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Detection of Grape Clusters in Images Using Convolutional Neural Network

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

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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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Identification of Switched Gated Recurrent Neural Networks Using the EM Algorithm 5

Identification of Switched Gated Recurrent Neural Networks U...

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5th international conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Bai, Wentao Gu, Suhang Yan, Chao Zhang, Haoyu Jiang, Chunli Guo, Fan School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Electrical Engineering and Automation Changshu Institute of Technology Changshu China School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Automation Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9798350393521

In the domain of nonlinear hybrid dynamic system modeling, the effectiveness of switched autoregressive exogenous (SARX) systems may face certain restrictions. To address this issue, this paper presents an enhanced switched system framework. In this framework, all SARX subsystems are replaced with gated recurrent neural networks, aiming to overcome these limitations. Importantly, the proposed switched system does not rely on any prior assumptions about the knowledge of operating modes. Finally, a new identification method is proposed based on the expectation-maximization (EM) algorithm, and its effectiveness is validated through a simulation example. © 2023 IEEE.

关键词： Recurrent neural networks

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Research on noise reduction algorithm of unmanned surface vessels fault signal based on frequency stacked noise reduction self-encoder 6

Research on noise reduction algorithm of unmanned surface ve...

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6th international conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： He, Cheng Liu, Daling Yuan, Qinghai Qi, Xingbiao Mou, Zonglei College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao China Jining Ganghang Taipinggang Co. Ltd Shandong Jining China

ISBN: (纸本)9798331541750

The relentless advancement of technology and the swift development of unmanned systems have positioned unmanned surface vessels (USV s) as versatile, multi-mission small platforms. These USV s exhibit substantial potential and value in various domains, including marine research, offshore engineering, and military operations. However, as the complexity of their operating environments increases, the reliability and safety issues of unmanned surface vessels have become increasingly prominent. Therefore, promptly and accurately diagnosing faults in USV s is crucial for ensuring their safe operation. However, the fault data of USV s often contains significant noise interference, which affects the accuracy of fault diagnosis. To address this problem, this paper proposes a fault signal denoising method for unmanned surface vessels based on frequency-stacked denoising autoencoders. First, the employment of a moving average filtering technique as an initial noise reduction strategy. Secondly, this study introduces a frequency stacking denoising autoencoder capable of identifying and extracting noise features, thereby facilitating profound denoising of the initially denoised vibrational signals from USV s propellers. To verify the effectiveness of the proposed method, this paper constructed a specialized experimental platform and conducted a comprehensive test using real-world operational data from USV s. The experimental results show that using this method to denoise vibration signals for USV s fault diagnosis achieves an average accuracy of 94.44 %. This represents a 20% improvement in both accuracy and reliability compared to using undenoised vibration signals, providing high-quality data support for precise fault feature identification and diagnosis. © 2024 IEEE.

关键词： Signal denoising

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Solar Energy: An Analysis of Global, Direct and Diffuse Solar Irradiance for Mauritius

Solar Energy: An Analysis of Global, Direct and Diffuse Sola...

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2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Cunden, Tyagaraja Sooprayen Modelly Doorga, Jay Rovisham Singh Boojhawon, Ravindra Rughooputh, Soonil Dutt Université des Mascareignes Faculty of Sustainable Development and Engineering Department of Electromechanical Engineering and Automation Beau Bassin-Rose Hill Mauritius Université des Mascareignes Faculty of Sustainable Development and Engineering Department of Emerging Technologies Beau Bassin-Rose Hill Mauritius University of Mauritius Faculty of Science Department of Mathematics Reduit Mauritius University of Mauritius Faculty of Science Department of Physics Reduit Mauritius

ISBN: (纸本)9798350322972

Information on the components of solar radiation is key to the implementation of solar energy projects. It is highly sought by policymakers, energy planners, engineers, architects, and investors, as well as researchers and practitioners in the field of solar energy. In the current study, spatial and temporal analyses of the main components of solar radiation are conducted which reveal mean global horizontal irradiance, direct normal irradiance, and diffused irradiance of 178.5 kWh/m2, 187.9 kWh/m2, and 58.1 kWh/m2, respectively, over the 20-year period analyzed. Spatial analysis reveal that the northern plain of Mauritius favors the implementation of solar energy projects owing to high global and direct irradiances. High diffusion of incoming irradiance is observed to occur on the central plateau region and attributed to the presence of increased moisture content as a result of cloud convection occurring in the vicinity. A mean clearness index value of 0.6214 has been obtained over Mauritius, which is indicative of intermediate to clear sky conditions prevailing on the island. The clear sky conditions and high insolation are propitious conditions for solar technology implementation in Mauritius. © 2023 IEEE.

关键词： Solar energy

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The Smart Sustainable Development of automation Mobility in Industry 4.0 using IoT based Sensor Networks

The Smart Sustainable Development of Automation Mobility in ...

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Disruptive Technologies (ICDT), international conference on

作者： Felicita. F Ruth T. Shirmila Amit Kumar Mishra Birendra Kumar Saraswat Sanjay Srivastava Manisha Aeri Department of Commerce (General) Stella Maris College (Autonomous) Chennai Department of Commerce Madras Christian College Madras India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Industry 4.0, also called the 4th Commercial Revolution, is characterized by mixing advanced digital technologies into production and automation strategies. As part of this revolution, the development of automation mobility has grown to be an important aspect in accomplishing clever and sustainable increases in business sectors. The use of net of factors (IoT) primarily based sensor networks has been tested to be a key solution in driving this development. automation mobility refers to the capacity of machines and gadgets to move and operate autonomously in the manufacturing technique. Through this automation is made extra wireless and powerful by leveraging IoT-based sensor networks. These sensor networks allow real-time information collection, analysis, and communiqué among machines, allowing them to make smart decisions and adapt to converting environments.

