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Research and Experiment on automatic Navigation control Technology of Intelligent Electric Tractor

Research and Experiment on Automatic Navigation Control Tech...

引用

2022 international conference on Agri-Photonics and Smart Agricultural Sensing Technologies, ICASAST 2022

作者： Cai, Xiaolei Fan, Wenjie Wang, Yang Qian, Yushuai School of Electrical and Information Engineering Jiangsu University Jiangsu Zhenjiang212013 China

ISBN: (纸本)9781510657731

The automatic navigation control system of the intelligent electric tractor is mainly studied in this research. Based on the analysis of the overall scheme of the control system of the electric tractor, a path tracking algorithm is proposed for the automatic navigation control system, and the working principle and control process of the algorithm are analyzed in detail. Finally, the effectiveness and stability of the automatic navigation control system are verified by a tractor automatic navigation experiment in a field. © 2022 SPIE.

关键词： control systems

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Evaluating Model Performance Through a User-Centric Explainable Framework for Probabilistic Load Forecasting Models 3

Evaluating Model Performance Through a User-Centric Explaina...

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3rd international conference on Power, control and computing Technologies, ICPC2T 2024

作者： Robin, Rebecca Heistrene, Leena Belikov, Juri Baimel, Dmitry Levron, Yoash Nirma University Department of Electrical Engineering Gujarat Ahmedabad India School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Tallinn University of Technology Department of Software Science Tallinn Estonia Sami Shamoon College of Engineering Electrical Electronics Engineering Department Beer-Sheva Israel Technion-Israel Institute of Technology The Andrew and Erna Viterbi Faculty of Ele. Comp. Engg. Haifa Israel

ISBN: (纸本)9798350349207

Load forecasting models ensure efficient, secure, and stable operation of the modern power system. Probabilistic forecasting accounts for uncertainties associated with missing features that are often overlooked by deterministic approaches. However, machine learning-based probabilistic models are complicated. This paper proposes a user-centric explainable AI framework that presents global and local interpretations aligned with the expertise and explanation needs of the targeted user. The overall influence of temporal and spatial exogenous features at the model development stage is evaluated using the Permutation Feature Importance technique. Such an explanation provides a holistic picture of the knowledge gained by the Gradient Boosting Regressor-based probabilistic load forecasting model. Further-more, the proposed framework suggests the implementation of SHapely Additive exPlanations (SHAP) at the post-deployment stage for individual forecast instances. Local explanations provided by SHAP are used to distinguish between interval forecasts with higher and lower forecast accuracy. Such distinction is applied for both the lower and upper bounds of the forecast interval. This is specifically useful for the non-AI expert end-users that need load forecasts for their strategizing their daily operations. This work is validated on the Kaggle data set on the national load demand of Panama supported with several other exogenous features such as weather-related quantities, holidays, and date-time details. Results show the efficacy of the proposed framework and its ability to provide user-friendly interpretations aligned with users' explanation goals. © 2024 IEEE.

关键词： Forecasting

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Enhanced electrical Wheel Chair based on Eye Blinking control 2

Enhanced Electrical Wheel Chair based on Eye Blinking contro...

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Chowdary, Ch. Venkat Madhusudan Rao, M. Radhika, S. Meenakshi, V. Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350315905

Wheelchair is an inevitable requirement for seriously impeded individuals to enable navigation. Driving a wheelchair is a drawn-out task for seriously incapacitated people, except if they utilize the tongue to control the joystick. All the while, visually impaired and paraplegic individuals manage two issues, which cause uncomfortable circumstance for them, i.e., headway and confinement. Various frameworks are being created to defeat the issues depicted above, permitting the end-client to perform safe developments and achieve some day to day existence of significant assignments. Our Automated wheelchair utilizes eye squint and head slant development to control the wheelchair. Likewise, we can give more freedom to the impaired individual by utilizing a similar head slant development to speak with the gadgets in a space for instance: a fan. This correspondence is finished utilizing a RF transmitter and beneficiary. Utilizing this, the individual have some control over different gadgets without any problem. © 2023 IEEE.

关键词： Wheelchairs

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Grouping of the UAV Swarm Based on automatic Fuzzy Clustering

Grouping of the UAV Swarm Based on Automatic Fuzzy Clusterin...

