2020 13th International Symposium on Computational Intelligence and Design (ISCID)最新文献

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Research on the Safety of Sea Crab Based on Machine Olfactory 基于机器嗅觉的海蟹安全性研究
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00054
Y. Liu, Chunya Wang, Xinxin Yuan, Tingting Xiong
{"title":"Research on the Safety of Sea Crab Based on Machine Olfactory","authors":"Y. Liu, Chunya Wang, Xinxin Yuan, Tingting Xiong","doi":"10.1109/ISCID51228.2020.00054","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00054","url":null,"abstract":"In recent years, with the development of the national economy and the improvement of living standards, the output and consumption of seafood in my country have increased. However, the new crown virus that broke out in early 2020 is very easy to survive in humid and cold environments, so seafood has become Its best food carrier. Both the government and consumers are paying more and more attention to the inspection of the hygienic quality and safety of seafood. Freshness is an important indicator of the quality of seafood products, and it is very important to the final quality of the products. This paper selects fresh sea crabs that have just been salvaged from the sea and sea crabs stored under refrigerated conditions as experimental materials, uses an electronic nose to detect the freshness of sea crabs, and analyzes and explores the use of electronic nose for sea crab freshness through a combination of simulated annealing and genetic algorithms The feasibility of degree detection, and the establishment of the best detection and pattern recognition methods, qualitative and quantitative analysis of the freshness of sea crabs.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125239243","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
Digital Image Encryption and Decryption Algorithm Based on Optimization and Fusion Strategy 基于优化与融合策略的数字图像加解密算法
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00061
Nan Wan, Yi Zhang
{"title":"Digital Image Encryption and Decryption Algorithm Based on Optimization and Fusion Strategy","authors":"Nan Wan, Yi Zhang","doi":"10.1109/ISCID51228.2020.00061","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00061","url":null,"abstract":"Digital image chaotic system has the characteristics of large key space and strong anti-plaintext attack, so it has become a hot research topic in recent years. At present, there are still two important problems to be solved. One is that the chaos degree of chaotic sequence is not enough due to improper selection of initial parameters and the other is easy to solve by using chaotic encryption and decryption alone. In this paper, a digital image encryption and decryption algorithm based on optimization and fusion strategy is proposed. Firstly, the parameters of chaos parameters are optimized by genetic algorithm to obtain a better chaotic Logistic mapping encryption relationship. Then, the fusion encryption is carried out by two-level permutation and one-level diffusion technology. Finally, the corresponding decryption algorithm is given in this paper. Through the test of various Performance evaluation indexes, the rationality and validity of the proposed algorithm are verified.electronic document is a \"live\" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"45 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127247906","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
Dog Breed Classification Based on Deep Learning 基于深度学习的犬种分类
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00053
Changqing Wang, Jiaxiang Wang, Quancheng Du, Xiangyu Yang
{"title":"Dog Breed Classification Based on Deep Learning","authors":"Changqing Wang, Jiaxiang Wang, Quancheng Du, Xiangyu Yang","doi":"10.1109/ISCID51228.2020.00053","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00053","url":null,"abstract":"Deep learning is part of the field of artificial intelligence. It has powerful feature extraction and learning capabilities. Because of its various advantages, it has been applied in many fields. Object detection is an important technology in deep learning, and object detection based on deep learning has also been studied by many people. With the gradual improvement of people's living standards, pets have gradually received people's love, among which pet dogs occupy the majority. Different types of pet dogs will bring different problems. For example, large pets may be more aggressive and cause problems for city management. If dangerous pets can be distinguished in time, it can bring more security to people and avoid some people being bitten by pet dogs. In the deep learning algorithm, YOLOv3 has better object detection performance, but it only targets different species and objects, and the classification of different categories of specific species is not good enough. In daily life, the body of the pet dog is sometimes hidden by the complicated background, which makes it difficult to extract the overall characteristics of the pet dog. At this time, the facial features of the pet dog can be fully utilized to distinguish the pet dog. In order to solve this problem, this paper proposes an improved yolov3 model for face detection and categorization of pet dogs. This paper establishes a data set of 8 different kinds of pet dogs. The data set is divided into training set and a test set, and the training set is sent to the established model for training. Finally, we use the test set to verify the effect of the model. This paper establishes a data set of 8 different kinds of pet dogs. Pet dog types include Akita, Golden Retriever, Poodle, Pomeranian, Samoyed, Corg, Pug, and Husky. Experiments show that this paper can realize the detection and classification of pet dogs with high detection speed and accuracy, and mAP(mean Average Precision) can reach 94.91%.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452125","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: 5
