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Research on power grid integrated regulation authority setting method based on role security control

Research on power grid integrated regulation authority setti...

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electrical engineering and control science (IC2ECS), international conference on

作者： Hongyu Shi Haiying Huang Sisi Ye Platform & Security Company of CSG Digital Grid Research Institute Guangzhou Guangdong China

Aiming at the problem of poor identification and management effect of integrated regulation authority setting management of power grid, a method of integrated regulation authority setting of power grid based on role security control is proposed. The function of automatic regulation authority management of power grid dispatching is optimized in combination with the principle of role security control, Compared with the traditional method, the method of power grid integrated regulation authority setting based on role security control has significantly higher recognition accuracy of role authority, and the response time of authority setting is faster, which takes 40min longer than the traditional method.

关键词： Regulators Power grids Regulation Dispatching Real-time systems Safety Security

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Crop Monitoring and automatic Weed Detection using Drone

Crop Monitoring and Automatic Weed Detection using Drone

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2021 international conference on control, Automation, Power and Signal Processing, CAPS 2021

作者： Hafeez, Abdul Tiwari, Vikas Verma, Vinod Kumar Ansari, Akhtar Saleem Husain, Mohammed Aslam Singh, S.P. Khan, Ahmad Neyaz Rajkiya Engineering College Dept. Electrical Engineering Ambedkar Nagar India Govt Polytechnic Ambedkar Nagar India Integral University Department of Computer Science Lucknow India

ISBN: (纸本)9781665445771

The effective farming means lossless farming which is a desired need of current scenario, since the cost of farming is increasing continuously. Weeds are a big problem in agriculture because it is major part of agricultural losses. Nowadays, herbicides are sprayed uniformly throughout the field to control the weed. This method is not only expensive but also have bad impacts on environment and human health. Therefore, an automatic system is needed for spot spray. Improvement in drone technology leads to availability of high payload drone that are suitable for pesticides spraying. Embedded system programed with deep learning when use to control the drone then it will be possible to spray the herbicides at desired place. This paper discusses the artificial intelligence (AI) based crop monitoring and weed detection system that are suitable for spot herbicides spraying. © 2021 IEEE.

关键词： Herbicides

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Comparative Analysis of Deep Learning Models for Cotton Leaf Disease Detection

Comparative Analysis of Deep Learning Models for Cotton Leaf...

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international conference on Big Data and Cloud computing, ICBDCC 2021

作者： Mary, X. Anitha Raimond, Kumudha Raj, A. Peniel Winifred Johnson, I. Popov, Vladimir Vijay, S.J. Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Plant Pathology TNAU Coimbatore India Additive Manufacturing Technion–Israel Institute of Technology Haifa Israel Department of Mechanical Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9789811921766

Cotton is the most essential crop and plays an important role in the agricultural economy of the country. Cotton crop is prone to many diseases because of changes in the climatic conditions, insects such as pink bollworm, and many other factors. These diseases decrease crop productivity, and at present farmers, are diagnosing the diseases with their own experience. But these kinds of naked-eye observations do not give accurate results on large plantation areas. Therefore, it is necessary to develop an automatic, accurate, and economic system for detecting leaf diseases. The aim of this work is to detect the infected cotton leaf using Convolutional Neural Network (ConvNet/CNN) which is a deep learning technique. Nearly 519 healthy leaves and 387 diseased leaves are collected from reliable sources and studied. This work focusses on the performance evaluation and comparison of the powerful CNN architectures: AlexNet, InceptionV3, and Residual Network (ResNet) 50, VGG 16, NASNet and Xception in detecting the diseased cotton leaf. Out of these six models, ResNet50 and VGG 16 has shown significant performance with 97.56% of accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Design and Implementation of Running Motion Generator for Humanoid Robots using SLIPM and CPG

Design and Implementation of Running Motion Generator for Hu...

