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Tomato Ripening Stage Prediction Using a Cascade Model of YOLOv8 and ViT

Tomato Ripening Stage Prediction Using a Cascade Model of YO...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Pradeep Nahak Jyotirmay Singh Dilip Kumar Pratihar Alok Kanti Deb Department of Mechanical Engineering Indian Institute of Technology Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Precise fruit or vegetable maturity assessment is crucial in agriculture to optimize harvest time and product quality. However, this process faces several challenges. Traditionally, maturity prediction was done visually, which is both time-consuming and labour-intensive. Therefore, there is a need of advanced methods for ripeness prediction using computer vision and deep learning techniques while considering environmental factors. This study shows a cascade model that combines the YOLOv8 (You Only Look Once Version-8) object detection algorithm and vision Transformer (ViT) classification algorithm architectures to measure tomato maturity accurately. The cascade model first uses YOLOv8n to locate and detect tomatoes within the images and then uses ViT to classify the fine-grained maturity stage. The model was trained, validated and tested on a dataset of 1460 images which consisted of mature, immature and partially mature classes. The cascade approach shows promising performance, achieving 93.1% accuracy in tomato maturity assessment. This is a significant improvement over the YOLOv8x classification model, which scored 87.93% accuracy on the same dataset. The proposed cascade model enables automated assessment of tomato maturity, which could be highly beneficial for enhancing performance in optimizing crop harvesting and minimizing post-harvest losses.

关键词： YOLO computer vision Accuracy Computational modeling Crops Predictive models Transformers Real-time systems Classification algorithms Quality assessment

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Link Prediction in Social Networks Using Node Embeddings with Nature Inspired Approach 3

Link Prediction in Social Networks Using Node Embeddings wit...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Tiwari, Himanshu Kumar, Sanjay Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665438117

Social networks are important for tasks such as transmitting information, predicting the outcomes of an event, and predicting new relationships. The link prediction task involves the prediction of new relationships that may be created over time. More formally, suppose we have a social network's snapshot at the current time, we are interested in accurately predicting the edges that are not currently present but will be present after some time has elapsed. A new approach, Nature Inspired using CC-CD, has been proposed in this paper which makes use of node embeddings and closeness centrality. We have used the cosine distances of node embeddings as a proxy of distances and Closeness Centrality as a proxy of masses in Newton's Gravitational Law for prediction of new links. © 2021 IEEE.

关键词： Complex networks

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Fault Pattern Recognition Method of Rolling Bearing Based on MTF-2DWDCNN 3

Fault Pattern Recognition Method of Rolling Bearing Based on...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xun, Zhiwen Miao, Xiaodong Yu, Hu Gu, Yinji Nanjing Tech University School of Mechanical and Power Engineering Nanjing China Nanjing Tech University School of computer science and technology Nanjing China

ISBN: (纸本)9781665492812

2-D fault feature images have advantages as neural network input for fault pattern recognition, but the most deep image recognition algorithms are non-sensitive to the time-series features of images. This paper proposes a new algorithm named MTF-2DWDCNN based on Markov transfer field(MTF) jointly improved deep convolutional neural networks with wide first-layer Kernel(WDCNN). Firstly, the original 2-D sequence signal is converted into a 2-D time series image by MTF, used as the input of the neural network;secondly, the convolution mode of the original WDCNN is improved to adapt to 2-D time series;finally, the accuracy and error experiments are carried out to verify its effectiveness. Experimental results show that the method proposed in this paper has certain advantages over traditional algorithms regarding recognition accuracy. © 2022 IEEE.

关键词： Bearing deep learning fault diagnosis MTF

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Improved application of SCR flue gas denitrification control system in power plant

Improved application of SCR flue gas denitrification control...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Zhongyan Wang Jia Li Ruicai Si Chi Zhou Yang Song Xiucheng Li State Grid Corporation of China Jilin Province Electric Power Science Research Institute Co Ltd Changchun China Changchun Thermal Power Plant Changchun China Huaneng Jiutai Power Plant Changchun China

This paper introduces the automatic control system of flue gas denitrification in thermal power units, and makes many improvements on the traditional control strategy. The improved algorithm has the advantages of simple process, clear parameter setting, fast object identification, and makes full use of the dynamic relationship between oxygen concentration at denitrification inlet and NOx concentration at flue gas inlet. On the basis of traditional control, the problem that NOx concentration at flue gas outlet is difficult to be controlled due to large delay is solved. The actual operation OF the unit shows that the improved control strategy has strong robustness, achieves the control indexes, especially the environmental protection requirements, has strong universality and practicability, and meets the requirements of denitrification automation control.

关键词： Automation Heuristic algorithms Stability criteria Process control control systems Robustness Object recognition

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Semantic Segmentation Using HRNet with Deform-Conv for Feature Extraction Dependent on Object Shape 3

Semantic Segmentation Using HRNet with Deform-Conv for Featu...

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3rd international conference on Cybernetics and Intelligent Systems, ICORIS 2021

作者： Ando, Daiki Arai, Shuichi Graduate School of Integrative Science and EngineeringTokyo City University Tokyo Japan

ISBN: (纸本)9781665425803

In the field of Semantic Segmentation, High-Resolution Network (HRNet), which processes multiple resolutions in parallel, has dramatically improved the identification accuracy. However, there is a problem that the identification accuracy of objects with small area in the image is lower than that of other classes. We believe that this is due to the fact that the convolution with a fixed receptive field does not fully represent the features of object details. There are objects of various shapes and sizes in the actual image, and we believe that the receptive fields required should be different for each of them. Therefore, in this paper, we propose Deform-Conv HRNet, which uses Deformable Convolution with a deformable receptive field and enables feature extraction according to the shape and size of the object. Compared with the conventional method, the proposed method improves the discrimination accuracy by up to 3.9 pt. © 2021 IEEE.