A Method of Relation Extraction Using Pre-training Models 一种基于预训练模型的关系提取方法
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00046
Yu Wang, Yining Sun, Zuchang Ma, Lisheng Gao, Yang Xu, Yichen Wu
{"title":"A Method of Relation Extraction Using Pre-training Models","authors":"Yu Wang, Yining Sun, Zuchang Ma, Lisheng Gao, Yang Xu, Yichen Wu","doi":"10.1109/ISCID51228.2020.00046","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00046","url":null,"abstract":"Relation Extraction (RE), as an essential task of Natural Language Processing (NLP), aims to extract potential relations between two entities in a sentence. It is a crucial step in information extraction from unstructured data and building a Knowledge Graph (KG). The performance of deep learning methods for RE, like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), heavily depends on the quality and scale of the training set. Recently, pre-training models like BERT and ERNIE, have achieved State-Of-The-Art (SOTA) results in many NLP tasks, because they can obtain the prior semantic knowledge during the procedure of pre-training. Therefore, it is interesting to know whether the performance of RE can be improved utilizing the pre-training models. In this paper, we propose a method of RE using two kinds of pre-training models: BERT and ERNIE. First, in the input sequence, unique symbols are appended around the entities. RE is then regarded as a text classification task, and the prior semantic knowledge obtained by pre-training models is used to improve the performance. Experiments are carried on the SemEval 2010 Task 8 dataset. Results demonstrate that the method we proposed improves the performance of RE compared with previous approaches.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993551","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: 2
Research on Pet Dog Species Identification Based on Convolution Neural Network 基于卷积神经网络的宠物犬种类识别研究
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00068
Yanmei Liu, Yuda Chen
{"title":"Research on Pet Dog Species Identification Based on Convolution Neural Network","authors":"Yanmei Liu, Yuda Chen","doi":"10.1109/ISCID51228.2020.00068","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00068","url":null,"abstract":"At present, the related research of image recognition is getting more and more popular, but in the process of research, the recognition effect of the model is not good enough and it is easy to misrecognize. This paper proposes an improvement solution for the above problems on the selection and construction of the model structure and the adjustment and optimization methods in the model training process. The final result achieves 96% recognition accuracy on the data composed of 9092 pet dog images. It is proved that the model by choosing deep-level network model and adopts regularization method to adjusting and optimizing the model, which can effectively improve model for image recognition effect.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121952948","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
Application of improved k-means k-nearest neighbor algorithm in the movie recommendation system 改进的k-means k-近邻算法在电影推荐系统中的应用
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00076
Chang-Ping Cai, Li Wang
{"title":"Application of improved k-means k-nearest neighbor algorithm in the movie recommendation system","authors":"Chang-Ping Cai, Li Wang","doi":"10.1109/ISCID51228.2020.00076","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00076","url":null,"abstract":"In this paper, we propose a clustering and reclassification method for movie recommendation. We use the improved K-means algorithm to cluster according to the scores of similar users, Firstly, the elbow function is used to estimate the number of clusters, and the elbow method is used to determine the K value. Then, the K-means algorithm of the maximum and minimum distance method is used to select the initial cluster center, and finally the cluster and cluster center are obtained. According to the similarity between the test data of the user's rating and user's personal information and the clustering center, they are divided into the cluster to which they belong, and the sample set in the cluster is used as the training set for K-nearest neighbor classification.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125596700","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: 7
Two families of LRCs with availability based on iterative matrix 基于迭代矩阵的两类具有可用性的lrc
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00081
Mao Zhang, Ruihu Li
