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检索条件"任意字段=2022 2nd International Conference on Detection Technology and Intelligence System, DTIS 2022"

共 1121 条记录，以下是71-80 订阅

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Research on Lightweight Object detection Based on Improved YOLOv8n for Experimental Scenarios 2

Research on Lightweight Object Detection Based on Improved Y...

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2nd international conference on Artificial intelligence and Automation Control, AIAC 2024

作者： Lin, Runqi Zhuang, Jiafan Hao, Zhifeng Fan, Zhun Shantou University Department of Electronic Engineering Shantou515063 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China ShenZhen611731 China

ISBN: (纸本)9798331529147

Lightweight object detection algorithms, represented by YOLOv8n, have achieved significant optimization in terms of model size and parameter count. However, the neck network of YOLOv8n is not efficient at fusing contextual information, and the backbone network's feature extraction capabilities are limited. This results in challenges such as missed and incorrect detections in complex images, occluded objects, or scenes with significant background noise, making it difficult to meet the requirements of end-to-end industrial deployments. To address these issues, this paper proposes a lightweight object detection algorithm based on an enhanced YOLOv8n architecture. By integrating FasterBlock, GhostConv, and BiFPN modules, the model's detection performance and feature extraction capabilities are significantly improved, while the overall model size is reduced. Additionally, we constructed a dataset based on experimental scenarios to validate the effectiveness of the proposed model. Experimental results show that the modified model improves detection accuracy by approximately 7% compared to the original YOLOv8n, reduces the parameter count from 3.01 million to 1.73 million. This significantly enhances detection accuracy while substantially reducing the model's size. The improved YOLOv8n model not only demonstrates superior detection performance but also effectively reduces model complexity and resource consumption. This research lays a solid foundation for future applications of AI-driven automated scoring systems in experimental environments and provides a new direction for end-to-end industrial deployment. © 2024 IEEE.

关键词： Complex networks

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Exploration Study on Learning Styles Identification and Prediction Techniques 2

Exploration Study on Learning Styles Identification and Pred...

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2nd international conference on Innovative Research in Applied Science, Engineering and technology (IRASET)

作者： Jebbari, Mohammed Cherradi, Bouchaib El Gannour, Oussama Hamida, Soufiane Raihani, Abdelhadi Hassan II Univ Casablanca EEIS Lab ENSET Mohammedia Mohammadia Morocco Hassan II Univ Casablanca STIE Team CRMEF Casablanca Settat EEIS LabENSET Mohammedia Mohammadia Morocco

ISBN: (纸本)9781665422093

A learning style helps determine each learner preferred method and content by knowing their behaviors and habits. This is why learning style is important in learning (or learning systems), because it shows the learner its strengths and weaknesses in learning process. Also allows the teacher (or system) to invest, develop the competence of these learners. We will give an overview of most of the techniques used to detect learning styles, by analyzing and discussing results of learners' behavior. The static techniques (traditional) used to identify learners' learning styles based on questionnaires or tests. Due to the limitations and less accurate results of this technique, several automatic models (dynamic) have been proposed to detect the learning styles of learners during the online learning, based on the data collected from them in the educational system. Thus, several approaches have been proposed for the hybrid detection of learning styles, bringing together Static and automatic techniques. In each technique, we propose a model that describes the process for detecting the learning style. This study can be helpful to researchers working in the field of learning styles.

关键词： Learning style (LS) learner Behavior (LB) Artificial intelligence (AI).

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Synthesis of flower-like S-doped SnO2hierarchical structure for gas sensing application at room temperature 2

Synthesis of flower-like S-doped SnO2hierarchical structure ...

