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共 47494 条记录，以下是191-200 订阅

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The All Wheel Speed Balance control Concept 27

The All Wheel Speed Balance Control Concept

引用

27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Atheupe, Gael P. Martinez, Didier Monsuez, Bruno Renault Technical Centre Renault Group & Ensta Paris Paris France Renault Technical Centre Renault Group Dept. Chassis Control & Adas Systems Guyancourt France Ensta Paris Dept. Computer Science & Systems Engineering Paris France

ISBN: (纸本)9798331505929

The transition to vehicle electrification introduces new demands on chassis dynamics, paving the way for advances in driving dynamics, safety, and efficiency. A key consideration arises: how are driving torque impulses converted in electric vehicles with multiple power sources? This study introduces a novel approach to address these demands through the integration of state-of-the-art yaw control, using concurrent front/rear axle and front right/front left torque distribution based on the wheel speed balance control concept. Our aim is not necessarily to demonstrate the supremacy of our approach, but rather to present a fresh philosophical perspective on the problem of yaw control in electric vehicles. Our method embodies dynamic load transfer and axle dynamics into the yaw control problem formulation, setting it apart from current methodologies. Through experimental results, we illustrate the effectiveness of our proposed approach regardless of road surface conditions. © 2024 IEEE.

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A Comparison of Prompt engineering Techniques for Task Planning and Execution in Service Robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

引用

23rd IEEE-RAS international conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-engineering. © 2024 IEEE.

关键词： Human robot interaction

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intelligent Refrigerator control System Based on SCM

Intelligent Refrigerator Control System Based on SCM

引用

2023 IEEE international conference on Sensors, Electronics and computer engineering, ICSECE 2023

作者： Huang, Lei Wang, Lingxiao Dalian University of Technology City Institute Liaoning Dalian116000 China

ISBN: (纸本)9798350313734

With the gradual development of science and technology, intelligent equipment is also gradually entering into people's life. The intelligent refrigerator control system has now developed into an intelligent home appliance product, which can monitor and control the internal environment of the refrigerator in real time through temperature, humidity and other sensors, and has communication technologies such as mobile phone App and Internet, so that users can remotely control or monitor the refrigerator at home. The intelligent refrigerator control system based on SU adopts STM 32 as the main control chip so that the refrigerator will be more intelligent and developed. © 2023 IEEE.

关键词： Refrigerators

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

引用

2024 international conference on advances in Data engineering and intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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Anomaly Detection for Bolt Tightening in Automotive Engine Assembly Based on Machine Learning 5

Anomaly Detection for Bolt Tightening in Automotive Engine A...

引用

5th international conference on computer engineering and intelligent control, ICCEIC 2024

作者： Zhang, Dong Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9798331507992

With the advancement of industrial automation, the quality requirements for automotive engine assembly bolt tightening have become increasingly stringent, as they are directly related to engine performance and vehicle safety. Traditional monitoring methods for bolt tightening rely on preset torque criteria, which often overlook anomalies such as material defects and tool wear. To address the issue of limited negative samples in industrial data, this study proposes an innovative positive sample boundary modelling strategy based on artificial intelligence, combined with a random forest algorithm for anomaly detection. By transforming time-series data into high-dimensional feature data, an efficient classifier is constructed through the integration of multiple decision trees. Experimental results from real engine assembly line data demonstrate that the proposed method can quickly and accurately identify abnormal conditions without increasing computational burden, significantly improving assembly quality and reducing failure rates. © 2024 IEEE.

关键词： Bolt tightening

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Deep Learning-Based Detection of Fungal Diseases in Apple Plants Using YOLOv8 Algorithm

Deep Learning-Based Detection of Fungal Diseases in Apple Pl...

引用

2024 international conference on advances in Data engineering and intelligent Computing Systems, ADICS 2024

作者： BalaChandralekha, S. Thangakumar, J. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Over the last decade, the identification of plant illnesses by automatic means has become a challenging field. In recent times, computer vision has made it feasible to identify diseases quickly and accurately, which has enhanced yield by allowing for the necessary treatment. Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), Faster-CNN, Mask R-CNN, Siamese Networks, are the models built on deep learning have been applied to diagnose and cure crop disease. These models accomplish well when it comes to identifying plant diseases, but they have trouble with complex field images and frames with several leaves. The goal of this study is to enhance their application in real-world agriculture, where problems with intricate images and multiple leaves are frequent. This paper uses a YOLOv8 model to identify fungal diseases that affect apple leaves, including cedar rust, scab leaves, and black rot. The dataset used for the implementation was gathered from a Kaggle of 540 images, and the model obtained 86% mAP, outperforming the existing YOLOX model which has achieved 73%mAP. © 2024 IEEE.

关键词： Convolutional neural networks

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Comprehensive Review: Advancing Cognitive Computingthrough Theory of Mind Integration and Deep Learning in Artificial Intelligence 24

Comprehensive Review: Advancing Cognitive Computingthrough T...

