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Design of Intelligent control System for Wheeled Drug Spraying Robot 3

Design of Intelligent Control System for Wheeled Drug Sprayi...

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3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Xu, Shenglong Cui, Yuping Wuwei Vocational College Gansu Province Wuwei China

ISBN: (纸本)9798350331578

Facing the problems of low manual efficiency and great harm to human body in the process of pesticide spraying, this paper puts forward a remote control solution ofof wheeled drug spraying robot, and designs the corresponding intelligent control system. The system takes Siemens S7-1200 PLC as the core controller, takes the touch screen as the man-machine interface, and combines the multi-sensor system to complete the allocation and control of the motor drive unit, so as to realize the functions of advancing, retreating, steering, speed regulating of the wheeled drug spraying robot. In addition, according to the experimental test results, PLC can realize the coordinated control of the drug spraying robot, and the whole system runs smoothly, responds quickly, and its operability and flexibility meet the design requirements, which provides a useful technical reference for realizing the automatic and intelligent operation of pesticide spraying. © 2023 IEEE.

关键词： control systems

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

ISBN: (纸本)9789819784639

The proceedings contain 50 papers. The special focus in this conference is on Intelligent Computing Techniques For Smart Energy Systems. The topics include: Analysis of Fault and Current-Limiting Methods in the Power System;UDS for Data Communication in Temperature Monitoring;approximation in the Weighted Lipschitz Class by (C,α,θ)(E,q) Mean of Its Fourier Series;demand Response and Load Profile Analysis in Modern Electricity Systems;First-Principles Study of the Structural, Optical and Electronic Properties of Zinc Blende GaN Through PBEsol and TB-mBJ Functionals;optimization-Based Cost Analysis for Energy Management System in Microgrids;classification and Prediction of Knee Osteoarthritis by Deep Learning Approach;Predicting Stock Price and Market Direction Using Statistical and LSTM Models;Comprehensive Evaluation of Time Management with Balancing Resistors in Passive Cell Balancing Using the 1RC Model;PAWnnect: Pioneering IoT-ML Driven Pet Monitoring;transactive Energy Management for Microgrids: Peer-To-Peer Trading Using Stackelberg Game Approach;Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using Machine Learning and Deep Learning;unlocking IoT Security: Enabling the Future with Lightweight Cryptographic Ciphers;Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET;flower Patterned with Elliptical Petals Multiband Microstrip Fractal Patch Antenna for Wireless Applications;Optimal Scheduling of Solar PV Generators and Network Assets in Distribution Network;revolutionizing Biomedical Engineering with Microcontroller Applications: A Comprehensive Review;investigation of Battery Temperature Impact on High Voltage Cable of Electric Vehicles;deep Learning Technique for computer vision-Based Pose Estimation for Augmented Reality;Rectangular Shape Patch Antenna with Defected Ground Plane for 5G Sub 6 GHz Wireless Communication Systems.

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The Design of Robotic Arm Based on ROS 3

The Design of Robotic Arm Based on ROS

引用

3rd international conference on computer, control and Robotics, ICCCR 2023

作者： Zhu, Mingyi Lv, Xiaolong Zuo, Siyang Zhang, Xinyu College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Undergraduate School Harbin Engineering University Harbin China

ISBN: (纸本)9781665492126

In this paper, the design of a ROS-based robotic arm is introduced from three aspects: structure, hardware and software. In order to reduce the mass of the entire robotic arm, the connecting rod of the robot arm is mainly made of carbon fiber material. Different loads lead to a decline in the control accuracy of the joint angle of the robotic arm. To solve this problem, we added a gravity compensation system to the control system of the robotic arm. In addition, we designed a software framework that can be widely used in various robotic arm systems. Finally, we carried out a test experiment on the robotic arm. © 2023 IEEE.

关键词： control systems

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BiLSTM based approach for Energy Demand Forecasting and Synchronization among Smart Grids 3

BiLSTM based approach for Energy Demand Forecasting and Sync...

