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2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献

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Study on hydrodynamic characteristics prediction technology of deep-sea crude oil transfer multi-floating system 深海原油输送多浮式系统水动力特性预测技术研究
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00111
Chen Chen, Hong Zhou, Cheng-Yueh Wu, Yong Ding
{"title":"Study on hydrodynamic characteristics prediction technology of deep-sea crude oil transfer multi-floating system","authors":"Chen Chen, Hong Zhou, Cheng-Yueh Wu, Yong Ding","doi":"10.1109/ICAICE54393.2021.00111","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00111","url":null,"abstract":"In order to solve the motion response characteristics of the multi-floating system contained in the crude oil transfer mode with CTV as the transfer medium under the actual operating sea conditions, and provide a reference for the optimization of the engineering application, the motion characteristics of FPSO, CTV and VLCC were simulated by means of AWQA software, and the influence of sheltering effect, wind load and mooring cable traction on the floating state of multi-floating system was judged and the results of numerical simulation were verified by modal test. The research can provide reference for the prediction of hydrodynamic characteristics of this deep-sea crude oil transfer model.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124482627","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 Object Detection Method of Underground Video Image Based on SSD 基于SSD的地下视频图像目标检测方法研究
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00116
Guangyao Yang, Beizhan Liu, Bo Huang, Zhongqiang Wang
{"title":"Research on Object Detection Method of Underground Video Image Based on SSD","authors":"Guangyao Yang, Beizhan Liu, Bo Huang, Zhongqiang Wang","doi":"10.1109/icaice54393.2021.00116","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00116","url":null,"abstract":"Monitoring video image target detection in coal mine is of great significance to the safety of underground workers. In order to solve the problem of huge task and low efficiency of manual monitoring target, this paper establishes a deep learning model for target detection of underground images. Firstly, the deep neural network is trained by a large number of underground monitoring images, and then different deep learning algorithms are used to detect the target in the image. Finally, the mAP, precision and recall of different neural network target detection are calculated and evaluated, and detection effects of different deep learning detection algorithms are compared by analyzing the detection results. The analysis results show that the four deep learning models in this study have achieved good average accuracy. The target detection effect based on these four deep learning models is more accurate and efficient than other traditional target detection algorithms, which can be applied to target detection in the coal mines.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122688025","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
Application and existing problems of computer network technology in the field of artificial intelligence 计算机网络技术在人工智能领域的应用及存在问题
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00035
Gen-Kuo Chen, Qihong Yuan
{"title":"Application and existing problems of computer network technology in the field of artificial intelligence","authors":"Gen-Kuo Chen, Qihong Yuan","doi":"10.1109/ICAICE54393.2021.00035","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00035","url":null,"abstract":"With the development of science and technology, computer technology is more and more widely used in people's life. As a branch of computer technology, artificial intelligence technology is also gradually developing and expanding, affecting people's life. Nowadays, the processing ability shown by simple computer technology has gradually lagged behind the times. If you want more convenient data processing, artificial intelligence is an excellent solution. Agent technology in artificial intelligence makes artificial intelligence have stronger data processing ability and learning ability than computer technology, and can make decisions quickly according to data information. Artificial intelligence technology can promote automation and intelligence in various industries and improve the production efficiency of enterprises. Starting from the elaboration of artificial intelligence, this paper deeply explores the relationship between artificial intelligence and computer, analyzes the development status of artificial intelligence and computer, and puts forward the technical problems between artificial intelligence and computer, so as to provide reference for the further development of artificial intelligence.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122460448","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
Using machine learning to identify epidemic threshold in complex networks 利用机器学习识别复杂网络中的流行病阈值
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00071
J. Ge, M. Tang
