• Title/Summary/Keyword: candidate model

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Improved Model Predictive Control Method for Cascaded H-Bridge Multilevel Inverters (Cascaded H-Bridge 멀티레벨 인버터를 위한 개선된 모델 예측 제어 방법)

  • Roh, Chan;Kim, Jae-Chang;Kwak, Sangshin
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.7
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    • pp.846-853
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    • 2018
  • In this paper, an improved model predictive control (MPC) method is proposed, which reduces the amount of calculations caused by the increased number of candidate voltage vectors with the increased voltage level in multi-level inverters. When the conventional MPC method is used for multi-level inverters, all candidate voltage vectors are considered to predict the next-step current value. However, in the case that the sampling time is short, increased voltage level makes it difficult to consider the all candidate voltage vectors. In this paper, the improved MPC method which can get a fast transient response is proposed with a small amount of the computation by adding new candidate voltage vectors that are set to find the optimal vector. As a result, the proposed method shows faster transient response than the method that considers the adjacent vectors and reduces the computational burden compared to the method that considers the whole voltage vector. the performance of the proposed method is verified through simulations and experiments.

Improved Statistical Language Model for Context-sensitive Spelling Error Candidates (문맥의존 철자오류 후보 생성을 위한 통계적 언어모형 개선)

  • Lee, Jung-Hun;Kim, Minho;Kwon, Hyuk-Chul
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.371-381
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    • 2017
  • The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

THE APPLICATION OF GIS FOR EFFECTIVE DISTRIBUTION OF THE EMERGENCY MEDICAL SERVICE AREA

  • Yang Byung-Yun;Hwang Chul-Sue
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.61-64
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    • 2005
  • The purpose of this paper is to take a closer look at an area having shorted emergence facilities and to determine optional candidate sites instead of vulnerable area by using GIS spatial analysis. Newly determined new candidate is performed by concerning spatial efficiency and spatial equity for a public service. It was determined through using the analyzing of the physical accessibility measure, the Location-Allocation, sort of classic model in spatial statistics and general network analysis. The area of this research has been used in administrative boundary of Young-Dong in Gangneung including 13 emergency, medical hospitals, 46 fire-stations and sub-fire stations. In general terms, what all this show is that the way we are approached for geographical view from using GIS spatial analyzing technique of determined location and allocation problem by the social, economical, political factor and simple administrative discrimination at the meantime. At the same time, with problem occurred in the space it is possible to make an Effective proposal or means, policy, decision for new candidate location-allocation suggesting optimum model.

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A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

  • Yang, Boya;Peng, Wanli;Xue, Yiming;Zhong, Ping
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.11
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    • pp.4184-4202
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    • 2021
  • Text steganography combined with natural language generation has become increasingly popular. The existing methods usually embed secret information in the generated word by controlling the sampling in the process of text generation. A candidate pool will be constructed by greedy strategy, and only the words with high probability will be encoded, which damages the statistical law of the texts and seriously affects the security of steganography. In order to reduce the influence of the candidate pool on the statistical imperceptibility of steganography, we propose a steganography method based on a new sampling strategy. Instead of just consisting of words with high probability, we select words with relatively small difference from the actual sample of the language model to build a candidate pool, thus keeping consistency with the probability distribution of the language model. What's more, we encode the candidate words according to their probability similarity with the target word, which can further maintain the probability distribution. Experimental results show that the proposed method can outperform the state-of-the-art steganographic methods in terms of security performance.

A study of Analysis and Review of Cargo Urban Railway Stations of Korea for Underground Logistics Systems

  • Myung Sung Kim;Kyung Ho Jang;Young Min Kim;Joo Uk Kim
    • International journal of advanced smart convergence
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    • v.12 no.1
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    • pp.208-219
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    • 2023
  • Recently, as mega-urbanization progresses, urban logistics centered on large cities is growing rapidly, and logistics transportation is increasing due to the lack of logistics infrastructure and the operation of a delivery system using cargo trucks in the city center. being cited as a cause. In order to solve this problem, domestic researchers are conducting research on the development of " Urban Underground railway Logistics System" that minimizes the initial infrastructure construction cost by utilizing the existing urban railway facilities in the city. Therefore, this paper analyzed the usage environment of the actual urban railway station to which the system will be applied in order to derive candidates for the test bed selection of "Urban Underground railway Logistics System". The evaluation criteria for test bed candidate role selection were established, and the candidate group derived from the Brown & Gibson model was used to evaluate the candidate group and derive the candidate role. A review of the results was conducted to contribute to the establishment of a test bed for the " Urban Underground railway Logistics System" under development.

Answer Snippet Retrieval for Question Answering of Medical Documents (의학문서 질의응답을 위한 정답 스닛핏 검색)

  • Lee, Hyeon-gu;Kim, Minkyoung;Kim, Harksoo
    • Journal of KIISE
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    • v.43 no.8
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    • pp.927-932
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    • 2016
  • With the explosive increase in the number of online medical documents, the demand for question-answering systems is increasing. Recently, question-answering models based on machine learning have shown high performances in various domains. However, many question-answering models within the medical domain are still based on information retrieval techniques because of sparseness of training data. Based on various information retrieval techniques, we propose an answer snippet retrieval model for question-answering systems of medical documents. The proposed model first searches candidate answer sentences from medical documents using a cluster-based retrieval technique. Then, it generates reliable answer snippets using a re-ranking model of the candidate answer sentences based on various sentence retrieval techniques. In the experiments with BioASQ 4b, the proposed model showed better performances (MAP of 0.0604) than the previous models.

A Note on Performance of Conditional Akaike Information Criteria in Linear Mixed Models

  • Lee, Yonghee
    • Communications for Statistical Applications and Methods
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    • v.22 no.5
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    • pp.507-518
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    • 2015
  • It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.

Object Tracking with the Multi-Templates Regression Model Based MS Algorithm

  • Zhang, Hua;Wang, Lijia
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1307-1317
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    • 2018
  • To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

Analysis of mixture experimental data with process variables (공정변수를 갖는 혼합물 실험 자료의 분석)

  • Lim, Yong-B.
    • Journal of Korean Society for Quality Management
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    • v.40 no.3
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    • pp.347-358
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    • 2012
  • Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.