引用

international automatic control conference

作者： Hui-Hsuan Lee Kuan-Ting Cho Tzuu-Hseng S. Li Department of Electrical Engineering aiRobots Laboratory National Cheng Kung University Tainan Taiwan

The purpose of this research is devoted to establishing a running generation technique for bipedal robots. The motion generator is based on the Bipedal Spring-Loaded Inverted Pendulum Model (Bi-SLIPM) and is designed to stabilize running motions. This work incorporates human motion features during the generator process. First, this paper introduces the bipedal robot used for the tasks. The 2D Bi-SLIPM is employed to generate the running pattern. To ensure the applicability of the running motions generated by the 2D Bi-SLIPM in real-life scenarios, this paper introduces the concept of Discrepancy of Leg Compression (DLC) based on the 2D Bi-SLIPM. Additionally, a Central Pattern Generator (CPG) is integrated as an auxiliary synthesizer to generate DLC-CPG motions. These motions serve as the motion generators for the abduction direction of the hip and the ankle. Moreover, to address the challenges posed by tremors and inherent instability in running movements, this paper proposes the integration of feedback data from an inertial measurement unit. The insights gathered from the inertial measurement unit are incorporated into a heuristic algorithm, which systematically explores and refines parameters for the gait model. Through this iterative training process, the resilience and stability of the running gait are significantly improved. Ultimately, the training results demonstrate the successful realization of the proposed motion generation and control techniques on the physical robot.

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Emotion Recognition Based on Static Human Posture Features 6th

Emotion Recognition Based on Static Human Posture Features

引用

6th international conference on computing, control, and Industrial engineering, CCIE 2021

作者： Wang, Sihao Wang, Xianmei Wang, Zhiliang Xiao, Ruoxiu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789811939266

Currently, the research of emotion recognition mainly focuses on facial expressions and physiological signals. While there is little research on emotion recognition based on posture modality. This paper proposes an improved emotion classification method based on VGG16. Initially, static posture of human body can be described by the body factor and the shape factor based on the Laban Movement Analysis. 38-dimensional features were selected to encode human body posture. Moreover, the pose image is used as the input of the proposed improved VGG16 network. After the second fully connected layer in this network, the pose features extracted are added by offline vector merging. In this method, image features extracted by the network and artificial features are fused. A dataset BEAST adopted in the experiment avoids other modalities interference. The classification accuracy of the improved method reaches 64.37%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based Approach

Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based...

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international conference on Distributed computing Systems

作者： Weizhen Han Enshu Wang Bingyi Liu Zhi Liu Xun Shao Libing Wu Jianping Wang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Sanya Science and Education Innovation Park of Wuhan University of Technology School of Cyber Science and Engineering Wuhan University Wuhan China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Aichi Japan Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

With the capability of intelligent control and communicating with surrounding vehicles and infrastructures, connected and automated vehicles (CAVs) can drive cooperatively and have more positive effects on traffic efficiency. Cooperative and real-time path planning for CAVs stands as a pivotal solution to mitigate traffic congestion and augment travel efficiency. However, most of the existing path planning schemes predominantly concentrate on minimizing the travel times of vehicles, sidelining the broader issue of alleviating traffic congestion in urban settings. Therefore, in this paper, we propose a novel collaborative vehicle path planning scheme, leveraging the intelligent control and the communicating ability of CAVs. The primary objective is to reduce traffic congestion within the overall transportation system and improve traffic efficiency. Specifically, we focus on a general urban scenario with various types of vehicles, including CAVs, connected vehicles (CVs), and traditional human-driven vehicles (TVs), To enhance traffic efficiency in such a scenario, we design a collaborative path planning scheme to discover the efficient paths for both CAVs as well as CVs. In this scheme, we treat each CAV as an agent and formulate the multiple CAVs' path-planning problem as a Markov game. To solve the above Markov game, we design a multi-agent convolutional attention reinforcement learning (MACA) framework to generate paths with minimal travel time for CAVs. More concretely, the proposed MACA framework incorporates a convolutional neural network (CNN) layer to capture spatial correlation behind traffic conditions. Additionally, a graph attention network (GAT) layer is employed to integrate the influence of neighboring agents during the path-planning process. To further reduce traffic congestion, we extend the MACA framework into a collaborative MACA (C-MACA) scheme in vehicular networks, where CAVs are empowered to periodically broadcast their path information to surr