引用

international conference on Guidance, Navigation and control, ICGNC 2022

作者： Liu, Zhiheng Zhou, Rui Chen, Jinyong Zhang, Ning School of Automation Science and Electrical Engineering Beihang University Beijing100083 China Science and Technology on Aircraft Control Laboratory AVIC Xi’an Flight Automatic Control Research Institute Xi’an710065 China

ISBN: (纸本)9789811966125

The confrontation between UAV swarms will be an important combat style in the future, and a reasonable division of such a target air fleet is instructive to the deployment of our own resources. In this paper, we combine the genetic fuzzy clustering and the cluster validation to achieve autonomous grouping of the UAV swarm. Firstly, the flight state parameters such as position, speed, and yaw angle are used as characteristic components to measure the differences among UAVs. Then, the proposed approach establishes a mathematical model for grouping of the UAV swarm by fuzzy C-means algorithm. In addition, we compare five advanced clustering validity indexes and a clustering validation method based on graph theory, from which an evaluation criterion more applicable to grouping of the UAV swarm is selected. Finally, numerical simulations are performed to verify that the proposed approach can divide the air fleet autonomously and rationally in complex task scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Comparative Study on Extractive Summarization Using Sentence Ranking Algorithm and Text Ranking Algorithm

Comparative Study on Extractive Summarization Using Sentence...

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2023 international conference on Power, Instrumentation, control and computing, PICC 2023

作者： Majeed, Mansoora Kala, M.T. Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350334463

It is possible to create an accurate and succinct summary using a method called automatic text summarising. Before creating the necessary summary sentences, the machine learning algorithms may be trained to understand documents and recognise the portions that transmit crucial facts and information. Abstractive text summa- rization and Extractive text summarization are its two methods. An extractive text summarization refers to the process of removing significant text from a text document or source manuscript. Deep learning techniques are used to train an abstractive text summarizer, which then generates new phrases and keywords based on the content of the original text. The process of acquiring and assimilation of the knowledge from numerous sources takes time. automatic text summary is used to speed up the process of finding and consuming pertinent information. An extractive text summarising technique chooses crucial phrases, sentences, and other parts of the original text and concatenates them into a compressed form. The extractive text summarization produces grammatically correct sentences. The project is to develop a model that demonstrates extractive text summarization using sentence ranking and text rank algorithm. It shows that the text rank algorithm does a remarkable performance to extractive text summarization. Using a sentence ranking algorithm, extractive summaries are created that rate sentences according to their weights. In order to create a high-quality summary of the input document and store the summary as audio, the input document's highly scored sentences are extracted. A non-supervised, extractive text summarising method is called TextRank. While calculating the value of words and phrases in a document, the TextRank algorithm looks at how they relate to one another. According on how important the words are in each phrase, it then assigns a score to each one. The summary is formed using the algorithm's choice of the most crucial phrases. © 2023 IE

关键词： Deep learning

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Load-Aware Container Orchestration on Kubernetes Clusters 14

Load-Aware Container Orchestration on Kubernetes Clusters

引用

14th international conference on Cloud computing and Services science, CLOSER 2024

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789897587016

Microservice Architecture is quickly becoming popular for building extensive applications designed for deployment in dispersed and resource-constrained cloud-to-edge computing settings. Being a cloud-native technology, the real strength of microservices lies in their loosely connected, autonomously deployable, and scalable features, facilitating distributed deployment and flexible integration across powerful cloud data centers to heterogeneous and often constrained edge nodes. Hence, there is a need to devise innovative placement algorithms that leverage these microservice features to enhance application performance. To address these issues, we propose extending Kubernetes with a load-aware orchestration strategy, enhancing its capability to deploy microservice applications within shared clusters characterized by dynamic resource usage patterns. Our approach dynamically orchestrates applications based on runtime resource usage, continuously adjusting their placement. The results, obtained by evaluating a prototype of our system in a testbed environment, show significant advantages over the vanilla Kubernetes scheduler. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Containers

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Class Incremental Learning Method for Hyperspectral Images Based on Real Data Playback Mechanism and Classification Network Optimization 15th

Class Incremental Learning Method for Hyperspectral Images B...