引用

2022 2nd international conference on detection technology and intelligence system, dtis 2022

作者： Zhang, Yunfan Li, Meihua Gao, Xiaodong Gu, Yunlong Mou, Chao Ge, Shikun School of Information and Electronic Engineering Shandong Technology and Business University Yantai264005 China

Ammonia is one of the most common gases in life, which is produced in industrial emissions and agricultural activities. Therefore, effective detection of ammonia at low concentrations is vital for human health. Tin disulfide (SnS2) by virtue of its physical affinity and high surface area has become a promising material for gas sensing applications. The flower-like S-doped SnO2 3D hierarchical structure was succeeded in preparation via a two-step method. A variety of methods such as XRD, SEM, and TEM were utilized to characterize the prepared materials for the investigation of their surface morphological characteristics and elemental content. The morphological characterization analysis indicated that the prepared material was a self-assembled hierarchical flower-like SnS2 modified by SnO2 nanoparticles. In addition, the results from the elemental composition analysis showed the existence of Sn, O, and S elements in the material. Through experiments, we further tested the sensitivity, selectivity, and long-term stability of the S-doped SnO2 gas-sensitive material against NH3 at room temperature, and analyzed its sensing mechanism. As a result, the S-doped SnO2 was tested with a response value of 3.6 for 50 ppm ammonia's outstanding performance in terms of selectivity and long-term stability. This work highlights the advantages of flower-like S-doped SnO2 3D hierarchical structures for NH3 sensors. © Published under licence by IOP Publishing Ltd.

关键词： Ammonia

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Research on Visualization of Fragment Power Field and Target detection Method of Detonator 2

Research on Visualization of Fragment Power Field and Target...

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2nd international conference on Algorithms, High Performance Computing and Artificial intelligence, AHPCAI 2022

作者： Luo, Qiang Ma, Yangyang Chen, Kai Yang, Qifan Xi'An Modern Control Technology Research Institute Xi'an710065 China

ISBN: (纸本)9798350334784

The visual models of the static power field of the fragmentation and the dynamic dispersion of the fragmentation are established, and the fragment velocity information is displayed by means of trajectory lines for the static power field of the fragmentation. On the basis of the visual model of the power field of the fragmentation of the kill bomb, the detection and damage calculation of complex structural targets are completed by writing scripts, and the visual display of the damage process and results is completed based on Unity3D platform. © 2022 IEEE.

关键词： Damage detection

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PestinaNet- A Real-Time Crop Pest detection system 2

PestinaNet- A Real-Time Crop Pest Detection System

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2nd international conference on Computing and Machine intelligence (ICMI)

作者： Abid, Hammad Nida, Nudrat Irtaza, Aun Univ Engn & Technol Dept Comp Sci Taxila Pakistan Natl Sci & Technol Park NUST Islamabad Pakistan

ISBN: (纸本)9781665474849;9781665474832

Crop pests are the major factor that reduce the crop yield and influence the health of crops. However, manual identification of pests is time-consuming, and there is a high probability that some pests would go unnoticeable. Therefore, farmer's essential task is to identify, maintain and control the pest's population to ensure crop health and quality yield. In this research, we propose an autonomous pest detection system that uses a deep learning technique to assist farmers in localization of pests. Towards this, we used RetinaNet and modified it for pest detection. The experiments are evaluated on publicly available IP102 dataset comprising 97 classes. The PestinaNet was used for pest detection due to its capability to resolve the issue of class imbalance problem between crop and pest pixels. Our experimental findings reveal a significant performance gain of 51.91% mean average precision(mAP) on the benchmark IP102 dataset in contrast with state-of-the-art methods. The proposed work has practical bearing in the development of an automatic pest detection system.

关键词： Smart Agriculture Crop Pests IP102 PestinaNet Deep learning Crop Pest detection

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Research on Network Security State Prediction Method Based on SVM 2

Research on Network Security State Prediction Method Based o...

引用

2nd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Li, Gen Artificial Intelligence and Data School Guangdong Business and Technology University Zhaoqing Guangdong526020 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

With the rapid development of information technology, the requirements for network security are getting higher and higher. Therefore, it is necessary to set a reasonable algorithm for network security detection and conduct network security detection on big data to ensure authenticity and reliability. This paper analyzes the network security and security factors and establishes a model for the shortcomings of traditional network security prevention algorithms. The accuracy of the security prediction algorithm is finally made universal by further transforming the neural network model, and the accuracy of the network security state detection is higher. © 2022 IEEE.