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3rd IEEE international conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Sakib, Mohd Mustajab, Suhel Anwar, Khalid Alam, Mahfooz Aligarh Muslim University Department of Computer Science Aligarh India School of Camp. Sci. Engg. & Technology Bennett University Greater Noida India

ISBN: (纸本)9798350387193

Recent growth in industrialization and urbanization has increased pollution. Air pollution is one of the major concerns of this era. The main cause of air pollution is the use of fuel-based vehicles. Electric vehicles (EVs) are being used to reduce air pollution which has increased the demand for electric energy and created chaos. This study presents an innovative approach that enhances the synchronization among the various Stockholders in the smart grid (SG) system. The model is advantageous for several parties involved in the smart grid, such as power generation companies, owners of hybrid and electric vehicles (EVs), and electric vehicle charging stations. Charging stations serve as crucial intermediaries, ensuring a balance between demand and supply. A Bidirectional Long Short-Term Memory (BiLSTM) network is at the center of this approach, with the objective to correctly predict the consumption of energy. In order to improve smart grid synchronization, our research utilizes data from PJM Interconnection LLC, a regional transmission organization. The paper compares the performance of various forecasting models on different evaluation metrics like MAE, MSE, RMSE, and MAPE. We got an MAE score of 26.15 and an RMSE score of 35.15, which are comparatively less than other models. The results show that the BiLSTM model is more effective at making predictions than other methods. This study not only contributes to the field of energy forecasting but also paves the way for more efficient and sustainable energy management in smart grid systems. © 2024 IEEE.

关键词： Charging stations

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Lane detection method based on row and column guidance 3

Lane detection method based on row and column guidance

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3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： He, Zhonghe Gong, Pengfei Ye, Hongcheng School of Electrical and Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9781510681880

The lane detection algorithm based on deep learning classifies according to different lane representation forms. Among them, the lane detection algorithm based on row classification uses the prior characteristics of lane distribution to realize fast and accurate lane detection. With the development of technology, the recall rate and detection ability of row classification lane detection algorithm in complex urban scenes are slightly insufficient. This paper reconstructs the lane line detection method based on row classification, and detects the exact position of lane lines by calculating the positive sample grid of lane lines, so as to replace the expectation-based lane line location calculation method;Secondly, combining the prior characteristics of lane lines, the column guidance matrix is designed to solve the problem of insufficient constraints caused by the sparse distribution of lane lines between different anchors at the edge of the image. At the same time, the global information of the guidance matrix is used to improve the lane detection ability of the model in the complex urban environment. In this paper, CULane and TuSimple benchmark data sets were used for verification, and the F1 reached 77.9% and 97.2% respectively, which realized the fast and accurate detection of lane lines in complex environments. © 2024 SPIE.

关键词： Matrix algebra

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Video Captioning Using Large Language Models 3

Video Captioning Using Large Language Models

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Malaviya, Priyanshu Patel, Dhruvit Bharti, Santosh Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat Gandhinagar382007 India

ISBN: (纸本)9798350381931

In this research, we delve into the intricate domain of video captioning, a pivotal aspect of multimedia content analysis, by harnessing the synergistic potential of convolutional and recurrent neural networks. Our study meticulously integrates a suite of models, including EfficientNet-LSTM, InceptionV3-LSTM, VGG16-LSTM, and ResNet50-LSTM, capitalizing on the LSTM's prowess in processing temporal data and CNN's efficiency in feature extraction. These models are instrumental in interpreting visual inputs and translating them into coherent textual descriptions. We further explore the BLIP model's effectiveness in bridging the gap between visual perception and language interpretation by finetuning it, thereby enriching our video captioning methodology. A novel experiment in our study involves the fusion of ResNet50 with GPT2, aiming to create a robust framework that marries advanced image recognition with sophisticated language processing capabilities. This hybrid approach is anticipated to enhance the accuracy and context-awareness of caption generation. Additionally, we introduce an Attention-Based CNN+LSTM model, designed to refine the focus on salient aspects in videos, thereby ensuring the generation of more precise and contextually relevant captions. This approach addresses the challenge of varying significance across successive frames, ensuring that the captions accurately reflect the most critical content of the video. Our comprehensive analysis and innovative methodologies contribute significantly to the advancement of video captioning techniques, offering new perspectives and solutions in the intersection of computer vision and natural language processing. © 2024 IEEE.

