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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68823 条记录，以下是1211-1220 订阅

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Research on automation Control of Intelligent Logistics Assistant 5

Research on Automation Control of Intelligent Logistics Assi...

引用

5th international conference on Machine Learning and computer Application, ICMLCA 2024

作者： Li, Ziwen Lin, Dun Deng, Kaitao Li, Weijia Li, Yezhao Feng, Libin College of Electronic Information Engineering Zhuhai College of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798331530334

This paper investigates an intelligent logistics assistant based on the YOLOv5 algorithm, using a three-wheel structure design with a master control system of STM32F407ZGT6. The task information is displayed through the LCD display, and the system is equipped with an MG996 robotic arm servo to control the clamping of the material while the servo of the material tray realizes the rotation of the material tray. To ensure the stability of the attitude, the system uses HW101 gyroscope for attitude correction, while the laser distance measurement module is responsible for the accurate calibration of the position. openMV vision camera runs the YOLOv5 algorithm to realize the real-time recognition and processing of the environment, and the RFID scanning module is used to obtain and identify the task code. For motion control, Emm _ V5.0closed-loop stepper motors drive three omnidirectional wheels for flexible movement, while a gimbal closed-loop stepper motor is used to regulate the rotation of the robotic arm. The lifting slide rail cooperates with the transmission belt to realize the lifting and lowering transmission of the robotic arm. Through the synergistic work of the above modules, the designed intelligent logistics robot has an efficient automated material handling capability. © 2024 IEEE.

关键词： Robotic arms

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Design and simulation of audio amplifiers based on amplification circuits

Design and simulation of audio amplifiers based on amplifica...

引用

2023 IEEE international conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Jie, Tan Nanxiang, Wang Ketong, Lin Ying, Wang Dalian University of Technology College of Electronics and Automation City Institute Dalian China Dalian University of Technology Engineering Practice Center City Institute Dalian China

ISBN: (纸本)9798350313734

This article introduce a circuit design method for audio signal two-way frequency division and power amplification, which utilizes amplifier circuits and active crossover networks. Two AD603 chips are used to achieve a gain of 46dB in the circuit and a common-collector amplification circuit is established to maximize the input impedance;High-pass and low-pass filter circuits are established using 2N3906 and 2N3904 chips, respectively. After software simulation testing, the design can achieve audio amplification function. © 2023 IEEE.

关键词： Timing circuits

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Detection of Grape Clusters in Images Using Convolutional Neural Network

Detection of Grape Clusters in Images Using Convolutional Ne...

引用

2023 international conference on Robotics and automation in Industry, ICRAI 2023

作者： Shahzad, Muhammad Osama Aqeel, Anas Bin Qureshi, Waqar Shahid National University of Sciences and Technology Department of Mechatronics Engineering Islamabad Pakistan School of Computer Science TU Dublin Dublin 7 Ireland

ISBN: (纸本)9781665464727

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully;many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don't address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes. © 2023 IEEE.

关键词： Object detection

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Global-Local Transformer for Unsupervised Person Re-Identification 4

Global-Local Transformer for Unsupervised Person Re-Identifi...

引用

4th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2023

作者： Xie, Meng-Si Chen, Si-Bao School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9781510666412

Unsupervised person re-identification (Re-ID) focuses on studying the issues of person Re-ID in the open world where there are no available annotated labels for pedestrian images. The original transformer only learns global features. It ignores local features and fails to consider high-level local information on images. Therefore, we introduce a transformer-based multi-branch unsupervised person Re-ID framework, named GLTReID, with learning more robust features of pedestrian images. Specifically, we split the original transformer into one global branch and two local branches, with learning global and local features in parallel. The global branch learns global features of pedestrian images, and two local branches learn local features of different parts of pedestrian images. Numerous experiments are conducted on several person Re-ID datasets, including CUHK03, Market-1501 and MSMT17. Numerous experiments denote that our method is better than most unsupervised learning methods and unsupervised domain adaptation methods in person Re-ID. © 2023 SPIE.

关键词： Learning systems

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Driver Anomalous Driving Behavior Detection Based on Supervised Contrastive Learning 4

Driver Anomalous Driving Behavior Detection Based on Supervi...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Li, Zhonglun Jin, Duan Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781510674448

In the context of vehicle operation, detecting and identifying abnormal driver behaviors is challenging due to the complex in-car environment, changing lighting, and varied driver postures. Our approach, utilizing supervised contrastive learning, categorizes driver behaviors as normal or abnormal. We employ depth images from the driver's front and above to address environmental complexities and improve accuracy. Our enhanced 3D-MobileNetV2 architecture achieves impressive results on the DAD dataset test set, with a 94.18% accuracy rate and a 0.962 AUC, validating the effectiveness of our method in driver anomaly detection. © 2024 SPIE.

