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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18817 条记录，以下是681-690 订阅

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Research control a Smart Car Based on Chinese Voice Recognition Technology through the Raspberry Pi 4B Platform 3

Research Control a Smart Car Based on Chinese Voice Recognit...

引用

3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Wu, Qianru Beijing No.4 High School Beijing China

ISBN: (纸本)9781665472784

A substantial proportion of ways controlling small smart cars such as wireless remote control and mobile phone remote control are achieved through manual controlling the cars. This paper proposes a smart car control system based on speech recognition technology, which controls smart car movement via voice command and improves the level of intelligence and individuality efficiently. The key of the project is to combine voice recognition with intelligent car control through the Raspberry Pi 4B platform. This paper combines the voice recognition module and coding in Python to realize the effective voice control of the intelligent car. © 2023 IEEE.

关键词： Python

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Breast cancer diagnosis using machine learning Techniques 3

Breast cancer diagnosis using machine learning Techniques

引用

3rd international conference on Optics, computer Applications, and Materials Science, CMSD 2023

作者： Muhamediyeva, D.K. Shaazizova, M.E. Doshanova, M. Yu. Tashkent University of Information Technologies Named after Muhammad Al-Kharezmy Tashkent Uzbekistan Digital Technologies and Artificial Intelligence Development Research Institute Tashkent Uzbekistan

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and na39;ve Bayesian classifiers. The properties of cell nuclei taken f... 详细信息

ISBN: (纸本)9781510674486

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms-naive Bayesian classifier, SVM and decision tree-are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer. © 2024 SPIE. All rights reserved.

关键词： computer vision

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Effects of Image Augmentation Techniques for Rice Leaf Disease Detection 3

Effects of Image Augmentation Techniques for Rice Leaf Disea...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Talati, Trusha Bhat, Akshat Kalbande, Dhananjay Sardar Patel Institute of Technology Department of Computer Engineering University of Mumbai India Sardar Patel Institute of Technology Department of Computer Science and Engineering University of Mumbai India

ISBN: (纸本)9798350338607

Rice leaf diseases substantially reduce crop yield, resulting in food shortages and financial losses. Early identification and control of these diseases can be aided and enhanced by automated computer vision-based detection systems. However, existing techniques suffer from low accuracy and inconsistency due to several issues. To improve model resilience against corrupted inputs and adversarial cases, this study examines the effects of image augmentation techniques on three transfer learning models for diagnosing rice leaf diseases. A consolidated dataset, which includes rice leaf images of five different classes, was used to train these models. The VGG-16 model trained on images augmented using the Random Flip technique achieves a maximum accuracy of 99.47%. However, we present the lightweight EfficientNet-B0 model, trained on MixUp augmented images, with an accuracy of 98.01%, as an alternative model that is more robust and suitable for deployment in mobile/web applications. Our results demonstrate that image augmentation techniques can enhance the model's robustness against synthetically altered images without affecting its ability to detect and predict rice leaf diseases. © 2023 IEEE.

关键词： Image enhancement

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Neural Contextual Adaptation (NCA): For Unsupervised Learning in Dynamic Environments 3

Neural Contextual Adaptation (NCA): For Unsupervised Learnin...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen Kumar, G.D. Sanjan, S. Suraj, P. Rohan, U.A. Srinivas, R. Thanush, V. Visvesvaraya Technological University P.E.S College of Engineering Department of Computer Science and Engineering Mandya India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In recent years, artificial intelligence (AI) has made significant strides in various domains, primarily driven by supervised learning techniques requiring vast amounts of labeled data. However, real-world applications, particularly those involving rapidly changing environments, demand models capable of autonomous adaptation without the need for extensive labeled datasets. In this paper, we present a novel framework for unsupervised learning that allows AI models to dynamically adapt in real-time to changing environments. Our approach leverages a combination of self-organizing maps and contrastive predictive coding to enhance model flexibility and adaptability across diverse settings. We introduce an architecture that incorporates temporal data processing and on-the-fly adaptation mechanisms, enabling the model to learn and generalize from evolving data distributions. Through extensive practical assessments and experimentation, we demonstrate our model's ability to outperform existing unsupervised approaches in real-time object detection and decision-making scenarios. The results, which show a significant reduction in model retraining time and increased adaptability to novel environments, suggest that our framework can be applied to various fields including robotics, autonomous vehicles, and dynamic control systems. © 2025 IEEE.

关键词： Self-supervised learning

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Face detection technology based on skin color features in MATLAB 3

Face detection technology based on skin color features in MA...

引用

3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Dang, Longfei Yang, Miao Jing, Xiaodong Xu, Yizhi Xiao, Qicheng He, Wanyu Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China Department of Computer Science and Technology Sichuan Police College LuZhou China Department of Crimina Technology Sichuan Police College LuZhou China

ISBN: (纸本)9781510681880

Face detection is the most basic processing in the face recognition process. If the detection method is not appropriate, it can cause the recognition process to collapse. Face detection is very important in face recognition. The facial pattern is very complex and susceptible to external interference. Common facial detection algorithms generally have weaknesses such as high computational complexity, slow speed, and high false alarm rates. In this paper, a skin color model detection method based on the YCbCr color space is used for facial detection, which solves the problem of high computational complexity. Morphological theory is also used for subsequent image processing. © 2024 SPIE.

关键词： MATLAB

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Research on Three-Phase Electronic Load Based on Three-Vector Model Predictive Current control by Expected Sector 3

Research on Three-Phase Electronic Load Based on Three-Vecto...