{"title":"Two families of LRCs with availability based on iterative matrix","authors":"Mao Zhang, Ruihu Li","doi":"10.1109/ISCID51228.2020.00081","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00081","url":null,"abstract":"Locally repairable code (LRC) in distributed storage system decreases repair degree of failed nodes. LRC with availability is extremely desired in distributed storage system because it permits local repair of failed nodes and parallel access of hot data. In this paper, a novel construction of LRCs with availability is proposed. Explicitly, by matrix iteration, two families of LRCs with all symbol locality and availability are constructed. The first family LRC is SA-LRC and keeps the code structure binary which is convenient to apply. The second family LRC is systematic code and possesses inspiring information rate $frac{r}{{r + 2}}$. Our construction is concise and explicit parity-check matrices of LRCs are given.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124365309","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 Method of Establishing Evaluation Model of Online Teaching Interactive Behavior 网络教学互动行为评价模型的建立方法
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00056
Defu Bao, Junhao Jiang, Hanze Xiao, Danni Shen
{"title":"A Method of Establishing Evaluation Model of Online Teaching Interactive Behavior","authors":"Defu Bao, Junhao Jiang, Hanze Xiao, Danni Shen","doi":"10.1109/ISCID51228.2020.00056","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00056","url":null,"abstract":"This paper aims to establish the online education interaction triangle model and the online education interaction evaluation model between the teacher, the student, and the computer, and demonstrate its feasibility. It elaborates the interactive relationship formed by the teaching media (computer) under the online education environment and evaluates the interaction in the existing online teaching by establishing and quantifying the interactive evaluation index in the online interaction, identifying the validity of the interaction, establishing the index system, and evaluating the resulting base. The purpose of this paper is to provide model support for the existing evaluation of online teaching to help improve the current online teaching interaction of lower quality.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017818","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
Improving the identification of finger movements using high-density surface electromyography pre-processed with PCA 基于PCA预处理的高密度表面肌电图改进手指运动识别
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00062
Dandan Yang, Xiaoying Wu, Zhengyi Li, Hui Zhou, Dao Zhou, Jin-an Guan, Shuiqing Xie, W. Hou
{"title":"Improving the identification of finger movements using high-density surface electromyography pre-processed with PCA","authors":"Dandan Yang, Xiaoying Wu, Zhengyi Li, Hui Zhou, Dao Zhou, Jin-an Guan, Shuiqing Xie, W. Hou","doi":"10.1109/ISCID51228.2020.00062","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00062","url":null,"abstract":"We investigated whether identification of different finger tasks only relying on the agonist or antagonist extensor digitorum communis (EDC) can be improved by using high-density sEMG (HDsEMG) pre-processed with principal component analysis (PCA). Monopolar HDsEMG was respectively recorded from EDC when the EDC muscle respectively acted as agonist or antagonist muscles. PCA-based approach was evaluated using k-nearest neighbour (KNN) classifier and compared with the classical spatial filters. Using PCA-based configuration can achieve better classification performance in identification of tasks and effort levels and dramatically outperformed spatial filtering configurations in all cases (p<0.05). It can be concluded that PCA can replace the prevailing spatial filters as a general procedure pre-processed HDsEMG, showing that distinct activation distribution patterns of EDC muscle as a function of individual finger flexion as well as extension and its corresponding contraction levels.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128830163","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 of SPCA-LSSVM model in soft measurement of Gasoline dry point SPCA-LSSVM模型在汽油干点软测量中的应用
2020 13th International Symposium on Computational Intelligence and Design (ISCID) Pub Date : 2020-12-01 DOI: 10.1109/ISCID51228.2020.00051
Liying Guo, Yu Zhang
{"title":"Application of SPCA-LSSVM model in soft measurement of Gasoline dry point","authors":"Liying Guo, Yu Zhang","doi":"10.1109/ISCID51228.2020.00051","DOIUrl":"https://doi.org/10.1109/ISCID51228.2020.00051","url":null,"abstract":"A soft measurement model of least squares support vector machine based on data preprocessing for sparse principal component analysis(SPCA-LSSVM) is proposed to solve the problem that the gasoline dry point on the top of atmospheric pressure tower is difficult to measure online. This method combined sparse principal component analysis (SPCA) with the least squares support vector machine (LSSVM) method, principal component analysis (PCA) is transformed to solve the regression optimization problem with quadratic penalty by introducing the sparse constraint aiming at variable selection, in this way, the dimensions of the sparse eigenvector reduces dependency and becomes more independent, it also reduces the influence of measurement noise. The data sample processed by SPCA is used as the input of LSSVM model to establish the soft measurement model of dry point at atmospheric pressure tower top, the problem of lack of sparsity in LSSVM is solved. The simulation results showes that the SPCA-LSSVM model has higher prediction accuracy than the traditional LSSVM model, and the PCA-LSSVM model, reducing the complexity of the model, showing the superiority of the soft-sensing model.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130726051","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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