引用

8th international conference on computer Science and Application engineering, CSAE 2024

作者： Liu, Shuyan Wang, Kangsheng School of Information Science and Engineering Yunnan University Yunnan Kunming China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400718090

In this article, we will review our advances in intelligence modeling the mind and explore various efforts to mimic the processes that would allow humans to perform cognition files and line widgets of processing. It presents ideas and stresses how understanding cognitive functions is vital for AI progress. Three principal modeling strategies are examined: cognitive mind modeling, assignation mind modeling, and intelligent body mind modeling. In the article, he explores a few theories, such as symbol systems and the ACT-R framework. It further argues that learning seems limited in encoding models of the mind, citing its successes in language-oriented tasks and its limitations in translating basic mechanisms like reward to model-level language and data from neuroscience. Additionally, it investigates ways of assessing and enhancing learning theory of mind abilities based on tasks that utilize end-to-end function. The review reiterates the need for an improved understanding of human-level thought processing to develop intelligent systems that can better discern intentions and feelings. It provides some ideas on evaluating this based on deep learning approaches. The review argues that viewing learning through the lens of systems yields new insights into human thought and social behavior that could enable the development of novel and more adaptive AI technologies. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Design of Garbage intelligent Recognition and Automatic Classification Device 4

Design of Garbage Intelligent Recognition and Automatic Clas...

引用

4th international conference on advances in Electrical engineering and computer Applications, AEECA 2023

作者： Wu, Jiaqi Zhang, Yuhao Yi, Haibo Deng, Weiping Qu, Tao School of Artificial Intelligence Shenzhen Polytechnic Shenzhen China WeBank Shenzhen China Guangzhou Urban Blockchain Industry Association Guangzhou China

ISBN: (纸本)9798350308082

Artificial Intelligence (AI) has received increasing attention in the field of computer science. and it has been applied in robotics, economic and political decision-making, control systems, and simulation systems. In recent years, there have been efforts to accelerate the classification of household waste, establish a garbage classification and recycling system, and expedite the establishment of a garbage treatment infrastructure for sorting, placement, collection, transportation, and processing. The Raspberry Pi is a credit card-sized microcomputer with a Linux-based operating system. Given the need for a smaller recognition device with higher computational complexity, we choose the Raspberry Pi as our hardware platform for this project. Utilizing the Raspberry Pi's lightweight hardware and AI, intelligent garbage classification can achieve greater accuracy, speed, and efficiency. This article presents an intelligent garbage classification device designed using the Raspberry Pi 4B, which can be used in various scenarios due to its small footprint. The design method proposed in this paper can be widely applied to fields such as smart homes and smart cities. © 2023 IEEE.

关键词： computer vision

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Design and implementation of intelligent Water Cup system based on wireless sensor network 24

Design and implementation of Intelligent Water Cup system ba...

引用

5th international conference on computer Information and Big Data Applications, CIBDA 2024

作者： Wang, Nan Liu, Yan Yan, Zhiqiang College of Computer Science Wuhan Donghu University WuHan430299 China College of Computer and Artificial Intelligence Wuhan University of Engineering Science Wuhan430200 China

ISBN: (纸本)9798400718106

This project has designed an intelligent water cup system based on based on wireless sensor network. The cup cover is provided with a memory area of a memory chip and an intelligent control system is embedded on the cup body, through intelligent sensors (temperature sensor, GPS sensor, pressure sensor, etc.) to achieve measurement and remind users to drink water. Its specific functions include: Using GPS sensors to monitor the location of the cup to avoid losing track;using temperature sensors can determine the ambient temperature and water temperature;through Bluetooth, transmission of basic information in the cup, including the cup of electricity, water temperature, water allowance and other information. The intelligent Cup is also equipped with display, alarm, communication, storage, positioning and other modules, so as to achieve intelligent control, to bring more convenience to people’s lives. © 2024 ACM.

关键词： Wireless sensor networks

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intelligent control Method and Application of Sintering Process 24

Intelligent Control Method and Application of Sintering Proc...

引用

8th international conference on computer Science and Application engineering, CSAE 2024

作者： Xiao, Xuewen Sun, Xiaodong Wu, Fangrui Zhou, Jipeng Tang, Bin CISDI Engineering Co. Ltd. Chongqing Chongqing China Co. Ltd. Chongqing Chongqing China

ISBN: (纸本)9798400718090

Sintering process is one of the most important parts during the whole iron and steel production, the quality of the produced sinter is directly affected by the stability of the sintering process, and high-quality sinter is beneficial to the subsequent iron-making process. As a crucial parameter characterizing the state of the sintering process, burn-through point (BTP) is typically utilized to determine whether current sinter production is stable, therefore the stable control of the BTP is the key to controlling the stability of the entire sintering process. In this paper, an intelligent sintering process control method consisting of two modules is proposed. One module based on the deep neural network (DNN) is designed to achieve accurate prediction of BTP, the other module is designed to stabilize the BTP by adjusting the sintering machine speed, according to the deviation between the predicted BTP from the first module and the target BTP set by the expert. Moreover, the speed adjustment model of sintering machine is established by a convolution neural network (CNN), whose input design is similar to the observation logic of manual adjustment by operators. The experimental results show that the sintering machine speed adjustment recommended by the method is highly consistent with that in the actual sintering production process. The proposed intelligent sintering process control method provides a favorable guidance for the speed adjustment of sintering machine, and implies the foundation for the closed-loop control of the sintering machine speed. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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