引用

15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Liu, Xiaodong Wang, Siqi Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Computer Science and Technology Information Countermeasure Technique Institute Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application requirements, new classes of HSI continue to emerge. In the face of open and dynamic application scenarios, the model is required to be able to continuously learn new categories based on maintaining existing category knowledge. Class incremental learning algorithms have received extensive attention as key solutions to the problem above. In this paper, we use a regularization-based incremental learning algorithm, elastic weight consolidation (EWC) for class incremental learning for HSI classification. After data preprocessing to reduce data dimensions, we select new classes for research on class incremental learning. We combine 3D-2D convolutional structures and residual blocks as the classification network and use real data playback mechanism to improve class recognition accuracy and realize class incremental recognition of HSI data. We demonstrate the feasibility of the algorithm with extensive experiments based on three widely used hyperspectral datasets. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Self-Tuning PID controller for Serial Robots with Real-Time Functional Optimization

Self-Tuning PID Controller for Serial Robots with Real-Time ...

引用

international conference on electrical engineering, computing science and automatic control (CCE)

作者： Cesar U. Solis Cervantes Sergio I. Palomino Resendiz Hipolito Aguilar Sierra ESIME IPN CDMX México Universidad La Salle México CDMX México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This paper presents an interesting application for Robust Extremum Seeking Algorithm (RESA) to a self-tuning PID controller for serial robots. The theoretical framework is presented to guarantee the stability and convergence of this proposal. The main advantage of this method is that it has an easy-to-implement the structure and does not require strong mathematical conditions allowing minimize a measurable convex functional in real-time which is selected to reduce the error norm and its derivative however there is the possibility of choosing it in another way. To validate the methodology we applied it to develop self-tuning Proportional-Integral-Derivative (PID) controller for a Selective Compliant Assembly Robot Arm (SCARA) robot.

关键词： Robotic assembly Mechatronics Perturbation methods Measurement uncertainty Stability analysis Real-time systems Proposals PD control Robots Optimization

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Real Time Implementation of Non-Intrusive Appliance Load Monitoring to Improve the Energy Efficiency of a Smart Home 12

Real Time Implementation of Non-Intrusive Appliance Load Mon...

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2024 12th international conference on Systems and control, ICSC 2024

作者： Chabane, L. Chrifi-Alaoui, L. Drid, M.-D. Drid, S. University of Geulma Laboratoire de Génie Electrique de Guelma Electrical Engineering and Automatic Department Algeria IUT de l'Aisne 13 Avenue François Mitterrand Soissons Cuffies02880 France University of Science and Technology Houari Boumedien Electrical and Industrial Systems Laboratory Algiers Algeria Higher National School of Renewable Energy Environment and Sustainable Development Batna05078 Algeria

ISBN: (纸本)9798331531812

The increase of the energy demand presents a main problem for modern buildings. The energy saving and reduction of energy consumption are the two challenges of the world. This paper consists in determining the consumption by uses in a domestic environment from the measurements taken at the level of the energy meter just downstream, using a current and voltage sensor, without a learning phase inside the residence. This so-called non-intrusive method has several advantages. It is used to process load curves and extract useful information for identifying uses, especially the most energy intensive. Also, this method helps us to review the energy management, control, metering and billing systems for our consumption, to achieve our goal of increasing energy efficiency and minimizing consumption. © 2024 IEEE.

关键词： Smart homes

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Visual Positioning System of Bag Removal Robot Based on Deep Learning 9

Visual Positioning System of Bag Removal Robot Based on Deep...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Liu, Bo Tang, Ruiyin College of electrical engineering North China University of Science and Technology Tangshan China School of electronic and control engineering North China Institute of Aerospace Engineering Langfang065000 China

ISBN: (纸本)9798350376548

In industrial production, the dumping of raw material packaging is mostly done manually, which not only affects production efficiency but also endangers the health of workers. This article proposes an improved algorithm based on YOLOV5, which uses Mobiletv3 instead of the YOLOV5 backbone network, adjusts parameters using an improved SE attention mechanism, and optimizes the model using the LeakyReLU activation function. The entire system uses an improved algorithm to detect the target bag rope, uses a depth camera to obtain the target coordinates, and finally calculates the target coordinates in the robot coordinate system. Compared with Fast RCNN and YOLOV5 algorithms, the improved YOLOV5 MobileNetV3 algorithm has a knot detection accuracy of 97.5%, a recall rate of 92.43%, and an F1 score of 94.8. The model has a small volume, fast detection speed, and a single image detection time of only 187ms, with a mapping rate of 97.5%. The error between the coordinates obtained after hand eye calibration and the actual coordinates of the robotic arm in the XYZ axis direction does not exceed 3mm, and the actual coordinates are much smaller than the target size. © 2024 IEEE.

关键词： Robotic arms

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