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- 저자 : Yixiang Chen (검색결과 6 건)
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A Petri Net Processing Model of STeCEQL
Huiyong Li,Yixiang Chen,Kangli He 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.5
The internet of vehicles is an important internet of things. It is an urgent problem that how to real-time processes these spatial and temporal data of the internet of vehicles. The complex event processing technology can filter the concerned data to the event by the complex event query language (EQL) and the system can responds effectively. The STeCEQL is a complex event language for the internet of vehicles, which constraint with spatial and temporal. The processing model of the complex event language is a core issue of the complex event processing technology. In this paper, we established a Petri Net processing model of STeCEQL. We give all kind of processing model of STeCEQL expressions. And these processing models are synthesized by two types of basic Petri Net model: the sequence structure and the logic and structure. Finally, we proved the Petri Net processing model of STeCEQL is structural boundedness and structural conservativeness, but it is not structural repetitiveness.
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A Vehicle Monitoring System Based on STeCEQL
Huiyong Li,Yixiang Chen 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6
The vehicle monitoring system is a very important class of information systems. However, the existing monitoring systems generally require the support of the large database systems and the monitoring event could not be processed in real time. The research of internet of vehicles has proposed the novel ideas and methods to build the monitoring system. In this paper, we construct a vehicle monitoring system based on a complex event query language: STeCEQL. Firstly, we give the approach of verifying the reasonableness of the monitoring events through building the model of the vehicles. Then we propose the method of describe the vehicle monitoring events by STeCEQL. Meanwhile, we give the complex event detection algorithms based on the event tree. Finally, the simulation results demonstrate the effectiveness of the vehicle monitoring approach of internet of vehicles.
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Parameter Optimization of SVM Based on Improved ACO for Data Classification
Wen Chen,Yixiang Tian 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1
The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.
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Short-term Prediction of Stock Index Based on EMD and SVMs
Wen Chen,Yixiang Tian 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.8
In allusion to the prediction of the stock return rate, this paper has proposed nonlinear combined prediction method based on EMD (Empirical Mode Decomposition) and SVMs (Support Vector Machines). The method has divided stock return rate series into several components of different frequencies using EMD technology, getting three new sequences by grouping superposition of each component according to the frequency, which represents items of market volatility, major events, and trend respectively. Based on these three sequences, prediction is made by constructing different SVMs models to obtain the predicted value of each sequence. With SVMs, a combined model is built on the basis of predictive value of each sequence to obtain the final predicted value of stock return rate. Using CSI 300 Index, the validity of this method is verified, and the results show that the proposed model is better than the other models presented in this paper on forecasting CSI 300 Index.
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Hu Hanyu,Shan Weifeng,Tang Yixiang,Heidari Ali Asghar,Chen Huiling,Liu Haijun,Wang Maofa,Escorcia-Gutierrez José,Mansour Romany F,Chen Jun 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6
The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.
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Fabrication of W-Cu Alloy via Combustion Synthesis Infiltration Under an Ultra-Gravity Field
Yuepeng Song,Qian Li,Jiangtao Li,Gang He,Yixiang Chen,김형섭 대한금속·재료학회 2014 METALS AND MATERIALS International Vol.20 No.6
Tungsten copper alloy with a tungsten concentrate of 70 vol% was prepared by self-propagating hightemperaturesynthesis in an ultra-gravity field. The phase structures and components of the W-Cu alloyfabricated via this approach were the same as those via traditional sintering methods. The temperature andstress distributions during this process were simulated using a new scheme of the finite element method. The results indicated that nonequilibrium crystallization conditions can be created for combustion synthesisinfiltration in an ultra-gravity field by the rapid infiltration of the liquid copper product into the tungstencompact at high temperature and low viscosity. The cooling rate can be above 100,000 K/s and high stresses intungsten (~5 GPa) and copper (~2.6 GPa) were developed, which passivates the tungsten particle surface,resulting in easy sintering and densifying the W-Cu alloy. The reliability of the simulation was verified throughtemperature measurement and investigation of the microstructure. The W-Cu composite-formation mechanismwas also analyzed and discussed with the simulation results.
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