引用

3rd international conference on Artificial Intelligence and computer Information Technology, AICIT 2024

作者： Wan, Renxue Shu, Mingzheng Wang, Zhen School of Electrical and Information Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Aiming at the problems such as low simulation accuracy, slow dynamic response and poor anti-interference ability of traditional control methods for linear or nonlinear loads, a three-vector model predictive current control method based on fast prediction sector judgment is proposed. Firstly, the mathematical model of α-β coordinate system is established, and the relationship between the expected voltage vector and the basic voltage vector is analyzed. Then using the sector decision condition proposed in this paper, the sector in which the desired voltage vector is located can be quickly determined. This method greatly reduces the amount of computation required to perform 6 times cost function evaluation in traditional three-vector control method. Finally, two-step prediction is introduced to compensate for sampling and calculation delay. The simulation and experimental results show that the proposed control method can shorten the adjustment time by 0.1s compared with the traditional PI control, effectively improve the simulation accuracy of nonlinear load, and reduce the calculation amount by about 70% compared with the traditional three-vector method. © 2024 IEEE.

关键词： Vectors

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Improved FCOS algorithm for vehicle pedestrian object detection 3

Improved FCOS algorithm for vehicle pedestrian object detect...

引用

3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Wang, Dong Mi, Lei Yang, Peng Department of Electronic Engineering Chongqing University of Technology Banan Chongqing400054 China

ISBN: (纸本)9781510680449

To address issues such as low accuracy in vehicle and pedestrian detection and slow inference speed, an improved version of the FCOS object detection algorithm is proposed. The algorithm replaces the original backbone with ResNet18 and incorporates deformable convolution to enhance its ability to capture shape features of the targets, thereby improving feature extraction and model inference speed. A coarse localization box mechanism is added to the algorithm's localization detection head branch to improve the model's classification accuracy and detection box recall. A new background prediction branch is introduced to enhance the model's detection capability for positive samples by predicting the probability of differences with the environment. Additionally, an ATSS (Adaptive Training Sample Selection) label assignment scheme and GFocal Loss function are employed to replace the original label assignment scheme and loss function, mitigating the impact of imbalances between positive and negative samples in the dataset. The experimental results show that the improved algorithm achieves 94.7% on the KITTI traffic target data set, which is 22.2% higher than the original FCOS model, improves the inference speed to 74 pieces per second, and has better effect compared with some mainstream target detection algorithms. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer vision

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Automation in Monitoring Temperature, Vibration of Industry and Preventing Accident Using IoT 3

Automation in Monitoring Temperature, Vibration of Industry ...

引用

3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Uma Maheswari, Sabari L Teotia, Shashiraj Kumaravel, R. Pandey, Himani Dhinakaran, K. Kumar, Ashok R.M.K. Engineering College Department of Electrical and Electronics Engineering Kavaraipettai India Swami Vivekanand Subharti University Department of Computer Application Meerut India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Elayampalam India Baroda University Department of Applied Science and Humanities Vadodara India Misrimal Navajee Munoth Jain Engineering College Department of Computer Science and Engineering Chennai India Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9798350336238

Industrial safety is more important in both safety of human being and machines. To ensure this the temperature of the machine is monitored continuously and if any abnormal situation arises, the machine will be isolated from the supply using IoT. Similarly, the sound and vibration inside the factory is monitored using vibration sensor and intimated to the control room if needed. The control room may be located in remote place the information will be sent to them using IoT. Vibration may induce due sudden fall of any heavy materials, braking of machine shaft, fall of objects from Crane, etc. The vibration caused by the unexpected incident will be identified by the vibration sensor provide in the factory/ Industry in various places. The sensor will alarm by siren and intimate to the operator or control room. SMS (Small Message Service) also send to the operator through GSM. © 2023 IEEE.

关键词： Accident prevention

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Research on personnel vigilance helmet recognition algorithm based on Tengine 3

Research on personnel vigilance helmet recognition algorithm...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Chen, Ruidong Wang, Xin Bing, He Yang, Maocai Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510661363

In the complex environment of the city, due to the limited visual blind spot of the human perspective, the existing police helmets and other equipment cannot give sufficient protection to the special service squad because it does not have a self-warning function. In order to protect the safety of team members and improve the completion rate of tasks, a personnel vigilance system based on Tengine was designed. First, the high-performance RK3399 computing platform and the dual MIPI for real-time picture acquisition were selected according to the design requirements Wide-angle camera. Secondly, simulation experiments are carried out on virtual machines, and lightweight image recognition algorithms based on Tengine are trained and built through the VOC2012 dataset. Finally, the appropriate algorithm is selected according to the analysis and comparison of the experimental results. Experiments have proved that the personnel vigilance helmet recognition algorithm based on Tengine can meet the deployment and real-time target detection requirements of the embedded platform RK3399, which can effectively improve the survival rate of special agents and ensure the efficient completion of tasks. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer vision

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Designing Torus and HyperCube Network-on-Chip Systems Based on MPLS Networking Technique 3

Designing Torus and HyperCube Network-on-Chip Systems Based ...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Sllame, Azeddien M. Daddsh, Malak University of Tripoli Faculty of Information Technology Computer Network Department Tripoli Libya

ISBN: (纸本)9798350319897

This paper describes a simulator for two types of network-on-chip designs that employs the MPLS as an essential communication tool for switching and routing at the packet level inside the chip of multiprocessing system on chip implemented as a network-on-chip. Modeling and simulation results demonstrated the when using MPLS with network-on-chip system efficient results obtained with reduced latency, less packet error ratio, and fault tolerance achieved with the nature principle of the MPLS. © 2023 IEEE.

关键词： Network-on-chip

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