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2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)最新文献

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Approximate Model Checking based on Deep Forest 基于深度森林的近似模型检验
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930208
Weijun Zhu
{"title":"Approximate Model Checking based on Deep Forest","authors":"Weijun Zhu","doi":"10.1109/AICIT55386.2022.9930208","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930208","url":null,"abstract":"Some classical Machine Learning (ML) algorithms have been applied to predict model checking results, while a data set contains several thousands of samples. However, the power of prediction will reduce sharply when the scale of dataset is bigger. To this end, some Deep Learning (DL) algorithms are employed in this study. First, a part of samples are inputted to a DL algorithm. Second, the obtained DL model can be used to predict model checking results. Our experiments demonstrate that Deep Forest (DF) has the better performance when one million samples are used, compared with the classical ML algorithms and the deep learning based on deep neural network.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127732276","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: 1
Path planning of improved DQN based on quantile regression 基于分位数回归的改进DQN路径规划
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930247
Lun Zhou, Ke Wang, Hang Yu, Zhen Wang
{"title":"Path planning of improved DQN based on quantile regression","authors":"Lun Zhou, Ke Wang, Hang Yu, Zhen Wang","doi":"10.1109/AICIT55386.2022.9930247","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930247","url":null,"abstract":"To solve the problems of slow convergence and overestimation of the value of quantile regression-deep reinforcement learning algorithm, a Dueling Double Depth Q algorithm based on quantile regression (QR-D3QN) was proposed. Based on QR-DQN, the calculation method of the target Q value is modified to reduce the influence of value overestimation. Combining the confrontation network and adding preferential experience sampling to improve the utilization efficiency of effective data. It is verified by the ROSGazebo simulation platform that the robot can effectively select actions, get a good strategy, and can quickly avoid obstacles and find the target point. Compared with D3QN, the route planned by the robot is shortened by 4.95%, and the obstacle avoidance path is reduced by 18.8%.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116667276","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: 1
Image Classification Based On Pcanet And Salient Feature Fusion 基于Pcanet和显著特征融合的图像分类
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930220
Yanfei Chen, Yuliang Huang, Zhangchen Yan, G. Wang, Tiange Huang, Jinhu Hu
{"title":"Image Classification Based On Pcanet And Salient Feature Fusion","authors":"Yanfei Chen, Yuliang Huang, Zhangchen Yan, G. Wang, Tiange Huang, Jinhu Hu","doi":"10.1109/AICIT55386.2022.9930220","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930220","url":null,"abstract":"Aiming at the shortcomings of traditional image classification model in extracting features, we propose an improved color contrast algorithm to extract higher quality saliency map. We first analyze the feature extraction ability of HC saliency algorithm in image classification and improve it by adding the location information, then we propose a novel features fusion module to combine the saliency map with the output features from PCANet to enhance the feature expression, contributing to classification capability of the model. The accuracy on Caltech101 and Pascal VOC2007 can achieve excellent performance by using our method.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122523908","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
AGV Path planning based on improved adaptive genetic algorithm 基于改进自适应遗传算法的AGV路径规划
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930180
Wei Zhou, Shihong Qin, Can Zhou
{"title":"AGV Path planning based on improved adaptive genetic algorithm","authors":"Wei Zhou, Shihong Qin, Can Zhou","doi":"10.1109/AICIT55386.2022.9930180","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930180","url":null,"abstract":"In order to solve the path planning problem of Automated Guided Vehicle (AGV) transporting goods and packages in warehouse logistics, this paper studies the Algorithm of the path planning problem, an improved adaptive genetic algorithm is presented to solve the path optimization problem. In order to avoid falling into local optimum, an improved self-adaptive crossover method is proposed. In order to avoid the conflict of AGV in the process of path planning, the concept of Congestion Coefficient is introduced into the design of fitness function, reduce the AGV in the path optimization process of conflict. The mathematical model of AGV to accomplish multi-task is established, and the comparative experiment is set up. The experimental results show that the improved adaptive genetic algorithm can improve the optimization performance by 4.2% in solving the optimal path problem, and improve the convergence speed by 4.2%, the improved algorithm has obvious advantages.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128123012","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: 4
Gradient-guided GAN for dynamic scene deblurring 梯度引导GAN动态场景去模糊
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930290
Zhigao Huang, Yixin Zhou, Yu Shi, Jisong Chen, Ting Lai, C. Shao
{"title":"Gradient-guided GAN for dynamic scene deblurring","authors":"Zhigao Huang, Yixin Zhou, Yu Shi, Jisong Chen, Ting Lai, C. Shao","doi":"10.1109/AICIT55386.2022.9930290","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930290","url":null,"abstract":"Dynamic scene blur, mainly caused by camera shake and motions, is one of the most common causes of image degradation. Recent GAN-based strategies have performance on deblurring tasks. To further improve the performance of GAN-based approaches on deblurring tasks, we propose Gradient-guided GAN for dynamic scene deblurring, it includes image restoration branch and gradient branch, which uses the gradient as a guide to supervise the restoration process. In particular, perform an attention fusion of feature image generated by restoration branch and gradient feature image generated by gradient branch, which using gradient information to guide the network to fully learn the deep feature information. Extensive experiments on GOPRO dataset show that our method achieve state-of-the-art performance in dynamic scene deblurring.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128482144","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
DDRICFuse:An Infrared and Visible Image Fusion Network Based on Dual-branch Dense Residual And Infrared Compensation DDRICFuse:一种基于双分支密集残差和红外补偿的红外与可见光图像融合网络
