2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献_第7页

A Comparative Study of Multi-objective Flexible Job-shop Scheduling Problem 多目标柔性作业车间调度问题的比较研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731252

Yi Feng, Ya-Tai Lin, Mingliang Wu, Dongsheng Yang, Zhile Yang

引用次数: 0

Complex Scene Tracking Algorithm Based on Multi-feature Fusion 基于多特征融合的复杂场景跟踪算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9730889

Jian-Bo Cheng, Hongfei Xiao, Liang Chen, Pengpeng Yan, Yinan Guo

{"title":"Complex Scene Tracking Algorithm Based on Multi-feature Fusion","authors":"Jian-Bo Cheng, Hongfei Xiao, Liang Chen, Pengpeng Yan, Yinan Guo","doi":"10.1109/acait53529.2021.9730889","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9730889","url":null,"abstract":"At present, natural disasters of coalmine in China are still serious, and coalmine safety guarantee capabilities are still relatively low. In recent years, a lot of manpower and material resources have been implemented to upgrade the coalmine monitoring system, but manual operation has limited their applications and effects, the efficiency is low and the error rate is high. The application of computers for intelligent tracking and monitoring of dynamic object can greatly reduce the waste of manpower, improve monitoring efficiency, and provide more reliable and concise safety early warning and system linkage than manual operation. In addition, object tracking can provide information support for behavior understanding, event detection, object classification, its importance is self-evident. The object tracking algorithm has achieved good results in open scenes with rich features and sufficient illumination. However, in confined spaces such as coalmine and tunnels, due to unfavorable factors such as coal dust, lack of illumination, scale changes, and image blur-ring, etc. The stability and robustness of tracking are greatly affected. In this paper, the deep network model is used to extract the depth features of the tracking target, the depth features and the results of the HOG feature location filter are weighted and merged to obtain the final target location. The algorithm has better tracking stability and robustness in the case of insufficient illumination and scarce feature points in coalmines. The dynamic object tracking experiment is carried out on the coalmine monitoring video. The experimental results show that the algorithm is more robust than the comparison algorithm and can meet the needs of object tracking in complex scenes in coalmine.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122218494","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

Location estimation method based on circular target by UAV’s monocular 基于圆形目标的无人机单目定位方法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731175

H. Wang, Xuan Pei, Jiaxin Li, Shijie Zhang, Tianmiao Wang, Taogang Hou

引用次数: 1

TDNN:A Tensor Decomposition Adversarial Defense Method Based on Neural Network 基于神经网络的张量分解对抗防御方法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731274

Wei He, Bingbing Song, Ruxin Wang, Wenyu Peng, Shenghong He, Wei Zhou

{"title":"TDNN:A Tensor Decomposition Adversarial Defense Method Based on Neural Network","authors":"Wei He, Bingbing Song, Ruxin Wang, Wenyu Peng, Shenghong He, Wei Zhou","doi":"10.1109/acait53529.2021.9731274","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731274","url":null,"abstract":"In recent years, neural networks have shown strong performance on various tasks. However, neural networks show the vulnerability to carefully designed noise of adversarial examples. Through research, it is found that the neural networks usually have good robustness to common noise, but almost no resistance to carefully designed imperceptible perturbations noise of adversarial examples. To solve this problem, related works have proposed to transform the noise of the adversarial sample into random ordinary noise, which greatly protects the model from adversarial attack. To solve this problem, we propose an adversarial defense method based on tensor decomposition, which use tensor decomposition technology to decompose and reconstruct the image, and retain the main features of the image and remove the perturbation of adversarial examples. Based on traditional tensor decomposition method, we further propose the tensor decomposition of neural networks method (TDNN). Compared with traditional tensor decomposition, TDNN has better defense effect and lower running time. Beside TDNN can be combined with existing defense methods and does not require extra changes for model. Through Rigorous experiments show that TDNN can remove carefully added perturbation and greatly improve the robustness of the model.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123863130","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

Dual fusion paired environmental background and face region for face anti-spoofing 基于环境背景和人脸区域双融合的人脸防欺骗算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731173

