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Psychological Assessment Data Analysis Based on Particle Swarm Algorithm

Psychological Assessment Data Analysis Based on Particle Swa...

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Chen, Yuxi Wang, Houbin Zhang, Zanglong Ren, Yunying Deng, Zetao Tu, Jinxia Sino-German College of Intelligent Manufacturing Shenzhen Technology University Guangdong Shenzhen China School of Resources and Environmental Engineering Ludong University Shandong Yantai China Shandong University of Traditional Chinese Medicine Faculty of Intelligence and Information Engineering Shandong Jinan China School of Computer Engineering Chongqing College of Humanities Science & Technology Chongqing China Modern Industry School of Virtual Reality of Jufe Jiangxi University of Finance and Economics Jiangxi Nanchang China School of Business Chongqing College of Humanities Science & Technology Chongqing China

ISBN: (纸本)9798350329773

Research has found a close connection between a mother's mental health and an infant's physiological, cognitive, emotional, and social development. This paper delves into the impact of maternal physical and psychological health on infant growth using various academic research methods, providing theoretical guidance for promoting infant development. Firstly, a canonical correlation analysis is employed to explore the specific relationship between maternal physical and psychological indicators and infant behavioral characteristics and sleep quality. The conclusion is that maternal psychological indicators largely account for variations in infant sleep quality and behavioral characteristics. Secondly, a multivariate linear regression model, combining stepwise regression and ridge regression, is used to analyze the relationship between maternal and infant indicators and make predictions. The result indicates that the 391st infant is of the contradictory type, among others. Subsequently, an objective planning model incorporating particle swarm optimization algorithm reveals that the behavioral characteristics of the 238th infant can shift from contradictory to moderate type, requiring a treatment cost of $9577.01. Finally, based on principal component analysis, the 391st infant's sleep quality is assessed as moderate. © 2023 IEEE.

关键词： Principal component analysis

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Latent 3D Scene Graph with Aligned Visual-Language Perception for Object-Goal Navigation 3

Latent 3D Scene Graph with Aligned Visual-Language Perceptio...

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3rd International Conference on Mechatronics, control and Robotics, ICMCR 2025

作者： Zhang, Jianwei Zhou, Kang Yang, Junjie Li, Xiaojian College of Engineering Southern University of Science and Technology Shen Zhen China School of Computer Science City University of Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shen Zhen China

ISBN: (纸本)9798331544546

Object-goal navigation requires agents to accurately locate and navigate to specified target objects in complex indoor environments. Existing methods primarily rely on single visual observations or simple semantic matching, making it difficult to effectively understand 3D scene relationships between target objects, which leads to inefficient path planning during navigation. Furthermore, the feature discrepancy between visual and language modalities poses challenges for accurate target identification. To address these issues, we propose Latent 3D Scene Graph with Aligned Visual-Language Perception (LSGAP), a novel approach that explicitly models 3D scene relationships between objects through depth perception and latent 3D scene graph construction. Meanwhile, we leverage multimodal pretraining strategies to achieve effective visual-language feature alignment, enhancing the model's target recognition capabilities. This end-to-end architectural design improves spatial cognition ability and enables more efficient navigation decision-making. We conduct extensive experimental evaluations in the AI2THOR interactive environment to validate the effectiveness of our navigator. © 2025 IEEE.

关键词： Depth perception

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An Enhanced control Strategy for Standalone Brushless Doubly Fed Induction Generator Under Unbalanced Load Using Reactive Power Loop 1

An Enhanced Control Strategy for Standalone Brushless Doubly...

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1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Elbabo Mohammed, Omer Mohammed Liu, Yi Xu, Wei Islam, Md. Rabiul University of Sinnar Faculty of Engineering Department of Electrical Engineering Sinnar Sudan Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan China University of Wollongong School of Electrical Computer and Telecommunications Engineering Wollongong Australia

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

Without brushes and slip rings, the brushless double-fed induction generator (BDFIG) can operate more stably and reliably, so it is prospective to be one of important machines for standalone systems in the next few years. The stability of amplitude of voltage and frequency of power winding (PW) is major aim of control in off-grid system. Also, the standalone system is oversensitive to abnormal running situations, especially with unbalanced conditions. The unbalanced loads would lead to harmful phenomena, such as the unbalanced impact of current and voltage of PW. To handle this issue, the reactive power loop is presented to minimize voltage unbalance of PW through creating control winding (CW) compensation voltage via converter of machine side (MSC). The simulation is executed to test the performance of presented method. © 2022 IEEE.

关键词： Reactive power

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy Systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

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arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

arXiv

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna

Application of Evolutionary Computing for Optimal Design of ...

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IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Naveed Hamid Nawaz Khan Botao Feng Xiao Ding Li Deng Wenzhe Gu Kwok L. Chung College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering Huizhou University Huizhou P. R. China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S 11 | parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements.

关键词： Wireless communication Antenna measurements Base stations Transmitting antennas Interference Bandwidth Reliability

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Cooperative Global K-exponential Tracking control of Multiple Mobile Robots-Extended Version

arXiv

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arXiv 2024年

作者： Xu, Liang Su, Youfeng Cai, He College of Computer and Data Science Fuzhou University Fuzhou350116 China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally K-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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PDDM: IP Image Design via Diffusion Model

PDDM: IP Image Design via Diffusion Model

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Data science and computer Application (ICDSCA), IEEE International Conference on

作者： Mingbo Yao Haiyi Wang Shuxiang Yang School of Mathematics and Statistics Shandong University of Technology Zibo China College of Computer and Control Engineering Northeast Forestry University Harbin China Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Traditional methods and models, while capable of generating high-quality images, often require substantial human resources and time, limiting their feasibility for large-scale applications. To address these challenges, this paper proposes the Pose-Directed Diffusion Model. Through optimized training of this module, the model in this paper significantly reduces training time. Additionally, the model in this paper integrates the Pro-controlNet module to enhance fine control during the image generation process. Importantly, this paper constructs a new CCC Image Collection dataset, featuring precise classifications, detailed annotations, and high-resolution images. The experimental results on the CCC Image Collection dataset demonstrate that our model performs excellently in terms of evaluation metrics such as MSE, PSNR, and SSIM. It shows significant improvement in training effectiveness on the original dataset, with outstanding performance both in terms of absolute error measurement and visual perception by the human eye.

关键词： Training Adaptation models Visualization Accuracy Image synthesis Computational modeling Process control Diffusion models Data models IP networks

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