• Journal of IKEEE
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• v.20 no.4
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• pp.398-411
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• 2016

SVM, a machine learning method, is widely used in image processing for it's excellent generalization performance. However, to add other data to the pre-trained data of the system, we need to train the entire system again. This procedure takes a lot of time, especially in embedded environment, and results in low performance of SVM. In this paper, we implemented an SVM trainer and classifier in an FPGA to solve this problem. We parlallelized the repeated operations inside SVM and modified the exponential operations of the kernel function to perform fixed point modelling. We implemented the proposed hardware on Xilinx ZC 706 evaluation board and used TSR algorithm to verify the FPGA result. It takes about 5 seconds for the proposed hardware to train 2,000 data samples and 16.54ms for classification for $1360{\times}800$ resolution in 100MHz frequency, respectively.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.43-50
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• 2008

Multinomial logistic regression is a well known multiclass classification method in the field of statistical learning. More recently, the development of sparse multinomial logistic regression model has found application in microarray classification, where explicit identification of the most informative observations is of value. In this paper, we propose a sparse multinomial kernel logistic regression model, in which the sparsity arises from the use of a Laplacian prior and a fast exact algorithm is derived by employing a bound optimization approach. Experimental results are then presented to indicate the performance of the proposed procedure.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.75-80
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• 2001

Rule inconsistency is an important issue that is needed to be addressed while designing efficient and optimal fuzzy rule bases. Automatic generation of fuzzy rules from data sets, using machine learning techniques, can generate a significant number of redundant and inconsistent rules. In this study we have shown that it is possible to provide a systematic approach to understand the fuzzy rule inconsistency problem by using the proposed measure called the Commonality measure. Apart from introducing this measure, this paper describes an algorithm to optimize a fuzzy rule base using it. The optimization procedure performs elimination of redundant and/or inconsistent fuzzy rules from a rule base.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.265-270
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• 2003

When designing an engine part the designer has to choose the proper material and to define the dimensions of the part. in the most rudimentary case he has available as guidelines the collective experience from similar applications. The performance of the part is tested on occasion of the trial runs for the full system, and possibly occurring deficiencies are corrected until a satisfactory usable life and safety of operation is achieved. This procedure is time consuming and costly, and the learning effect is minimal. These requirements have to be condensed into characteristic values ameanable to computation. Since testing and computation are complementary we have to look at the currently existing mathematical models for engine bearing performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2484-2491
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• 2009

This paper proposes robust state feedback control of asynchronous sequential machines with model uncertainty. The considered asynchronous machine is deterministic, but its state transition function is partially known before executing a control process. The main objective is to derive the existence condition for a corrective controller for which the behavior of the closed-loop system can match a prescribed model in spite of uncertain transitions. The proposed control scheme also has learning ability. The controller perceives true state transitions as it undergoes corrective actions and reflects the learned knowledge in the next step. An adaptation is made such that the controller can have the minimum number of state transitions to realize a model matching procedure. To demonstrate control construction and execution, a VHDL and FPGA implementation of the proposed control scheme is presented.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.19-27
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• 2019

In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.