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2017 International Conference on Progress in Informatics and Computing (PIC)最新文献

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Task assignment modeling and simulation for cooperative driving of multiple vessels 多船协同驾驶任务分配建模与仿真
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359508
Shanshan Xiang, Yaojie Chen
{"title":"Task assignment modeling and simulation for cooperative driving of multiple vessels","authors":"Shanshan Xiang, Yaojie Chen","doi":"10.1109/PIC.2017.8359508","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359508","url":null,"abstract":"The task assignment problem for multiple vessels cooperative driving is the key problem of the multiple vessels cooperative control. Due to the system showing multi-objective, multi-tasking and multi-constrained features, a cooperative multi-task assignment model is proposed, which can transform multiple constraints task assignment problem into multiple constraints optimization problem based on the multiple vessels task assignment cost function. This method optimizes the results of task assignment by using genetic ant colony hybrid algorithm to search optimization solution globally. In the simulation experiment, it is compared with the genetic algorithm and the ant colony algorithm alone, and experimental results show that the method can optimize task assignment on the basis of satisfying these constraints.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859411","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
An improved constraint model for team tactical position selection in games 博弈中球队战术位置选择的改进约束模型
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359574
Weilong Yang, Quanjun Yin, Long Qin, Yabing Zha
{"title":"An improved constraint model for team tactical position selection in games","authors":"Weilong Yang, Quanjun Yin, Long Qin, Yabing Zha","doi":"10.1109/PIC.2017.8359574","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359574","url":null,"abstract":"With the rapid development of agent modeling technology, the modeling of agent behavior plays an important role in simulation system especially for games and training system. As the basic part of agent behavior modeling, the modeling of Tactical Position Selection (TPS) directly affects the intelligence of agent, the reality of agent's behavior model, and the user experience. Traditional TPS formulation methods do not take team cooperation into consideration. The modeling of this factor is an important area for TPS research. This paper abstracts Team-TPS (TTPS) problem as the Constraint Satisfaction Problem (CSP) and proposes an improved synchronous backtracking algorithm based on entropy ordering to solve the issue. In order to verify the effectiveness of the proposed model, a simulation experiment was carried out. The results demonstrate that for the same tactical task, the proposed Team-TPS model can find better solutions and of high efficiency.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668898","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
A research on adaptive calibration algorithm for multi-geometric projection 多几何投影自适应标定算法研究
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359584
Li-jia Chen, Jie Wang, Yaohui Chen
{"title":"A research on adaptive calibration algorithm for multi-geometric projection","authors":"Li-jia Chen, Jie Wang, Yaohui Chen","doi":"10.1109/PIC.2017.8359584","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359584","url":null,"abstract":"To realize the adaptive projection of the projector on a variety of common projection planes and improve the efficiency of geometric correction, a multi-geometric function for adaptive correction algorithm is proposed. It uses the feature points to automatically fit out the curvilinear function. By automatically filtering the function corresponding to the pixel mapping relationship and calculating the projection pixel offset, the projection screen is cut according to the degree of correction, and finally the coordinates of the vertex are corrected by cutting the texture relationship to achieve geometric correction. The experimental results show that the proposed algorithm can achieve the practical application value of geometric distortion correction, such as cylindrical surface, spherical surface, common surface and projector non — projection, simply and quickly.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121898766","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 english subordinate clause connective correction model based on genetic algorithm and k-nearest neighbor algorithm 基于遗传算法和k近邻算法的英语从句连词纠错模型
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359561
Guimin Huang, Chuang Wu, Sirui Huang, Hongtao Zhu, Ruyu Mo, Ya Zhou
{"title":"An english subordinate clause connective correction model based on genetic algorithm and k-nearest neighbor algorithm","authors":"Guimin Huang, Chuang Wu, Sirui Huang, Hongtao Zhu, Ruyu Mo, Ya Zhou","doi":"10.1109/PIC.2017.8359561","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359561","url":null,"abstract":"In English writing, English learners will inevitably make a variety of grammatical mistakes, especially in English subordinate clause connective. To alleviate high error rate of connective in subordinate clauses of Chinese students' English writing, an automatic error correction model for English subordinate clause connective is studied and implemented from the perspective of machine learning — genetic algorithm (GA) and k-nearest neighbor (KNN) algorithm combination model. Firstly, an automatic feature selection algorithm based on GA is adopted to reduce time consuming and space cost, and to improve the accuracy of connective error correction. Secondly, through comparing the Naive Bayes, decision tree, maximum entropy and KNN algorithm, KNN algorithm is found better while classifying the connectives. Finally, we compared the performance of several hybrid models, which combine different machine learning algorithms with GA. This proves that the combination of GA and KNN algorithm is optimal.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117289122","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 privacy-preserving data sharing solution for mobile healthcare 用于移动医疗保健的隐私保护数据共享解决方案
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359554
Chanying Huang, Kedong Yan, Songjie Wei, Dong Hoon Lee
{"title":"A privacy-preserving data sharing solution for mobile healthcare","authors":"Chanying Huang, Kedong Yan, Songjie Wei, Dong Hoon Lee","doi":"10.1109/PIC.2017.8359554","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359554","url":null,"abstract":"Personal Health Records (PHR) is patient-centric healthcare system, which allows patients to control who can get access to their health records and which section of the record can be accessed. Hot issues such as access control, patients control degree, and privacy protection, etc. are still the challenging concerns while designing a secure PHR system. In this paper, we propose dsPPS, a secure integrated PHR framework(from health data collection to health data sharing) that meets patients' full control of their PHR and sufficient privacy preservation. Specifically, dsPPS provides two schemes: Biometric-Based secure health data Collection (BBC) scheme and Attribute-Based health record Accessing (ABA) scheme. While BBC scheme enables patients to collect their scattered health data from multiple typical health systems securely and efficiently, the ABA scheme allows users (health systems) access to the PHR server with their sensitive attributes being protected. Comprehensive analysis is conducted to show the security of dsPPS against typical attacks. In addition, experiments in both smart phone and PC (Intel) platforms demonstrate that dsPPS produces reasonable performance in terms of storage, communication and computational overheads.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129481691","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
