• Analytical Science and Technology
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• v.34 no.5
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• pp.231-239
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• 2021

Detection of fire accelerants from fire residues is critical to determine whether the case was arson or accidental fire. However, to develop a standardized model for determining the presence or absence of fire accelerants was not easy because of high temperature which cause disappearance or combustion of components of fire accelerants. In this study, logistic regression, random forest, and support vector machine models were trained and evaluated from a total of 728 GC-MS analysis data obtained from actual fire residues. Mean classification accuracies of the three models were 63 %, 81 %, and 84 %, respectively, and in particular, mean AU-PR values of the three models were evaluated as 0.68, 0.86, and 0.86, respectively, showing fine performances of random forest and support vector machine models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.155-156
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• 2019

Traditional machine learning based emotion analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) are less accurate. In this paper, we propose an improved kNN classification method. Improved methods and data normalization achieve the goal of improving accuracy. Then, three classification algorithms and an improved algorithm were compared based on experimental data.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.42-46
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• 2022

To cultivate talented people with creative and convergent thinking skills to live in the era of the 4th industrial revolution, the national curriculum of Korea is gradually emphasizing convergence education and software education. To meet the demands of the times, this paper suggests subject-convergent teaching-learning materials for educating core principles of Support Vector Machines, especially targeting elementary learners. Based on analysis of the national curriculum, achievement standards of three subjects are integrated. After printable worksheets for traditional face-to-face classes had developed, they were transformed to online interactive worksheets for non-face-to-face classes. The teaching-learning materials are expected to promote the growth of the learners' academic motivation and knowledge.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.159-166
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• 2023

This paper investigates effective methods for implementing motion matching, which is actively used in real-time motion generation applications. The success of motion matching heavily hinges on its simple definition of a feature vector, yet this very definition can introduce significant variance in the outcomes. Our research focuses on identifying the optimal combination of feature vectors that effectively generates desired trajectories in locomotion generation. To this end, we experimented with a range of feature vector combinations and performed an in-depth error analysis to evaluate the results.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.171-176
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• 2023

This study is about a speech recognition error correction system designed to detect and correct speech recognition errors before natural language processing to increase the success rate of intent analysis in natural language processing with optimal efficiency in various service domains. An encoder is constructed to embedded the correct speech token and one or more error speech tokens corresponding to the correct speech token so that they are all located in a dense vector space for each correct token with similar vector values. One or more utterance tokens within a preset Manhattan distance based on the correct utterance token in the dense vector space for each embedded correct utterance token are detected through an error detector, and the correct answer closest to the detected error utterance token is based on the Manhattan distance. Errors are corrected by extracting the utterance token as the correct answer.

• Korean Journal of Agricultural Science
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• v.43 no.1
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• pp.136-143
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• 2016

Garlic and onion are staple agricultural products to Koreans and also are important with regard to agricultural producers' income. These products' acreage responses are highly correlated with each other. Therefore, it is necessary to test whether there is a cointegration relationship between garlic acreage and onion acreage when one tries to estimate the acreage response's function. Based upon the test result of cointegration, it is confirmed that there is no statistically significant cointegration relationship between garlic acreage and onion acreage. In this case, vector autoregressive model is preferred to vector error correction model. This study investigated the dynamic relationship between garlic and onion acreage responses using vector autoregressive (VAR) model. The estimated results of VAR acreage response models show that there is a statistically significant relationship between current and lagged acreage of more than one lag. Therefore, it is recommended that government should consider the long-run period's relationship of each product's acreage when it plans a policy for stabilizing the supply and demand of garlic and onion. For the price variables, garlic price only affects garlic acreage response while onion price affects not only onion acreage response but also garlic acreage response. This implies that the stabilizing policy for onion price could have bigger effects than that for garlic price stabilization.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.83-86
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• 2004

In this paper, the performance of wideband CDMA system with smart antenna is analyzed for different bandwidth(1.25MHz, 2.5MHz, 5MHz) and angular spread at base station. In detail, the spatio-temporal wideband multipath vector channel model is proposed. And the received signals in 2D-RAKE receiver are rigorously analyzed in proposed vector channel model. We consider the effect of correlation between any two elements of antenna array. Several multipahts within one chip are distinguished into each one and the strongest signal is selected as a desired one. As a result, the performance of W-CDMA system with smart antenna in spatio-temporal wideband vector channel has been improved in proportion to the increase of angular spread and bandwidth.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1664-1670
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• 1991

For kinematically redundant manipulators, conventional dynamic control methods of local torque optimization showed the instability which resulted in physically unachievable torque requirements. In order to guarantee stability of the null space vector method which resolves redundancy at the acceleration level, Maciejewski[1] analyzed the kinetic behavior of homogeneous solution component and proposed the condition to identify regions of stability and instability for this method. 'In this paper, a modified null space vector method is first presented based on the Maciejewski's condition which is a function of a manipulator's configuration. Secondly, a new control method which is based on the concept of aspects is proposed. It was shown by computer simulations that the modified null space vector method and the proposed method have a common property that a preferred aspect is preserved during the execution of a task. It was also illustrated that both methods demonstrate a drastic reduction of torque loadings at the joints in the tracking motion of a long trajectory when compared with the null space vector method, and thus guarantee the stability of joint torque.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.136-151
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• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.