International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)最新文献_第8页

Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation Retinex- sie:基于Retinex和同态滤波变换的自监督微光图像增强方法

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671157

Jiachang Yang, Qin Cheng, Jianming Liu

{"title":"Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation","authors":"Jiachang Yang, Qin Cheng, Jianming Liu","doi":"10.1117/12.2671157","DOIUrl":"https://doi.org/10.1117/12.2671157","url":null,"abstract":"Low-light images suffer from low visibility, much noise, uneven illumination distribution, etc. Many existing methods have problems such as over enhancement or insufficient detail enhancement when dealing with low-light images with uneven illumination distribution. To remedy the above shortcomings, we propose a Retinex-based self-supervised low-light image enhancement model (Retinex-SIE), which is mainly composed of three parts: Retinex-based self-supervised image decomposition network (Retinex-DNet), nonlinear conditional illumination enhancement function (NCIEF), and Image Reconstruction (IR). First, a uniform illumination image of the same scene with the low-light image is generated by homomorphic filtering transformation, and the low-light image and the uniform illumination image are input into Retinex-DNet for decomposition to obtain reflectivity, noise and illumination. Then, NCIEF is used to enhance the illumination after decomposition. Finally, the final enhanced image is obtained by multiplying the decomposed reflectance and the enhanced illumination. Experiments on severa challenging low-light image datasets show that Retinex-SIE proposed in this paper can better handle low-light images with uneven illumination distribution and avoid problems such as excessive enhancement or insufficient detail enhancement.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122630339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep neural network prediction method of ring network tank life based on internal discharge characteristics 基于内放电特性的环网罐寿命深度神经网络预测方法

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671172

Jianbing Pan, Yanwu Yu, Xiaoping Yang, Zhixiang Deng, Yuxiang Hao, Zaide Xu

{"title":"Deep neural network prediction method of ring network tank life based on internal discharge characteristics","authors":"Jianbing Pan, Yanwu Yu, Xiaoping Yang, Zhixiang Deng, Yuxiang Hao, Zaide Xu","doi":"10.1117/12.2671172","DOIUrl":"https://doi.org/10.1117/12.2671172","url":null,"abstract":"In order to improve the effect of ring network cabinet life prediction, the deep neural network life prediction method based on the characteristics of internal distribution of ring network cabinet is studied. Using the optimal wavelet packet transform method, the local discharge characteristics of ring network cabinet are extracted. Nuclear principal component analysis was used to reduce dimension to deal with the local discharge characteristics of ring network cabinet. The bidirectional long-term memory deep neural network was established. The local distribution characteristics after dimensionality reduction were input into the network and the autoregressive comprehensive moving average model, and the life prediction results of the ring network cabinet with nonlinear and linear characteristics were output. The final life estimation results are obtained by combining the two estimation results. Experimental results show that the algorithm can effectively extract and reduce the dimension of the internal local discharge features of ring network cabinet. It can accurately predict the service life of the ring network cabinet under different types of local distribution. Under different local discharge intensities, the R-square coefficient of the algorithm for predicting the life of the ring network cabinet is higher, which has better prediction effect.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124042034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification algorithm based on convolutional neural network for wild fungus 基于卷积神经网络的野生真菌分类算法

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671050

Yingyuan Du, Tao Wu, Gaoyuan Yang, Yuwei Yang, Ge Peng

引用次数: 0

Software technology analysis based on wearable devices 基于可穿戴设备的软件技术分析

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671398

DongRui Mao, Yuheng Sui, Sihan Wang

{"title":"Software technology analysis based on wearable devices","authors":"DongRui Mao, Yuheng Sui, Sihan Wang","doi":"10.1117/12.2671398","DOIUrl":"https://doi.org/10.1117/12.2671398","url":null,"abstract":"In recent years, due to the development of Internet technology, more and more people begin to connect their daily life with internet technology. Due to its intelligence, simple operation and daily characteristics, wearable devices have gradually entered the public's vision and been accepted by the public. More and more wearable devices appear with a variety of supporting software. This thesis explores how this software use different middleware for different functions, design the UI interface to make people easy to use and use different ways to protect user privacy. To analyze the advantages and disadvantages of the existing software products of wearable devices and propose changes and improvements for future software development, firstly find out some existing wearable device software, and analyze its data processing, functions, adopted protocols, user privacy management, and other aspects. Secondly, through searching corresponding thesis and questionnaires, finds users' dissatisfaction with existing wearable devices. Finally, based on the feedback of users, the data processing, privacy protection and other functions of the existing wearable device software are summarized, and the corresponding direction for the future improvement of wearable devices is put forward.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121255666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV obstacle avoidance based on improved artificial potential field method 基于改进人工势场法的无人机避障方法

