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共 60728 条记录，以下是151-160 订阅

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Manipulator System Hand-Eye and Tool Matrix Joint Calibration Algorithm Based on Error Analysis Model

Manipulator System Hand-Eye and Tool Matrix Joint Calibratio...

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2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Zhang, Xuegang Li, Jiaxiang Yuan, Kang Liu, Hao Shanghai University School of Mechanical Engineering and Automation Shanghai China Electronics Technology Co. Ltd. Anhui Hefei China Hefei LCFC Information Technology Co. Ltd. Anhui Hefei China Anhui JianZhu University School of mechanical and electrical engineering Anhui Hefei China University of Science and Technology of China School of Information Science and Technology Anhui Hefei China

ISBN: (纸本)9798331506797

Vision guidance systems and end tool systems, like eyes and hands, are crucial for enhancing the capabilities of manipulators and helping performing various tasks flexibly. Corresponding, calibration is the prerequisite of the vision and tool system operation. In the paper, a novel vision system hand-eye matrix and end tool system tool matrix joint calibration algorithm based on error analysis model is proposed. The joint calibration algorithm overcomes the error accumulation and transmission amplification in popular separated calibration method, and error analysis model in calibration improves the accuracy efficiently. The experiments and practical applications presented in the paper show the joint calibration algorithm has outstanding performance in high accuracy requirements application scenarios. © 2025 ieee.

关键词： Error Analysis Model Hand-Eye Calibration Linear Optimization Tool Frame Calibration

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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26th International conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 ieee.

关键词： Frequency domain analysis

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Non-Invasive EEG-Driven Brain-Computer Interface for Motion Control of Assistive Robotics

Non-Invasive EEG-Driven Brain-Computer Interface for Motion ...

引用

2025 International conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Fatima, Syeda Eman Rahman, Syed Arsal Mumtaz, Wajid Nazeer, Muhammad Shayan School of Electrical Engineering and Computer Science National University of Sciences and Technology IslamabadH-12 Pakistan

ISBN: (纸本)9798331529604

This study aims to develop a practical proof of implementing a deep learning-based brain-computer interface (BCI) for motion intent classification and control of an assistive robotic arm. A modified version of EEGNet with attention is used for the real-time classification of EEG signals recorded using the Emotiv Epoc X headset. The robotic arm is built on an ESP32-WIFI Arduino. The motor imagery dataset by Physionet used for training consists of four classes out of which two have been used to map right and left arm movements. The data goes through necessary preprocessing steps: down-sampling, Butterworth filter, truncation, and z-score normalization. The modified EEGNet achieves an 82.01% validation accuracy on the dataset. The system has been tested for real-time usability by recordings of 3 seconds at 128Hz with minimum latency. © 2025 ieee.

关键词： Butterworth filters

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Integrated Hydroponics automation System Utilizing IoT for Enhanced Plant Growth 3

Integrated Hydroponics Automation System Utilizing IoT for E...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nilahni, N. Sivasakthi, P. Kavitha, P. Madhumathi, S.M. Sakthi Lakshmi Priya, C. Saranyavaishalini, V.G. Department of Computer Science and Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798331527549

Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant advantages such as higher yield, reduced water usage, and efficient resource management compared to traditional farming. However, existing hydroponic systems often require separate monitoring and control platforms for different setups, such as Wick and Drip systems, increasing complexity and cost. This paper proposes a Combined Hydroponics automation System utilizing IoT, integrating both Wick and Drip systems into a single, unified platform. The system employs Nodemcu ESP8266 to monitor key parameters such as pH, humidity, moisture, and water levels, with data transmitted to a cloud platform for real-time access and control. The unified approach reduces development and operational costs while simplifying the monitoring process. The outcome demonstrates improved efficiency in plant management, with streamlined data visualization, remote control, and optimized resource allocation, making it a cost-effective solution for urban and resource-limited farming environments. © 2025 ieee.

关键词： Hydroponics

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MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection

MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregat...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zehao Yue, Haobo Zhang, Zhicheng Mu, Da Tang, Jin Yin, Jianqin School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

Sound Event Detection (SED) plays a vital role in comprehending and perceiving acoustic scenes. Previous methods have demonstrated impressive capabilities. However, they are deficient in learning features of complex scenes from heterogeneous dataset. In this paper, we introduce a novel dual-branch architecture named Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection (MTDA-HSED). The MTDA-HSED architecture employs the Mutual-Assistance Audio Adapter (M3A) to effectively tackle the multi-scenario problem and uses the Dual-Branch Mid-Fusion (DBMF) module to tackle the multi-granularity problem. Specifically, M3A is integrated into the BEATs block as an adapter to improve the BEATs' performance by fine-tuning it on the multi-scenario dataset. The DBMF module connects BEATs and CNN branches, which facilitates the deep fusion of information from the BEATs and the CNN branches. Experimental results show that the proposed methods exceed the baseline of mpAUC by 5% on the DESED and MAESTRO Real datasets. © 2025 ieee.

关键词： adapter aggregate sound event detection

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Subterranean Vegetable Sorting with YOLO Models and Flask Framework

Subterranean Vegetable Sorting with YOLO Models and Flask Fr...