{"title":"Using machine learning to identify epidemic threshold in complex networks","authors":"J. Ge, M. Tang","doi":"10.1109/icaice54393.2021.00071","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00071","url":null,"abstract":"Machine learning is a powerful tool for identifying the phase of matter. Usually when the phase information is fully marked, the direct application of supervised learning can successfully detect phase transitions, while the unsupervised learning method does not require any prior knowledge to distinguish phases of matter, and even discover new phases of matter. Here, we have developed a machine learning framework containing unsupervised learning ideas to identify phase transitions in the dynamics of epidemic spreading in complex networks. The framework trains the neural network so that the configuration information of the epidemic spreading dynamics can describe the effective spread rate, and the accuracy of the effective spreading rate predicted by the neural network can be used as an indicator of phase transition. Tests on a large number of synthetic networks and real networks have proved that the framework has low computational cost, high efficiency, and is suitable for complex networks of any size and topology.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132031362","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
Color Calibration Method for Images Taken by Different Imaging Conditions Which Suppresses False Color 不同成像条件下抑制假色的图像颜色校正方法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00166
Dongsheng Xu, Shi Bao, G. Tanaka, Saoying Ma, Jingping Yang, Fengyun Zuo
{"title":"Color Calibration Method for Images Taken by Different Imaging Conditions Which Suppresses False Color","authors":"Dongsheng Xu, Shi Bao, G. Tanaka, Saoying Ma, Jingping Yang, Fengyun Zuo","doi":"10.1109/ICAICE54393.2021.00166","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00166","url":null,"abstract":"Owing to different imaging conditions, color reproduction of images is usually different; hence, color calibration methods have been developed to unify color reproduction. Iterative distribution transfer (IDT) is one such method that can achieve good color calibration for most pixels of an input image by matching the color distribution of a reference image. This method, however, may lead to unnatural colors, called false colors. In this study, we propose a new color calibration method, which effectively suppresses false colors, whose result is similar to that obtained via the IDT. The input image is projected to specific powers called bases, which are multiplied by the corresponding projection coefficients and added to obtain the projection result. The projection coefficients can be obtained by minimizing the objective function, which is the mean-square error between the projection result and the IDT result. Considering that a false color in the IDT results is expected to be similar to the color of an adjacent pixel, a color-similarity-related item is added to the objective function to address this. Color similarity can be measured by the chromatic aberration between the adjacent pixels of the input image. By ensuring that similar colors in the input image remain similar in the output image, false colors can be suppressed. For the experiment, we use false color index, Kullback-Leibler divergence, and visual evaluation to evaluate the calibration results, which consistently proves that this method offers a better color calibration effect compared with other methods.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132080586","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 Snoring Signal Enhancement Algorithm Based on OM-LSA and Subspace 基于OM-LSA和子空间的打鼾信号增强算法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00042
BinYi Lv, Tieqiang Li, Han Yang, Xia Li
{"title":"A Snoring Signal Enhancement Algorithm Based on OM-LSA and Subspace","authors":"BinYi Lv, Tieqiang Li, Han Yang, Xia Li","doi":"10.1109/ICAICE54393.2021.00042","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00042","url":null,"abstract":"In view of the noise reduction of the snoring signal, a snoring signal enhancement method is proposed in this paper, which is combined with the optimal modified logarithmic spectrum amplitude estimation (OM-LSA) and subspace method. Firstly, the OM-LSA algorithm integrating improved minimum control recursive average (IMCRA) is used for preliminary noise reduction. The method uses short-time window to estimate the minimum value of noise. It uses noise estimation to obtain the optimal spectrum gain function to minimize the mean square error between the actual pure snoring signal power spectrum amplitude and the estimated pure snoring signal power spectrum amplitude to suppress the noise. Then, the subspace method further reduces the noise to make a more compromised choice in suppressing noise and reducing signal distortion. The experimental results show that this method is better than most traditional speech enhancement algorithms in different noise environments and can obtain better snoring signal quality.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130615541","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
An Improved Mutation Operator Which Can Improve the Performance of Genetic Algorithm 一种改进的变异算子，可提高遗传算法的性能
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00025
Yingying Song, Feifei Yan
