• 산업공학
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• 제24권4호
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• pp.323-329
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• 2011

We develop a stochastic model to predict the score of a soccer match. We describe the scoring process of the soccer match as a markovian arrival process (MAP). To do this, we define a two-state underlying Markov chain, in which the two states represent the offense and defense states of the two teams to play. Then, we derive the probability vector generating function of the final scores. Numerically inverting this generating function, we obtain the desired probability distribution of the scores. Sample numerical examples are given at the end to demonstrate how to utilize this result to predict the final score of the match.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.297-319
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• 2016

Football match predictions are of great interest to fans and sports press. In the last few years it has been the focus of several studies. In this paper, we propose the Poisson regression model in order to football match outcomes. We applied the proposed methodology to two national competitions: the 2012-2013 English Premier League and the 2015 Brazilian Football League. The number of goals scored by each team in a match is assumed to follow Poisson distribution, whose average reflects the strength of the attack, defense and the home team advantage. Inferences about all unknown quantities involved are made using a Bayesian approach. We calculate the probabilities of win, draw and loss for each match using a simulation procedure. Besides, also using simulation, the probability of a team qualifying for continental tournaments, being crowned champion or relegated to the second division is obtained.

• 응용통계연구
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• 제37권3호
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• pp.323-338
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• 2024

본 연구는 한국 프로배구 리그를 체계적으로 분석하고 대표적인 머신러닝 분류 방법을 활용하여 경기 결과를 예측하고자 한다. 이를 위해 2012/2013 시즌부터 2022/2023 시즌까지의 남자 프로배구와 여자 프로배구 리그 경기 데이터를 수집하였으며, 이 데이터는 경기 세부 내용을 상세하게 포함하고 있다. 데이터는 각 경기를 두 팀으로 분리한 경우와 홈팀을 기준으로 상대팀과의 성과 차이로 데이터를 가공한 경우로 두 가지 다른 데이터 구조를 모델에 적용했다. 이를 통해 남자 프로배구와 여자 프로배구 각각에 대해 총 4개의 예측 모형을 구축했다. 경기 종료 전에는 모형에서 사용하는 세부 변수 값들을 알 수 없기 때문에, 오늘 경기 직전까지의 3~4 경기의 결과를 전처리하여 이를 변수로 사용했다. 본 연구에서는 Decision Tree, Logistic Regression, Bagging, Random Forest, Xgboost, Adaboost, Light GBM 같은 다양한 머신러닝 기법을 분류에 활용하여, Random Forest를 사용한 모델이 가장 우수한 예측 성능을 보였다. 최종 선택한 모형에 대해 변수 중요도 그림과 부분 의존도 그림을 확인한 결과 성별과 데이터 구조에 따라 중요한 변수들이 다른 것으로 나타났지만, 공통적으로 세트 성공 수, 블로킹 득점, 범실 개수가 가장 중요한 변수임을 알 수 있었다. 본 승패 예측 모델은 사후적 예측이 아닌 경기 종료 전 사전 예측이 가능한 모형이라는 점에서 차별성을 가지며, 우리의 분석이 한국 프로배구 팀들에게 전략적 추론이 될 수 있을 것이라 기대한다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.2198-2204
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• 2003

In order to make a match of the insufficient direct solar radiation, in this study, the target output is lowered to 9 kW smaller than 25 kW in former studies. It is also necessary to match the collector/receiver with engine/generator systems to accomplish the power level of a system. The simulation analyses of a dish solar power system with stirling engine are totally carried out to predict the system performance with the designed values. In addition, an influence of direct solar radiation on system performance and operation control is discussed in simulation. It is found that the diameter of concentrator could be made small to 8 m regardless of slope errors with 2.5 and 5.0 mrad radiation, and the operation range of mean pressure control. is wide even if the direct solar radiation is a quit low.