Xin Huang, Qin Huang, Nan Zhang

{"title":"Dual fusion paired environmental background and face region for face anti-spoofing","authors":"Xin Huang, Qin Huang, Nan Zhang","doi":"10.1109/acait53529.2021.9731173","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731173","url":null,"abstract":"Face anti-spoofing is a key phase in the face recognition process, where threats come from various deception attacks. Previously, traditional methods and deep supervised learning methods were shown to be effective in face anti-spoofing, but most previous work only focused on a single application scenario, ignoring the importance of face anti-spoofing methods for generalization ability across different applications scenarios. As a result, we propose a new face anti-spoofing method based on misleading attack information found in the face area and maybe in the environmental backdrop. The convolutional neural network extracts deception attack information from the global picture, while the local feature descriptor extracts deception attack information from the face area. The dual-cue fusion method efficiently mitigates detector performance loss caused by changes in the detection backdrop. We conduct several trials using CelebA-Spoof, WMCA, and 3DMAD datasets to demonstrate the efficiency of our technique. The findings reveal that our solution is capable of dealing with the majority of assaults and has a high degree of generality for various application scenarios.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129931337","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

A Sliding Mode Tracking Control Method for Double Pendulum Crane Systems With Variable Rope Length 变绳长双摆起重机滑模跟踪控制方法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731285

He Chen, Xinya Yao

引用次数: 1

Zero-Watermarking Algorithm for Medical Images Based on Nonsubsampled Contourlet Transform and Double Singular Value Decomposition 基于非下采样Contourlet变换和双奇异值分解的医学图像零水印算法

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731179

Tongyuan Huang, Jia Xu, Yuling Yang, Shixin Tu, B. Han

{"title":"Zero-Watermarking Algorithm for Medical Images Based on Nonsubsampled Contourlet Transform and Double Singular Value Decomposition","authors":"Tongyuan Huang, Jia Xu, Yuling Yang, Shixin Tu, B. Han","doi":"10.1109/acait53529.2021.9731179","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731179","url":null,"abstract":"The privacy and security of medical images in storage and transmission are enormous challenges. In this paper, we propose a novel multi-algorithm fusion of medical image robust zero-watermarking algorithm based on the nonsubsampled contourlet transform (NSCT), double singular value decomposition (DSVD), and multi-level discrete cosine transform (MDCT). The NSCT of the original medical image obtains low-frequency domain information, blocks the low-frequency domain information and uses the MDCT to obtain the coefficient matrix of the low-frequency direction sub-bands, and then uses the DSVD to construct the feature vector. At the same time, the watermarking is encrypted using Logistic Map to ensure the security of the original watermarking information under the chaotic system. In the watermarking embedding and extraction phase, zero-watermarking technology is used to ensure the integrity of medical images. Experimental results show that the proposed algorithm can extract watermarking information effectively and accurately with fast calculation speed and high precision, and it has good invisibility and strong robustness for both common attacks and geometric attacks.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163707","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

Research on BIM intelligent management technology of wharf construction schedule based on Improved Genetic Algorithm 基于改进遗传算法的码头施工进度BIM智能管理技术研究

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731118

Y. Jia, Haifeng Wang

引用次数: 0

Design of MSPRN Target Recognition Algorithm for Vehicle Automatic Driving 车辆自动驾驶中MSPRN目标识别算法的设计

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731257

Min Yang

引用次数: 1

Regional growth inpainting strategy for depth image 深度图像的区域增长绘制策略

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731273

Zhengyang Chen, G. Chen, Zaizuo Tang, Bo Hu

{"title":"Regional growth inpainting strategy for depth image","authors":"Zhengyang Chen, G. Chen, Zaizuo Tang, Bo Hu","doi":"10.1109/acait53529.2021.9731273","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731273","url":null,"abstract":"The depth images acquired from depth sensors have inherent problems, such as missing depth values and noisy boundaries. In this paper, an inpainting strategy for depth image based on regional growth criterion is proposed. In terms of image inpainting sequence, based on Criminisi priority method, a new calculating method of confidence item is defined and the improved priority is used to weight the confidence items and data items. Compared with the inpainting results of Joint bilateral filter (JBF), JBFC (JBF based on Criminisi priority), and JBFW (JBF based on weighted pixel priority), the inpainting sequence and texture extension determined by weighted pixel priority are better. In terms of image inpainting field, Under the guidance of the weighted pixel priority, the adaptive neighborhood of the pixel to be inpainted is defined by region growth criterion which have higher similarity with the pixel to be inpainted than by traditional neighborhood. Experimental results show that the neighborhood constructed by region growth criterion is accurate and effective. Generally, the regional growth inpainting strategy can obtain higher inpainting accuracy while keeping the boundary information","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132415771","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