关键词： Support vector machines

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Deep Learning-based Fault detection in the Tennessee Eastman Process 2

Deep Learning-based Fault Detection in the Tennessee Eastman...

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2nd international conference on Artificial intelligence and Smart Energy, ICAIS 2022

作者： Verma, Rashi Yerolla, Raju Besta, Chandra Shekar National Institute of Technology Calicut

ISBN: (纸本)9781665400527

Nowadays, every sector has at least one fault that must be detected and diagnosed promptly to ensure appropriate monitoring. Numerous Fault detection and Diagnostic (FDD) techniques have been proposed and implemented. In recent years, it has been shown that using Machine Learning (ML) to conduct FDD more precisely and effectively has been demonstrated. Deep Learning (DL) is one such application of machine learning. DL is a set of approaches for modeling data with a complex architecture by combining non-linear processes. The Tennessee Eastman Process (TEP) was used to demonstrate the aforementioned suggested approach. © 2022 IEEE.

关键词： Recurrent neural networks

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Video Recording and Automatic Editing Method Based on Artificial intelligence Algorithm 2

Video Recording and Automatic Editing Method Based on Artifi...

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2nd international conference on Digital Society and Intelligent systems, DSInS 2022

作者： An, Lei Eurasia University Xi'an Shaanxi Xi'an710000 China

ISBN: (纸本)9781510663244

With the rise of short video platforms, video recording and automatic editing technology has become increasingly important to the development of short video, artificial intelligence algorithms can use massive video data to understand the content of time-series video, providing a technical basis for video recording and editing, specifically, video recording and automatic editing methods based on artificial intelligence algorithms already have the corresponding data base, technical basis and application basis In particular, AI algorithm-based video recording and automatic editing methods have the corresponding data, technical and application bases. Therefore, this paper discusses the method of video recording and automatic editing based on artificial intelligence algorithms. This paper firstly describes the automatic video editing process and the extraction of temporal action features, then designs the automatic editing system and the video recording and coding system, and finally analyses the detection speed of the system. The research results demonstrate that the AI algorithm-based automatic editing system passed the concurrency test with good performance and the AI retrieval scheme has a fast detection speed to meet the user's editing needs. © 2023 SPIE.

关键词： Video recording

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A Real Time Energy Meter Fault detection system using Camera comparing with Conventional Fault detection Technique 2

A Real Time Energy Meter Fault Detection System using Camera...

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2nd international conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Rakesh, A. Bhavani, N.P.G. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9781665466431

AIM: The study's major goal is to compare a real-time energy meter fault detection approach to a traditional fault detection technique using a new proposed system of unique remote application. MATERIALS And METHODS: Designing a novel remote application using android studio and python software with 10 samples compared with conventional fault detection technique and significance value is 0.03. The G power is 0.8, the alpha is 0.05, the beta is 0.2 RESULTS: Through this study it is found that a real time energy meter fault detection technique with a novel remote application has better statistical significance value (p © 2022 IEEE.

关键词： Cameras

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An intrusion detection model using extreme learning machine 2

An intrusion detection model using extreme learning machine

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2022 2nd conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Chen, Chen Yang, Bo Ye, Xiaoyan Wang, Biao State Key Laboratory of Astronautic Dynamics Shaanxi Xi’an710043 China Xi’an Satellite Control Center Shaanxi Xi’an710043 China

ISBN: (纸本)9781510663374

In the information age, with the increase of data feature dimensions, the performance of intrusion detection system (IDS) in training time and classification accuracy is declining. A large number of researchers have conducted a lot of research on how to use machine learning algorithms for fast and accurate intrusion detection. This paper presents an IDS model based on Extreme Learning Machine (ELM). Firstly, the intrusion detection data set NSL-KDD is normalized. Then, use the normalized data as input, and the hidden layer activation function of ELM algorithm suitable for intrusion detection is optimized by comparing the correct rate of the model under different hidden layer activation functions and the number of hidden layer nodes. Finally, The optimal number of hidden layer nodes corresponding to the optimal hidden layer activation function is obtained. Experiments show that the optimized ELM has better performance. © 2023 SPIE.

关键词： Intrusion detection

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