关键词： Heuristic algorithms Collaboration Transportation Games Reinforcement learning Path planning Real-time systems

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A Novel Meta-Learning Ensemble Framework for Cancer Classification Using Convolution Neural Networks

A Novel Meta-Learning Ensemble Framework for Cancer Classifi...

引用

Electronics, computing, Communication and control Technology (ICECCC), international conference on

作者： Anjaneyulu Nangunuri Abhijit T. Somnathe B Prasanthi E Latha Mercy S V Ramanan Pundru Chandra Shaker Reddy Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Electronics and Telecommunication Engineering Smt. Kashibai Navale College of Engineering Pune India Department of CSE (AI& ML) CVR College of Engineering Hyderabad Telangana India Electronics and Communication Engineering PPG Institute of Technology Coimbatore India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electrical and Electronics Engineering Government College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Detecting cancer at an early stage is vital because it is a complicated global health crisis that killed 10 million people in 2018. Creating more accurate technology that provides information about the patient's cancer through early diagnosis helps clinicians make better-informed treatment decisions. This research delves deeply into a number of malignancies. A solid overview of machine-learning(ML) and deep-learning(DL) strategies employed in cancer research is also presented in this work. The paper also suggested a multi-neural ensemble learning strategy that relies on stacking to improve the anticipation performance on 8datasets. These datasets include prostate, non-small cell lung cancer survival, cervical, mesothelioma, and Wisconsin breast cancer. The 3real-time cancer datasets from the Jammu and Kashmir region-lung, ovarian, and leukemia-are also examined in this work. Our study's methodology achieved the highest level of anticipation accuracy across all types of cancer datasets, according to the simulation findings. Also, statistical validation of the proposed approach has been done. The researchers set out to create and test a model that could integrate anthropometric, clinical, imaging, and genetic data to make predictions about cancer risk. When compared to homogenous model-multi-modal and uni-modal frameworks, the simulation results show that the suggested framework is more accurate.

关键词： Metalearning Accuracy Biological system modeling Simulation Stacking Solids Breast cancer

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Research on machining accuracy of workpiece oriented to autonomous behavior of equipment 26

Research on machining accuracy of workpiece oriented to auto...

引用

26th international conference on Automation and computing, ICAC 2021

作者： Ma, Honglong Liu, Yefeng Sun, Weitang Liu, Yiming Zhang, Qichun Shenyang Institute of Technology Liaoning Key Lab. of Information Physics Fusion and Intelligent Manufacturing for Grade CNC Machine Fushun113122 China Shenyang Institute of Technology College of Mechanical and Vehicle Engineering Fushun113122 China University of Bradford Department of Computer Science BradfordBD7 1DP United Kingdom

ISBN: (纸本)9781860435577

Around the problem of workpiece machining accuracy, a model of autonomous behavior of equipment and the method of establishing patterns in knowledge base have been developed in this paper. Two self-learning models based on pattern comparison: deterministic knowledge and uncertain knowledge self-learning model are presented, while a scene understanding method for intelligent autonomous behavior of equipment is proposed. The research scheme of intelligent autonomous behavior optimization of equipment based on scene understanding and the specific research framework of workpiece machining precision are produced. Finally, focusing on the actual production process of bearing end cover, the flexible automatic production line of bearing end cover is established, the workpiece machining accuracy research based on independent behavior of equipment for CMM and CNC machine tools is preliminarily realized with the help of automatic line control end, it lays a solid foundation for the overall autonomous and intelligent cooperation between the testing area and the processing area. © 2021 Chinese Automation and computing Society in the UK-CACSUK.

