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Improving Classification Accuracy Using Missing Data Filling Algorithms for the Criminal Dataset
Cuicui Sun,Chunlong Yao,Lan Shen,Xiaoqiang Yu 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4
Predicting crime types by using classification algorithms can help to find factors affecting crimes and prevent crimes. Due to various reasons in the process of data collection, there are often a large number of missing values in actual criminal dataset, which seriously affects the classification accuracy. Therefore, based on mutual KNNI (K nearest neighbor imputation) algorithm and combined with GRA (Grey Relational Analysis) theory, a novel data filling algorithm called GMKNN is proposed in order to improve the classification accuracy. The algorithm replaces the Euclidean distance formula used in mutual KNNI algorithm with the Grey relational grade formula to eliminate the effect of noise from the nearest neighbors and effectively deal with the discrete attributes. By comparing with several popular data filling algorithms based on a real criminal dataset with lots of missing values, higher classification accuracy can be obtained by using GMKNN algorithm, which is up to 77.837%.
A Text Similarity Measurement Method Based on Singular Value Decomposition and Semantic Relevance
( Xu Li ),( Chunlong Yao ),( Fenglong Fan ),( Xiaoqiang Yu ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.4
The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.
A Text Similarity Measurement Method Based on Singular Value Decomposition and Semantic Relevance
Li, Xu,Yao, Chunlong,Fan, Fenglong,Yu, Xiaoqiang Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4
The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.
Mining Frequent Spatio-Temporal Items in Trajectory Data
Fengjiao Yin,Xu Li,Chunlong Yao,Lan Shen 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.4
The time aspect is not currently taken into account for finding a region of interesting (ROI) or a hot region, so that due to the time to visit frequently a place cannot be determined, it is difficult to discover the visiting regularity for a moving object. To this end, the spatio-temporal item (STI) and frequent spatio-temporal item (FSTI) integrated spatial and temporal attributes are defined. The FSTIs can represent a moving object often visits which area in what time, which can provide more useful information to improve the level of the location-based services(LBS). In order to find FSTIs, STIs are generated by using a density-based clustering algorithm to recognize the stay regions of objects, and then the STIs are mapped to 3D-grids integrated spatial and temporal dimensions. Finally, the extraction - merger strategy is used on the frequent grid cells to recombine the FSTIs. Experimental results on real dataset show that the approach proposed for mining FSTIs is effective.
AN ANALYSIS AND RESEARCH OF ROUTING PROTOCOL FOR MANET
Lejun Chi,Zhongxiao Hao,Chunlong Yao,Yating Zhang,Kun Wang,Yang Liu 한국멀티미디어학회 2006 한국멀티미디어학회 국제학술대회 Vol.2006 No.-
In typical methods for accessing Internet or intranet in mobile wireless environment, users can access to fixed networks without multi-relay based on Broad Band access networks. Especially in some special applications, messages from source users can only arrive at destination terminals by multi-relay among several mobile users because wireless network is infrastructural, which is so called Ad hoc networks. There are many up-to-date research results in table driven routing and on-demand routing for ad hoc mobile networks are proposed in this paper. In the mentioned two kinds of routing protocols, DSR and DSDV, this paper not only introduces the contents of the routing protocol, but also points out their advantages and drawbacks, and evaluates these protocols based on a given set of parameters such as deliverance, end-to-end-delay, meanhop and load. This paper also evaluates some drawbacks of above routing protocols. Finally, the authors suggest the research direction in routing for ad hoc mobile wireless networks in the future
Improvement of OPW-TR Algorithm for Compressing GPS Trajectory Data
Meng, Qingbin,Yu, Xiaoqiang,Yao, Chunlong,Li, Xu,Li, Peng,Zhao, Xin Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.3
Massive volumes of GPS trajectory data bring challenges to storage and processing. These issues can be addressed by compression algorithm which can reduce the size of the trajectory data. A key requirement for GPS trajectory compression algorithm is to reduce the size of the trajectory data while minimizing the loss of information. Synchronized Euclidean distance (SED) as an important error measure is adopted by most of the existing algorithms. In order to further reduce the SED error, an improved algorithm for open window time ratio (OPW-TR) called local optimum open window time ratio (LO-OPW-TR) is proposed. In order to make SED error smaller, the anchor points are selected by calculating point's accumulated synchronized Euclidean distance (ASED). A variety of error metrics are used for the algorithm evaluation. The experimental results show that the errors of our algorithm are smaller than the existing algorithms in terms of SED and speed errors under the same compression ratio.
Improvement of OPW-TR Algorithm for Compressing GPS Trajectory Data
( Qingbin Meng ),( Xiaoqiang Yu ),( Chunlong Yao ),( Xu Li ),( Peng Li ),( Xin Zhao ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.3
Massive volumes of GPS trajectory data bring challenges to storage and processing. These issues can be addressed by compression algorithm which can reduce the size of the trajectory data. A key requirement for GPS trajectory compression algorithm is to reduce the size of the trajectory data while minimizing the loss of information. Synchronized Euclidean distance (SED) as an important error measure is adopted by most of the existing algorithms. In order to further reduce the SED error, an improved algorithm for open window time ratio (OPW-TR) called local optimum open window time ratio (LO-OPW-TR) is proposed. In order to make SED error smaller, the anchor points are selected by calculating point`s accumulated synchronized Euclidean distance (ASED). A variety of error metrics are used for the algorithm evaluation. The experimental results show that the errors of our algorithm are smaller than the existing algorithms in terms of SED and speed errors under the same compression ratio.
Yue Weiming,Zhang Shengxue,Li Chunlong,Jiang Tiancai,Liu Lubei,Wang Ruoxu,Huang Yulu,Tan Teng,Guo Hao,Zaplatin Evgeny,Xiong Pingran,Wu Andong,Wang Fengfeng,Zhang Shenghu,Huang Shichun,He Yuan,Yao Zeen 한국원자력학회 2020 Nuclear Engineering and Technology Vol.52 No.8
As a part of R&D work for the high intensity proton linac of China Accelerator Driven Sub-critical System project, a superconducting half-wave cavity with a frequency of 162.5 MHz and an optimal beta of 0.15 (HWR015) has been developed at Institute of Modern Physics (IMP), Chinese Academy of Sciences. In this paper, the design and test results will be described in detail. We introduced a new stiffening strategy for the HWR cavity, the simulation results show that the cavity has much lower frequency sensitivity coefficient (df/dp), Lorentz force detuning coefficient (KL), and can achieve more stable mechanical properties. The performance of the HWR cavity operated in cryostat will be also reported.