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2025 International conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Rakesh, M.D. Ronitha, R. Rudraswamy, S.B. JSS Science and Technology University Dept. of Electronics and Communication Engineering SJCE Mysuru India JSS Science and Technology University Dept. of Electronics and Communication Engineering Mysuru India

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529604

The integration of advanced deep learning models, such as YOLOv5 and YOLOv8, into carrot cultivation significantly enhances post-harvest sorting systems, allowing for the effective differentiation between healthy and rotten carrots. This work streamlines the sorting process, ensuring that only high-quality produce reaches the market, thereby providing substantial advantages for traders. By automating this procedure, farmers can improve food safety, monitor shelf life, and enhance customer satisfaction, ultimately increasing productivity and profitability. The system employs convolutional neural networks (CNNs) to analyze visual data captured during harvest or post-harvest stages. These models are trained to identify distinguishing features of carrots, such as color changes, texture anomalies, and shape deformations, resulting in precision rates of 98.3% and 96.4% in identifying freshness with YOLOv8 and YOLOv5 respectively, and 96.5% and 93.6% in detecting rotten. Extensive training and evaluation have demonstrated notable improvements in performance metrics, validating the effectiveness of these models. As a prototype, we have developed a Flask web application for detecting whether carrots are rotten or fresh. This system significantly enhances productivity by allowing for quick and accurate assessment of carrot quality. The adoption of this AI-driven technology marks a transformative step toward optimizing resource management and maximizing crop value in modern agriculture. © 2025 ieee.

关键词： Vegetables

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FractureAI Revolutionizing Bone Fracture Detection with Deep Learning CNNs for Precision Medical Imaging 3

FractureAI Revolutionizing Bone Fracture Detection with Deep...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Sudha, I. Ramesh, P.S. Durgadevi, P. Sundari, G. Senthilvadivu, S. Narang, Sudha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats University Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse TamilNadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani campus Chennai-26 India C. Abdul Hakeem College of Engineering and Technology Department of Cse India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering TamilNadu Chennai India Maharaja Agrasen Institute of Technology Sector 22 Delhi Rohini India

ISBN: (纸本)9798331542375

Detection of bone fractures is part and parcel of clinical diagnosis, previously relied on radiological interpretation of X-ray or CT scans. However, manually reviewed system involves human intervention and it is vulnerable to reveal more false negatives or delayed at revealing certain fractures. To counter these challenges, this research proposes Fracture AI, a deep learning system that employs CNNs, specifically ResNet-50 for fracture identification and categorization. With the help of transfer learning and techniques such as Grad-CAM for fracture localization, the system can reach remarkable results with the accuracy of 96.2%, precision of a 97.9%, recall of a 96.9%, and the F1-score of a 97.4%. The results presented here show how Fracture Ai can enhance the diagnostic process enabling the identification of fractures in a shorter time, especially in emergency departments, has been clearly shown here and the AI solution is accurate. © 2025 ieee.

关键词： Mammography

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Balancing Environmental Sustainability and Technological Innovation in 6G Wireless Technology 1

Balancing Environmental Sustainability and Technological Inn...

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1st International conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prathima Mabel, J. Vani, K.A. Ambika, P.R. Bhat, Archana Chikkaraddi, Laxmi M Vishwakarma, Pramatma Dayananda Sagar College of Engineering Department of Information Science and Engineering Bangalore560078 India City Engineering College Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India Lovely Professional University Department of Computer Aplication Punjab 144411 India

ISBN: (纸本)9798331518578

Unprecedented improvements in connectivity, speed, and latency are anticipated with the emergence of 6G wireless technology, opening the door for game-changing applications like AI-driven automation, smart cities, and holographic communications. Nevertheless, there are serious environmental consequences associated with these breakthroughs, such as resource destruction, elevated energy use, and electrical waste. It takes a comprehensive strategy that incorporates energy-efficient hardware, sustainable software, and green design concepts to strike a balance between technological innovation and environmental sustainability in 6G. Low-power network topologies, energy-harvesting technology, and the use of renewable energy sources are examples of emerging tactics. Furthermore, reducing the environmental impact of 6G architecture requires the adoption of circular economy principles like recycling devices and using eco-friendly materials. To ensure sustainable development, industry rules, policy structures, and cross-sector partnerships will be essential. In order to provide understanding of design considerations, problems, and future perspectives for a greener wireless future, this study examines the combined quest of technological excellence and environmental responsibility in 6G. © 2025 ieee.

关键词： Green development

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Predictive Analytics and automation in Supply Chain Management with Internet of Things (IoT) 3

Predictive Analytics and Automation in Supply Chain Manageme...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Yesodha, Karthik Kalyan Raj Kumar Rajendran, Prabhakaran Bhalerao, Manoj Gupta, Ketan Yuvaraj, N. Supply Chain and Manufacturing Caterpillar Inc BloomingtonIL United States Cscs Llc CummingGA United States Hub Group Oak BrookIL United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

Predictive analytics and automation with the Internet of Things (IoT) is a growing trend in supply chain management. The generation permits cost-efficient operations and organizations to reveal and control their supply chain systems. The combination of predictive analytics algorithms and automatic structures can offer insights into supply chain operations, expect adjustments in customer demands, and allow agencies to plan better and put things together for the future. IoT can also be used to screen supply chain techniques, become aware of regions for development, and proactively manipulate potential disruptions. U sing IoT -based analytics and automation, advantages that include increased visibility in supply chain operations, elevated customer satisfaction, and cost-effective supply chain processes may be achieved. Furthermore, predictive analytics and automation can help improve choice-making with actual-time records insights and ensure everyone's tactics are optimized for efficient performance. © 2025 ieee.

关键词： Supply chain management

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 ieee.

关键词： Authentication Protocol

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