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Intelligent Prediction Method of Online Open Course Passing Rate of automation Technology 8th

Intelligent Prediction Method of Online Open Course Passing ...

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8th EAI international conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Zhang, Weiwei Wu, Jiaofeng Guangdong Polytechnic of Environmental Protection Engineering Foshan528216 China

ISBN: (纸本)9783031211607

In the process of analyzing the passing rate of online open courses of automation technology, it is difficult to guarantee the prediction accuracy of the passing rate of courses due to the influence of course factors, learning behavior factors and environmental factors. Based on this, the intelligent prediction method of the passing rate of online open courses of automation technology is optimized and innovated. Firstly, collect the characteristic information of the online open courses of automation technology, then construct the evaluation index of the passing rate of the online open courses of automation technology, and finally optimize the evaluation algorithm to realize the intelligent prediction of the passing rate of the online open courses of automation technology. The experimental results show that the proposed intelligent prediction method of online open course passing rate of automation technology has high practicability and accuracy in the process of practical application, and fully meets the research requirements. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： E-learning

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Scenario-driven Technology Foresight for The Process Analytical Instrumentation Industry in The Philippines 8

Scenario-driven Technology Foresight for The Process Analyti...

引用

8th international conference on Business and Industrial Research, ICBIR 2023

作者： Magon, Selverino A. Ligutan, Dino Dominic F. Bandala, Argel A. Imbang, Glen A. De la Salle University Department of Electronics and Computer Engineering Manila Philippines Technology Management Center University of the Philippines Quezon City Philippines

ISBN: (纸本)9798350399646

Industrial automation has allowed firms from key sectors in the Philippines to remain competitive such as agriculture, retail, manufacturing, transportation, and warehousing. While the IT and BPO sectors are the biggest in terms of export services, manufacturing industries such as electronics assemblers remain significant players through the adoption of fourth industrial revolution (4IR) technologies. In effect, local manufacturing firms should upskill their workforce and keep their operations efficient by automating key processes of their businesses through the 4IR. The industrial developments in the Philippines created a growing need for locally available automation solutions, and process analytical instrumentation applications present opportunities for new product development that can spur new technologies and services for the local manufacturing scene. Advanced process analyzers such as gas chromatographs and spectrometers are normally sourced abroad. This study examines variables that can guide firms venturing into the local automation sector to consider possible future scenarios for the process analyzers market. By performing technology foresight through the scenario-building approach, key predictable drivers are identified and ranked through insights from industry professionals, and future scenarios are developed. © 2023 IEEE.

关键词： automation

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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2024 international conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Gao, Chao Bai, Mengting Lian, Xiaoqin Hu, Xiangyu Han, Shuangshuang Chen, Shichao Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350376739

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios. © 2024 IEEE.

关键词： Electronics industry

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CBS-YOLO for ship target detection in complex marine environments

CBS-YOLO for ship target detection in complex marine environ...

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2024 international conference on computer Application and Information Security, ICCAIS 2024

作者： Ma, Jing Wang, Jinsheng Deng, Tianao Liu, Peng Liang, Yongtu Tian, Yonglin Wang, Jiale Zhang, Yalong China Ship Research and Development Academy Beijing100192 China School of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781510689312

In intricate marine surroundings, problems such as target blockage and diminutive object dimensions frequently give rise to poor accuracy and low efficiency in conventional ship detection approaches. To tackle these hurdles, a ship target detection algorithm founded on CBS-YOLO and customized for such complex scenarios is proposed. Firstly, a small object detection layer is embedded into YOLOv5, which markedly enhances the model’s accuracy when it comes to detecting minute objects within images. Secondly, the CBAM attention mechanism is incorporated into both the Backbone and Neck segments, with the intention of strengthening the model’s capability to portray image features. Thirdly, the BiFPN is employed in the neck network, enabling the comprehensive utilization of feature information at diverse levels and consequently augmenting object detection performance. Through a succession of comparative experiments, it has been corroborated that the optimized CBS-YOLO algorithm surpasses the YOLOv5s detection algorithm by conspicuous margins. Precisely, it attains a 3.1% increment in accuracy, a 3.7% growth in mAP50:95, and a 2.7% enhancement in recall. These outcomes evidently manifest that CBS-YOLO demonstrates outstanding performance in maritime ship target detection. © The Authors.

关键词： Image segmentation

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Research on Reinforcement Learning-Based Modeling of Environmental Control in Confined Broiler Houses 5

Research on Reinforcement Learning-Based Modeling of Environ...

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5th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Zhang, Wanchao Zheng, Pengshen Wu, Laxiang Chen, Changxi Tianjin Agricultural University College of Computer and Information Engineering Tianjin China Food Co. Ltd Beijing China Ministry of Agriculture and Rural Affairs Tianjin China

ISBN: (纸本)9798350366174

In view of the complex environmental factors in the closed meat chicken house, the environmental condition is poor and difficult to control. The environmental control model of the chicken house based on reinforcement learning was established to realize the effective regulation of various environmental factors in the chicken house. A multi-terminal environment acquisition system based on NB-IoT technology is designed to meet the practical requirements of the regulation model verification in the chicken house. On this basis, Q-Learning algorithm is used to establish the regulation model. The Internet of Things environmental data acquisition system based on NB-IoT technology realizes reliable, stable and real-time collection of environmental data at a certain time interval in the meat and chicken house, providing sample data sets for the study of control models based on reinforcement learning. Compared with threshold switch control method, this control method has improved stability and uniformity. Through the environment acquisition system based on NB-IoT technology and the reinforcement learning model combined with Q-Learning algorithm, the environment of the closed meat chicken house can be effectively regulated. © 2024 IEEE.

关键词： Environmental monitoring

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