关键词： computer vision

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Adaptive Quaternion Network with Contextual Feature Enhancement for Robust Single Image Deraining

Adaptive Quaternion Network with Contextual Feature Enhancem...

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Advances in Engineering Science and Technology (AEST), international conference on

作者： Shimaa Janabi Zainab S. Jameel Omar Talib Khazraji Electrical Engineering Department Mustansiriyah University Baghdad Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

Rain streaks can impair object detection and recognition in computer vision. Complex rain patterns challenge current deraining processes, resulting in poor performance. The Adaptive Quaternion Network with Contextual Feature Enhancement (AQN-CFE), a deep learning model, efficiently removes rain from single images. AQN-CFE captures spatial and color features using quaternion representation, and its contextual feature enhancement module preserves fine details in heavy rain images. On the Rain100H dataset, AQN-CFE outperformed previous approaches with a PSNR of 31.8 dB, SSIM of 0.91, and MAE of 3.5. PSNR increases by 2.3 dB, SSIM by 0.04, and MAE decreases by 15%. These results demonstrate that AQN-CFE improves visual clarity, making it appropriate for autonomous driving and video surveillance.

关键词： Deep learning computer vision Visualization Rain Adaptive systems Image color analysis Quaternions Object detection Video surveillance Image restoration

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28th international conference on Image Processing, computer vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

引用

28th international conference on Image Processing, computer vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on Image Processing, computer vision, and Pattern Recognition. The topics include: Image-Based Seal Recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based computer control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Optimizing Thermo-Electric Generation with Quasi-Z-Source Converter in Energy Harvesting for Wearable Devices 3

Optimizing Thermo-Electric Generation with Quasi-Z-Source Co...

引用

3rd international conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Lotfy, Mostafa Wageh Hussein, Aziza I. Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781643685649

Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capturing energy from different sources, such as thermal energy, and converting it into electrical power. Thermo-Electric Generators (TEGs) are commonly used in many applications such as wearable devices. However Thermo-Electric Generators produce very low output voltages, which is insufficient for most low-power electronic circuits to operate. Therefore, a boost converter is essential to step up the voltage output from the TEG to a usable level. In this paper, a DC-DC converter with a high performance in terms of voltage gain is implemented. The proposed quasi-Z-source (q-ZSC) DC-DC converter not only offers high voltage gain but also several significant advantages. The proposed q-ZSC is capable of both boosting and regulating the voltage output efficiently, even under variable input conditions, making it highly suitable for low-power energy harvesting systems such as Thermo-Electric Generators. The design is simulated using MATLAB Simulink software, focusing on integrated circuits, with a strong emphasis on optimizing the circuit's efficiency. This work underscores critical factors in improving energy harvesting circuits, enhancing their performance and effectiveness in low-power applications for wearable technology. © 2024 The Authors.

关键词： DC generator motors

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Whole-building Energy Co-simulation for Dynamic Occupant-based Heating and Cooling control with Rule-based and Q-learning Algorithms 3

Whole-building Energy Co-simulation for Dynamic Occupant-bas...

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3rd IEEE international conference on Frontiers Technology of Information and computer, ICFTIC 2021

作者： An, Tongrui Ealing International School Liaoning Dalian China

ISBN: (纸本)9781665406055

Commercial building has become one of the most energy-consuming fields, which is as the similar level as transportations and industries. The majority of energy consumption in a commercial building is derived from heating ventilation and air conditioning (HVAC) system. Previously, the indoor temperature was mostly regulated by a fixed control strategy, without varying on the basis of other dynamic factors such as occupancy schedules, which leads to huge amount of energy waste. To optimize this control strategy for energy consumption reduction, this research study has employed two algorithm methods of rule-based control and tabular Q-learning control to achieve the dynamic control to reduce building energy consumption with agent-based stochastic occupancy simulator. As a result, with rule-based control, the simulation result has shown total energy consumption saves approximately 24% site energy annually compared to baseline control. In addition, the result derived from tabular Q-learning has shown 10% savings could be achieved during the cooling season with the optimal policy based on the defined action space and state space. In the future, both rule-base control and Q-learning control used for the cosimulation in this study are expected to be applied in more and more commercial buildings to achieve energy savings and economic benefits. © 2021 IEEE.

关键词： Energy utilization

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Fair Incentive Allocation for Secure Data Sharing in Blockchain Systems: A Shapley Value Based Mechanism 3rd

Fair Incentive Allocation for Secure Data Sharing in Block...

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Yang, Ming Feng, Honglin Wang, Xin Jiao, Xuguo Wu, Xiaoming Qin, Shuwei Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Public Security of Shandong Province Shandong Intelligent Public Security Joint Innovation Center Jinan China

ISBN: (纸本)9789819710829

In the contemporary era of information technology, the exponential surge in data has rendered colossal potential value, which would be fully unlocked through data circulation and sharing. However, when the data contains sensitive information, special attention must be paid to the data privacy and security problem for data sharing. A feasible solution is to incorporate blockchain technology into the data sharing system, and the new challenge is how to provide fair rewards for data owners to motivate them to contribute data. To address this challenge, in this paper, we first design a blockchain-based data sharing system to enhance the trust and reliability of the data shared among participants. Then we propose an incentive allocation method based on the Shapley value, which comprehensively incorporates each participant’s contribution while ensuring fairness for the allocation. Finally, multiple numerical simulations are conducted to validate the effectiveness of the proposed mechanism. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Blockchain

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