is proposed for permanent magnet synchronous motor (PMSM). Compared with traditional finite-control-set MPC, M2PC has small steady-state current harmonics benefitting from the synthesis of multiple vectors. However, due to the limit of inverter output capacity and the discreteness of M2PC, the operation range is difficult to extend the field-weakening region and ensure dynamic performance. To address this problem, a predictive current error (PCE) margin with a weight factor is designed to improve the dynamic performance in field-weakening operation, in which the PCE margin is derived from the voltage mapping. the experimental results are eventually conducted for validating the proposed methods.

关键词： Predictive models Permanent magnet motors Harmonic analysis Permanent magnets Inverters Trajectory Steady-state

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Influence of Mutual Inductance on High-Frequency Impedance Characteristics of Six-Phase PMSM under Inter-Turn Short-Circuit Fault

Influence of Mutual Inductance on High-Frequency Impedance C...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Zihang Yuan Minghao Wang Jingang Bai Jiaxuan Huang Ping Zheng School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

To improve the ability to identify the inter-turn short-circuit (ITSC) fault, which is one of the most frequent fault types, of six-phase permanent-magnet synchronous machines (PMSMs), the high-frequency impedance of six-phase PMSM under ITSC fault is analyzed with consideration of the mutual inductance characteristics. the model of coupled magnetic circuit between the fault turns and other phases is established, interpreting the characteristic of mutual inductances with different fault turns locations. the high-frequency impedances of both the ITSC fault phase and healthy phases are analyzed, and the impedance decreases of healthy phase caused by mutual inductance are evaluated. Fault diagnosis indicator utilizing the high-frequency current response is designed and the indicator reliabilities of different voltage injection methods are compared.

关键词： Fault diagnosis Inductance Voltage Reliability engineering Mathematical models Circuit faults Impedance

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