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671404

Y. Fan, Yuan Li, X. Li

引用次数: 0

A DDoS attack detection method based on AE network in the internet of vehicles 基于AE网络的车联网DDoS攻击检测方法

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671449

Shiwen Shen, Yuqiao Ning, Mingming Yu, Zhen Guo, Shihao Xue, Qingyang Wu

{"title":"A DDoS attack detection method based on AE network in the internet of vehicles","authors":"Shiwen Shen, Yuqiao Ning, Mingming Yu, Zhen Guo, Shihao Xue, Qingyang Wu","doi":"10.1117/12.2671449","DOIUrl":"https://doi.org/10.1117/12.2671449","url":null,"abstract":"With the rapid development of 5G technology, the Intelligent and Connected Vehicle (ICV) technology is also evolving and expanding its application scenarios. In order to achieve lower latency and reduce the network load caused by massive data reflow in ICV, MEC (Mobile Edge Computing) technology is introduced to support ICV communication. While MEC technology brings a good experience to users, more and more attacks against Telematics come along, the most common of which is DDoS attacks, which can bring huge losses to telematics systems. Based on this, this paper proposes a DDoS attack detection method based on SAE neural network. The method uses the stacked Auto-encoder-based model proposed in the paper to detect network traffic in the telematics network, feeds the traffic data into the test model, and determines whether the automotive network system is under DDOS attack based on a threshold value. The DDoS attack is detected using the method proposed in the paper, with high detection rates in the training and test sets and stable models. Better experimental results were also obtained by later changing the number of hidden layers in the SAE network to detect DDoS attacks. Comparing the method in this paper with the SVM and CNN methods, the experimental results show that the DDoS attack detection method based on SAE networks works best.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116326641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand gesture recognition using multi-sensor information fusion 基于多传感器信息融合的手势识别

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671270

Aiguo Wang, Huancheng Liu, Jingyu Yan

{"title":"Hand gesture recognition using multi-sensor information fusion","authors":"Aiguo Wang, Huancheng Liu, Jingyu Yan","doi":"10.1117/12.2671270","DOIUrl":"https://doi.org/10.1117/12.2671270","url":null,"abstract":"Accurately recognizing hand gestures has great significance in assisting human-computer interaction, enhancing user experience, and developing a human-centered ubiquitous system. Due to the inherent complexity of hand gestures, however, how to capture discriminant features of hand motions and build a gesture recognition model remains crucial. To this end, we herein propose a gesture recognition method based on multi-sensor information fusion. Specifically, we first use the accelerometer and surface electromyography (sEMG) sensor to capture the kinematic and physiological signals of hand motions. Afterward, we utilize the sliding window technique to segment the streaming sensor data and extract various features from each segment to return a feature vector. We then optimize a gesture recognition model with the feature vectors. Finally, comparative experiments are conducted on the collected dataset in terms of different machine learning models, different sensors, as well as different types of features. Results show the joint use of sEMG sensor and accelerometer achieves the average accuracy of 97.88% compared to the 90.38% of using sEMG sensor and 84.03% of using accelerometer among four classifiers, which indicates the effectiveness of multi-sensor fusion. Besides, we quantitatively investigate the impact of null gesture on a gesture recognizer.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on pattern recognition and retrieval of reliance based on deep learning 基于深度学习的模式识别与依赖检索研究

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671521

Chunxia Zhang, Guoyun Zhang

引用次数: 0

Aquila optimizer integrating Gaussian walk and somersault strategy Aquila优化器集成高斯行走和翻跟头策略

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671237

Qiuxiang Yu, Kuntao Ye

引用次数: 0

Nonlinear programming based liner shipping route optimization under ECA restriction 基于非线性规划的ECA约束下班轮航线优化

International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) Pub Date : 2023-04-28 DOI: 10.1117/12.2671177

Qiulan Wang

引用次数: 0