{"title":"An Improved Mutation Operator Which Can Improve the Performance of Genetic Algorithm","authors":"Yingying Song, Feifei Yan","doi":"10.1109/icaice54393.2021.00025","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00025","url":null,"abstract":"An improved combined mutation operator (CM) is proposed for the problems of premature convergence and local optimization which often occur in genetic algorithm (GA). The CM operator combines the Gaussian mutation and the initial mutation to perform local initialization operations on individuals in the population, and maintain the population diversity while improving the local search ability of the operator. The results of 15 benchmark optimization problems show that the proposed CM operator can effectively improve the performance of the algorithm, and compared with other advanced algorithms, the improved algorithm (IRCGA) has stronger search capabilities and faster convergence speed.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123718577","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
h-Restricted Connectivity of BCuben,k Data Center Networks h .数据中心网络连接受限
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00155
Cui Yu, Fei Gao, Boyong Gao
{"title":"h-Restricted Connectivity of BCuben,k Data Center Networks","authors":"Cui Yu, Fei Gao, Boyong Gao","doi":"10.1109/ICAICE54393.2021.00155","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00155","url":null,"abstract":"h-restricted connectivity of simple graph <tex>$G$</tex> proposed by [1], denoted by k<sup>h</sup>(G), is defined as the minimum number of elements in vertex set <tex>$F$</tex> of <tex>$G$</tex> such that <tex>$G$</tex> - <tex>$F$</tex> is disconnected, and the degree of each vertex in <tex>$G$</tex> - <tex>$F$</tex> is at least h. The n-dimensional BCube with k-port switches denoted as BCube<inf>n,k</inf>, is one of the most attractive data center networks to support the growing needs of cloud computing and big data. The <tex>$h$</tex>-restricted connectivity of a data center network is directly related to its reliability and fault tolerability of it, so it is an important indicator to evaluate the robustness of the network. In this paper, we focus ourselves on h-restricted connectivity of BCube<inf>n,k</inf>, and reveal that k<sup>h</sup>(BCube<inf>n,k</inf>) = pk<sup>r</sup>(k - 1)(n - r - 1) + k<sup>r</sup> (k - p) for h = r(k - 1) + (p - 1), where p < k, k ≥ 2 and n ≥ r + 4, which improves the result in [2].","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969563","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
CTFC: A Convolution and Visual Transformer Based Classifier for Few-Shot Chest X-ray Images CTFC:一种基于卷积和视觉变换的胸部x线图像分类器
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00122
Yufei Jin, Huijuan Lu, Wenjie Zhu, Ke Yan, Zhigang Gao, Zhao Li
{"title":"CTFC: A Convolution and Visual Transformer Based Classifier for Few-Shot Chest X-ray Images","authors":"Yufei Jin, Huijuan Lu, Wenjie Zhu, Ke Yan, Zhigang Gao, Zhao Li","doi":"10.1109/icaice54393.2021.00122","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00122","url":null,"abstract":"While there is only a limited number of samples available for a new disease, few-shot learning is usually adopted in the field of medical image analysis. In this paper, we propose a classifier named CTFC (Convolution and visual Transformer for Few-shot Chest X-ray images). The proposed classifier mainly includes two components, i.e., the feature extractor and the distance metric classifier. First, features are extracted from a small number of support set samples through the visual converter and ResNet50. The features are fused afterwards. Second, feature prototypes are calculated from these support set sample. Finally, the query set samples are classified according to the Euclidean distance. Experiments on open source datasets show that the proposed classifier has advantages in few-shot learning techniques for chest X-ray diagnosis.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114081291","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: 3
Parameter Factorial Weighting of Federated Learning: Center-Client Access Strategy and Application Design 联邦学习的参数因子加权:中心-客户端访问策略与应用设计
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00041
Huan Wang, Zerong Zeng, Ruifang Liu, Sheng Gao
{"title":"Parameter Factorial Weighting of Federated Learning: Center-Client Access Strategy and Application Design","authors":"Huan Wang, Zerong Zeng, Ruifang Liu, Sheng Gao","doi":"10.1109/ICAICE54393.2021.00041","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00041","url":null,"abstract":"Federated learning, using multi-party parameter sharing instead of data centralized training, effectively solves the data privacy and security problems in collaborative training, which has become an important research issue in recent years. In this paper, based on the previously proposed FedBN-PW-CTC model [1], we simulate realistic data and do supplementary experiments to further validate the effectiveness of the model and propose supplementary schemes and application scenarios, with the following main contributions. (1) We compare the training result of FedBN-PW-CTC model on both independently identically distribution (iid) data and non-iid data, and verify the effectiveness of parameter weighting (PW) on non-iid data. (2) We propose an asymmetric center-client access discrimination strategy for federated learning model. (3) A realistic application scenario, an intelligent federated learning-based elderly assistance service system, is proposed for the model, and we design the structure for the system.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774509","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
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