关键词： Anomaly detection

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Non-Uniform Filter Banks-based Minimum Distance to Riemannian Mean Classification in Motor Imagery BCI 13

Non-Uniform Filter Banks-based Minimum Distance to Riemannia...

引用

13th IEEE international conference on CYBER technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Tan, Ping Su, Xiaomeng Yu, Lingli Shen, Yi School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business Changsha410205 China School of Automation Central South University Changsha410083 China

ISBN: (纸本)9798350315196

The motion intention in the motor imagery brain-computer interface is identified by classifying the event-related de-synchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the best sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidth signal by using multiple band-pass filters firstly;Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. These feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the best sensorimotor frequency band. During the test phase, the test signals are filtered by the bandpass filter of the best sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experimental results on the BCI competition IV 2a data sets show that the proposed method is superior to MDRM and FBCSP classification methods. © 2023 IEEE.

关键词： Bandwidth

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Exploring the Challenge of Controller Placement in Software-Defined Networking: A Comprehensive Review 2

Exploring the Challenge of Controller Placement in Software-...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Gunavathie, M.A. Jacophine Susmi, S. Sivasankari, S.S. Venkatesh, M. Information Technology Panimalar Engineering College Chennai India Information Technology University College of Engineering Tindivanam India Electronics and Communication Engineering Dayananda Sagar University Bangalore India Computer Science and Business Systems Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798350340235

The placement of controllers is a critical issue that can significantly impact network performance and efficiency in Software DefinedNetworking (SDN). The optimal placement of controllers should take into account various factors such as network topology, resource requirements, and potential failures. For the purpose of determining the optimal placement of controllers, various approaches have been proposed in the literature, including mathematical optimization models, game theory, and simulation-based methods. These approaches aim to minimize latency, maximize network capacity, and improve network reliability in the face of failures. Overall, the optimal placement of controllers in SDNs remains an active area of research, and there is a need for continued exploration of new and innovative approaches to this problem. By taking into account the various factors that influence network performance and efficiency, researchers can continue to develop solutions that improve the design and operation of SDN networks. © 2023 IEEE.

关键词： Controller placement Software Defined Networking

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Numerical study of two microwave antennas dedicated to superficial cancer hyperthermia

Numerical study of two microwave antennas dedicated to super...

引用

2023 international conference on ENTERprise Information Systems, CENTERIS 2023 - international conference on Project MANagement, ProjMAN 2023 - international conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Benameur, Narjes Awadi, Rania Bouabidi, Ammar Mohammed, Mazin Abed Rehman, Masood Ur Ounalli, Leila University of Tunis El Manar Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technology Tunis Tunisia Tunisian Centre for Nuclear Sciences and Technology Technopark Sidi Thabet Ariana2020 Tunisia Faculty of Sciences of Tunis Technopark Sidi Thabet Ariana2020 Tunisia College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Energy and Matter Research Laboratory LR16CNSTN02

Microwave hyperthermia is an effective non-ionizing heating method for tumor sensitization. Therapy planning is necessary to achieve optimal hyperthermia treatment. This study aims to simulate two microwave antennas with different geometrical aperture's, used in hyperthermia, to choose the one giving better heating performances. The analysis of both the antennas provides a maximum temperature of 44.2°C and 41.9°C for the antenna with parallel plates and the trapezoidal antenna. Additionally, the Bland-Altman analysis shows an agreement between thermal simulations and measured temperatures with an average difference of 0.11°C and - 0.06 °C for parallel and trapezoidal antennas. © 2024 The Author(s).

关键词： Hyperthermia therapy

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

引用

7th IEEE international conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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A Generative Adversarial Model for Weather Radar Echo Extrapolation based on Cycle Generation Structure

A Generative Adversarial Model for Weather Radar Echo Extrap...

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2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Ma, Jianhao Liu, Qi Zhang, Yonghong Liu, Xiaodong School of Software Nanjing University of Information Science and Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing Edinburgh Napier University EdinburghER10 5DT United Kingdom

ISBN: (纸本)9798350304602

Precipitation forecasting is crucial given that extreme precipitation occurrences have a significant influence on human civilization. Radar echo extrapolation is a method for forecasting echo maps in the future based on observations of past echo maps, and it can forecast approaching short-term weather events. With moderate effectiveness, deep learning-based models have recently been used for radar echo extrapolation. However, high intensity radar echo values fail to be accurately predicted. In this paper, a novel generative adversarial model for weather radar echo extrapolation is proposed, which is composed of dual criss-cross transposed attention mechanism and cycle generation units. The findings show that the suggested model commonly performs better when in comparison to existing deep learning models. © 2023 IEEE.

关键词： Meteorological radar

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