关键词： Image recognition

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Unleashing Vanilla vision Transformer with Masked Image Modeling for Object Detection

Unleashing Vanilla Vision Transformer with Masked Image Mode...

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IEEE/CVF international conference on computer vision (ICCV)

作者： Fang, Yuxin Yang, Shusheng Wang, Shijie Ge, Yixiao Shan, Ying Wang, Xinggang Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China Tencent AI Lab Shenzhen Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350307184

We present an approach to efficiently and effectively adapt a masked image modeling (MIM) pre-trained vanilla vision Transformer (ViT) for object detection, which is based on our two novel observations: (i) A MIM pre-trained vanilla ViT encoder can work surprisingly well in the challenging object-level recognition scenario even with randomly sampled partial observations, e.g., only 25% similar to 50% of the input embeddings. (ii) In order to construct multi-scale representations for object detection from single-scale ViT, a randomly initialized compact convolutional stem supplants the pre-trained patchify stem, and its intermediate features can naturally serve as the higher resolution inputs of a feature pyramid network without further upsampling or other manipulations. While the pre-trained ViT is only regarded as the 3rd-stage of our detector's backbone instead of the whole feature extractor. This naturally results in a ConvNet-ViT hybrid architecture. The proposed detector, named MIMDET, enables a MIM pre-trained vanilla ViT to outperform leading hierarchical architectures such as Swin Transformer, MViTv2 and ConvNeXt on COCO object detection & instance segmentation, and achieves better results compared with the previous best adapted vanilla ViT detector using a more modest fine-tuning recipe while converging 2.8x faster. Code and pre-trained models are available at https://***/hustvl/MIMDet.

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Evaluation Algorithm of Urban Road Sanitation Quality Based on Machine vision 3

Evaluation Algorithm of Urban Road Sanitation Quality Based ...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Wang, Zhenyu Feng, Jiangwei Feng, Xiangsheng Liu, Xingyun Zhao, Xiangchao Nanchang Institute of Technology Nit School of Information Engineering Nanchang China

ISBN: (纸本)9781665459112

Aiming at the complexity and subjectivity of the evaluation process of urban road sanitation quality, an intelligent evaluation method of urban road sanitation quality based on machine vision is proposed. Firstly, YOLO v3 model is combined with DeepSORT algorithm to realize garbage classification and counting through the combination of target detection and target tracking. Compared with manual evaluation, the work efficiency has been effectively improved. Secondly, the distortion correction-angle of view transformation algorithm and multi-YOLO-proportional area algorithm are used to to calculate the road area and the distribution density of garbage. Finally, the intelligent evaluation of urban road sanitation quality is realized by taking the density of garbage as the measurement index and combining with the evaluation standard. In order to verify the performance of this algorithm, the data set of Nanchang urban roads is tested. The experimental results show that this algorithm simplifies the evaluation process and makes the objective evaluation more accurate. © 2022 IEEE.

关键词： Target tracking

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Using CNN and OpenCV, Emotion Recognition with Facial Feature Approach 3

Using CNN and OpenCV, Emotion Recognition with Facial Featur...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Kumar, Ashwini Thacker, Chintan Arka Jain University Department of Engg. & It Jharkhand Jamshedpur India Parul University Piet Department of Cse Gujarat Vadodara India

ISBN: (纸本)9798350399264

In the field of human-computer interaction, continuing research on the detection of emotions through the identification of face features may be found (HCI). Humans are capable of communicating a diverse range of feelings and emotions by the expressions on their faces and the way they carry themselves physically. In this project, we will utilize a technique that permits the computers to self-identify the face acknowledgment of person feelings. This will be done so that we can determine the person's true feelings from their facial expression and gesture. Sentiment Analysis is only a composite of data gathered from several types inside the end. The existing divide between computer systems and humans will start to close as soon as computers can recognise more human emotions. In this study paper, we will demonstrate how to interpret the frontface expression of a person in order to accurately detect a range of emotions, including neutral, pleased, sad, shocked, angry, fearful, and contemptuous expressions. © 2023 IEEE.

关键词： computer vision

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