关键词： Knowledge based systems

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In-situ automatic measuring device for ammonia volatilization in farmland

In-situ automatic measuring device for ammonia volatilizatio...

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2021 IEEE international conference on Advances in electrical engineering and Computer Applications, AEECA 2021

作者： He, Ying You, Kun Wang, Liming Li, Hongbin Yu, Dongqi Lei, Boen Zhang, Wangchun Fan, Boqiang Yang, Wenliang Zhu, Anning Zhang, Yujun Anhui Institute of Optics and Fine Mechanics CAS Key Laboratory of Environmental Optics Technology Hefei China Fengqiu Agro-ecological Experimental Station Institute of Soil Science CAS State Key Laboratory of Soil and Sustainable Agriculture Nanjing China

ISBN: (纸本)9781665435611

Excessive application of nitrogen fertilizer has caused the reduction of nitrogen fertilizer utilization efficiency, resource waste and environmental problems in our country. There are disadvantages of long sampling and heavy workload using the traditional methods of acid absorption and box method for farmland ammonia volatilization measurement. In order to meet the application needs for on-line automatic measurement of ammonia volatilization in farmland, an in-situ automatic measurement device for ammonia volatilization was designed combined with laser spectroscopy technology. The main body of the measurement device is an integrated sampling box, with the advantages of long optical path measurement, heat resistance and corrosion resistance. The control circuit of the sampling box used ARM as the main controller for environmental data monitoring, automatic opening and closing of the cover and lens temperature controlling. The ammonia volatilization results were analyzed by the data acquisition and processing system after the laser passed through the sampling box to obtain ammonia spectroscopy signal. The device was used for observation at Fengqiu Ecological Station, CAS. The results showed that the ammonia volatilization rose rapidly after 90 minutes for nitrogen fertilizer applying, and it decreased gradually to a low value after 270 minutes. According to the experimental results, the continuous measurement time for cover closing can be set to 3 minutes, and the cover opening time can be set to 3 to 5 minutes. The working performance of the automatic measurement device was verified through these experiments, with the advantages of low power consumption, high measurement sensitivity, and no gas sampling. It realizes insitu automatic measurement for ammonia volatilization in farmland with the natural respiration condition of the soil. © 2021 IEEE.

关键词： Nitrogen fertilizers

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Fog and Cloud computing-based IoT in Healthcare Monitoring System for Healthy Ageing 23

Fog and Cloud Computing-based IoT in Healthcare Monitoring S...

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23rd international conference on control Systems and Computer science Technologies, CSCS 2021

作者： Tudora, Eleonora Tirziu, Eugenia Nicolau, Nicolae-Dragos Gheorghe-Moisii, Maria National Institute for Research and Development in Informatics Systems and Applications for Society Department Bucharest Romania

ISBN: (纸本)9781665439398

The Internet of Things (IoT), through portable devices and sensor networks, has the potential to transform the way in which healthcare monitoring systems collect and analyze patients' medical data. Nowadays, the IoT-based healthcare monitoring systems for the elderly require large amounts of data that needs to be transferred to a centralized database and from the database to cloud data centers, which leads to a decrease in the performance of such systems. Thus, fog computing provides innovative solutions by bringing resources closer to the user and by providing low latency and efficient solutions for data processing as against to cloud. In this paper, we present the 'Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)' project which supports elderly centered healthcare services in order to ensure illness prevention, personalized assistance and monitoring healthcare services. According to the current development state of the project, we propose a functional framework of the architecture of RO-SmartAgeing system based on IoT, Fog and Cloud computing, present the hardware components of the system necessary for proper operation, the data flow chart of the RO-SmartAgeing system and an example of numerical validation and filtering of data from IoT devices. © 2021 IEEE.

关键词： Fog computing

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