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930162
Ke Wang, Lun Zhou, Han Yu, Zhen Wang
{"title":"DDRICFuse:An Infrared and Visible Image Fusion Network Based on Dual-branch Dense Residual And Infrared Compensation","authors":"Ke Wang, Lun Zhou, Han Yu, Zhen Wang","doi":"10.1109/AICIT55386.2022.9930162","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930162","url":null,"abstract":"Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress in recent years. In this paper, we implement a dual-branch dense residual infrared and visible image fusion network based on auto-encoder. Specifically, the encoder has two branches that extract the shallow features and deep features of the image, respectively. The fusion layer adopts the residual block to fuse the two sets of features from the same branch of infrared and visible image to get fused features. The decoder is utilized to generate a fused image. To improve the overall performance of the fusion image, an infrared feature compensation network is added that can compensate salient radiation features of the infrared image. Experimental results show that our proposed method achieves reasonable performance compared with other state-of-the-art image fusion methods on structural similarity.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133732825","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
AICIT 2022 Cover Page aicit2022封面页
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/aicit55386.2022.9930172
{"title":"AICIT 2022 Cover Page","authors":"","doi":"10.1109/aicit55386.2022.9930172","DOIUrl":"https://doi.org/10.1109/aicit55386.2022.9930172","url":null,"abstract":"","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360823","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
Fuzzy self-tuning control strategy for PWM rectifier voltage fast response PWM整流器电压快速响应的模糊自整定控制策略
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930198
Zixin Huang, Wei Wang, Jie Deng, Yingying Li, Lejun Wang, Zicheng Li
{"title":"Fuzzy self-tuning control strategy for PWM rectifier voltage fast response","authors":"Zixin Huang, Wei Wang, Jie Deng, Yingying Li, Lejun Wang, Zicheng Li","doi":"10.1109/AICIT55386.2022.9930198","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930198","url":null,"abstract":"Aiming at the voltage response rate of the traditional dual closed-loop control system of the three-phase voltage type pulse width modulation(PWM) rectifier on the direct current(DC) side. In this paper, Based on the underactuated system theory, the DC voltage control process of PWM rectifier is studied theoretically. The mathematical model of PWM rectifier in rotating coordinate system is established, and its underactuated characteristics are analyzed, a cascade double closed loop control structure is proposed. A current inner loop controller is designed using partial feedback linearization strategy. In order to improve the DC side voltage response speed, the fuzzy control of voltage outer loop is proposed. The PWM rectifier c ontrol system designed in this paper has better steady-state and dynamic performance, better improve the DC side voltage response rate. The simulation results prove the effectiveness of the designed control scheme.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131054624","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
Design of PROFINET-based communication system for rice processing line 基于profinet的大米加工线通信系统设计
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930324
Shuang Liu, Jin Zhou, Yonglin Zhang
{"title":"Design of PROFINET-based communication system for rice processing line","authors":"Shuang Liu, Jin Zhou, Yonglin Zhang","doi":"10.1109/AICIT55386.2022.9930324","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930324","url":null,"abstract":"The communication system of rice processing production based on PROFINET real-time industrial Ethernet is designed to address the problems of difficulty in communication between equipment, low data transmission rate and poor data security caused by various types of communication protocols and different communication protocols between host equipment in the rice processing production line. The article expounds the application of PROFINET IO communication scheme in the control layer and field layer in the network topology of rice processing production line, and explains in detail the communication implementation method of applying PROFINET among equipment at each level; By building a PROFINET tree topology, the whole communication system is divided into three layers, which makes the communication structure clear and concise, and enhances the transmission capability of the overall network of the communication system and the availability and security of data in the local star network. The design aims to improve the rapidity, stability and security of information flow in the production control system and realize the unification of communication in the production communication system. It is well commissioned in the experimental environment and is intended to be applied in the actual production line.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116157880","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
Maritime Vessel Detection and Tracking under UAV Vision 无人机视觉下的船舶检测与跟踪
2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) Pub Date : 2022-09-16 DOI: 10.1109/AICIT55386.2022.9930166
Yongshuai Li, Haiwen Yuan, Yuan Wang, Bulin Zhang
{"title":"Maritime Vessel Detection and Tracking under UAV Vision","authors":"Yongshuai Li, Haiwen Yuan, Yuan Wang, Bulin Zhang","doi":"10.1109/AICIT55386.2022.9930166","DOIUrl":"https://doi.org/10.1109/AICIT55386.2022.9930166","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are playing an important role in the development of smart maritime. However, images crowned with small-sized and highly dense cause the accuracy to decrease for ship detection under UAV vision. Aiming at the problem, this paper proposes an improved YOLOv5 to detect ships accurately under UAV vision and combines with deepsort to realize ship tracking. Firstly, we add a detection layer to make full use of shallow features with rich detail information in the part of feature fusion. Then, the coordinate attention is introduced in YOLOv5 to focus on more important feature information. The test results show that the accuracy, recall and average precision of the proposed SA-YOLOv5 are improved by 3.4%, 0.3% and 1.0% compared with YOLOv5. Finally, the deepsort is used as the tracker to realize the real-time ship tracking under UAV vision.","PeriodicalId":231070,"journal":{"name":"2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365643","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: 1
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