• 한국안전학회지
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• 제13권4호
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• pp.230-239
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• 1998

Though limited, Model Human Processor (MHP) has been used to explain the complex users' behaviors during human-computer interactions in a simplified manner. MHP consists of perceptual, cognitive and motor systems, each with processors and memories interacting with each other in serial or parallel mode. The important parameters of memory include the storage capacity, the decay time, and the code type of a memorized item. The important parameter of a processor is the cycle time. Using these features of the model, this study developed a computerized cognitive process simulator to predict the cognitive process time of a class match task process. An experimental validity test result showed that the mean prediction time for cognitive process of the class match task simulated 50 times by the simulator was consistent with the mean cognitive process time of the same task performed by 37 subjects. Animation of the data flow during the class match task simulation will help understand the invisible human cognitive process.

• 경영정보학연구
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• 제8권2호
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• pp.139-154
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• 2006

추천시스템은 사용자들의 관심을 끄는 아이템을 그들이 보다 쉽게 찾도록 도와주거나, 그들의 기호에 기반하여 의미 있는 아이템들을 제공한다. 지금까지 가장 성공적이었던 협업 필터링 기반 추천시스템은 다른 사용자들의 의견을 참조하여 추천을 원하는 사용자에게 추천을 한다. 즉, 아이템들에 대한 사용자 기호를 나타내는 다른 사용자들의 평가정보가 추천을 위한 정보원으로 사용된다. 이처럼, 협업 필터링 기반 추천시스템이 사용자들의 기호만을 이용하도록 설계되었지만, 다른 정보를 이용하면 추천시스템의 성능과 정확도를 높일 수 있을 것으로 사료되어, 본 논문에서는 유사 정도와 인구통계학 정보를 이용한 협업 필터링 기반 추천시스템을 제안한다. 이런 추천시스템에서는 평가정보가 계속적으로 누적되기 때문에, 추천시스템의 정확도를 유지할 수 있는 한, 사용하는 데이터의 양을 줄이는 게 중요하다. 본 논문에서는 유사 정도와 인구통계학 정보를, 사용할 데이터의 양을 줄이기 위한 기준으로 사용하여 자연스레 시스템의 성능을 향상시켰다. 본 논문에서는 실험을 통하여 유사 정도의 사용이 추천시스템의 정확도를 높여주었고, 특정 인구통계학 정보의 사용도 추천시스템의 정확도를 높였음을 보였다.

• 한국시스템다이내믹스연구
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• 제11권3호
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• pp.5-31
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• 2010

The purpose of this study is to develop a system dynamics model for growth prediction of low birth weight infants(LBWIs) based on nutrition. This growth prediction model consists of 9 modules; body weight, height, carbohydrate, protein, lipid, micronutrient, water, activity and energy module. The results of the model simulation match well with the percentiles of weights and heights of the Korean infants, also with the growth records of 55 LBWIs, under 37 weeks of gestational age, whose weights are appropriate for their gestational age. This model can be used to understand the current growth mode of LBWIs, predict the future growth of LBWIs, and be utilized as a tool for controlling the nutrient intake for the optimal growth of LBWIs in actual practice.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.157-165
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• 2022

As the stock market is affected by various circumstances including economic and political variables, predicting the stock market is considered a still open problem. When combined with corporate financial statement data analysis, which is used as fundamental analysis, and technical analysis with a short data generation cycle, there is a problem that the time domain does not match. Our proposed method, LSTE the operating profit and market outlook of a petrochemical company and estimates the sales and operating profit of the company, it was possible to solve the above-mentioned problems and improve the accuracy of stock price prediction. Extensive experiments on real-world stock data show that our method outperforms the 8.58% relative improvements on average w.r.t. accuracy.

• 산업공학
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• 제20권2호
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• pp.133-141
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• 2007

As the sales of the Sports Toto, the Korean lottery on sports games, have increased significantly in recent five years, interest in predicting the various results of sports matches has also been raised. Dixon and Coles (1997) proposed a bivariate Poisson model to predict the results of English soccer league matches. In this paper, we pay attention to the physical condition of players that may affect soccer match results and revise Dixon and Coles' model to consider probable fatigue due to the players' short rest followed by their frequent matches. We observed the fatigue effect in the match results, and found positive betting returns available when using our prediction model. Furthermore, the validity of probability-based odds in European and Korean betting markets is analyzed.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.