Multiple objects tracking based on multiple information integration 基于多信息集成的多目标跟踪
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359543
Kejia Pu, Z. Lian, Zhongeng Liu
{"title":"Multiple objects tracking based on multiple information integration","authors":"Kejia Pu, Z. Lian, Zhongeng Liu","doi":"10.1109/PIC.2017.8359543","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359543","url":null,"abstract":"Multi-target tracking algorithm often fail when targets are covered, or move fast, and it cannot be recovered from the failure. To solve this problem, firstly we use multiple information which integrate the target motion information and shape information. Based on the Fisher Criteria, we make the distance between same targets as close as possible which the distance between different targets far away. Secondly, the single target tracker based on strong discriminative ability and the Kalman predictor can track accurately when the target is covered or moves fast. The experimental results show that our multi-target tracking algorithm can track target in occlusion or in fast moving accurately in real time.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880105","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 short-term marginal price forecasting model based on ensemble learning 基于集成学习的短期边际价格预测模型
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359519
Kejia Pan, Wenbin Shi, Xin Wang, Jiazhou Li
{"title":"A short-term marginal price forecasting model based on ensemble learning","authors":"Kejia Pan, Wenbin Shi, Xin Wang, Jiazhou Li","doi":"10.1109/PIC.2017.8359519","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359519","url":null,"abstract":"This The system marginal price reflects the short-term supply and demand of electricity goods in the electricity market, which is an important economic link to the participating members of the market. The traditional prediction model has a large error and low generalization ability to forecast the short-term marginal price. Therefore, this paper proposes an ensemble learning algorithm for short-term marginal price forecasting based on AdaBoost. In this paper, the main factors influencing the short-term marginal electricity price are analyzed. Based on the AdaBoost algorithm, the short-term marginal electricity price forecasting problem is modeled. Four prediction models (C4.5, CART, Linear neural network, BP) are compared, and a short — term marginal price forecasting algorithm is proposed. By comparing the actual values with the predicted values, our proposed algorithm is superior to SVM and BP algorithm, which has high application values in power plant engineering.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129653590","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
Metrics analysis based on call graph of class methods 基于类方法调用图的度量分析
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359507
Du Qingfeng, Shi Kun, Yin Kanglin, Q. Juan
{"title":"Metrics analysis based on call graph of class methods","authors":"Du Qingfeng, Shi Kun, Yin Kanglin, Q. Juan","doi":"10.1109/PIC.2017.8359507","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359507","url":null,"abstract":"Evaluating software quality is a significant step in the process of developing software. Object-oriented metrics is an important way for software quality assessment and defect prediction. However, the existing object-oriented metric methods are difficult to reflect the complexity of software and vulnerabilities from the level of classes' method call relationship. A method is proposed in this paper, firstly we construct a call graph according to different classes methods call relationship in the system, and then three new software metrics are defined and analyzed by the call graph. The results of experiment indict that the metrics can reflect the complexity of software systems. These metrics can provide recommendations for evaluating software quality from the call graph.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129058613","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
Hot topic detection based on combined content and time similarity 基于内容和时间相似度相结合的热点话题检测
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359580
Yi Zhao, Kun Zhang, Hong Zhang, Xia Yan, Ying Cai
{"title":"Hot topic detection based on combined content and time similarity","authors":"Yi Zhao, Kun Zhang, Hong Zhang, Xia Yan, Ying Cai","doi":"10.1109/PIC.2017.8359580","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359580","url":null,"abstract":"Hot topic detection has always been a hot research field, and there are a large number of the applications of this technology in real life. Most of the previous work, however, focused only on the textual information of the news itself, while ignoring the other attributes of the news, such as the time the news was published, which can also tell the topic described in its perspective. And others use only one certain method to calculate the text similarity, which all have their disadvantages. To solve these problems, we proposed our own topic detection algorithm, which takes into account the information difference between the title and the text, combines several methods to calculate text similarity, and combines text and time similarity together. We tested the combined similarity calculation methods, and tested the effect of several time similarity equations. Then we took three different models to calculate the combined similarity which are linear model, quadratic polynomial model and neural network model. Finally, we give out the results and analysis of our experiments.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"491 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127574152","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
Deep and shallow features learning for short texts matching 短文本匹配的深浅特征学习
2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359513
Ziliang Wang, Si Li, Guang Chen, Zhiqing Lin
{"title":"Deep and shallow features learning for short texts matching","authors":"Ziliang Wang, Si Li, Guang Chen, Zhiqing Lin","doi":"10.1109/PIC.2017.8359513","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359513","url":null,"abstract":"Short texts matching problem is a special issue in natural language matching. Different from common natural language, short texts have their own characteristices, such as casual expressions and limited lengths, especially in the sentences from social media. Previous works usually use rule-based model and retrieval-based model to match short texts. These models merely focus on word-level similarity between short texts and can not capture deep matching relation of them. To boost the performance of short texts matching, we investigate a basic con-volutional neural network model to learn the sentence-level deep matching relation between short texts. Subsequently, we propose a hybrid model to merge sentence-level deep matching relation with shallow features to generate the final matching score. We evaluate our model on a dataset of short-text conversation based on real-world instances from Sina Weibo. The experimental results show that our model outperforms the previous state-of-art work on this task.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381372","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
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