• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.707-715
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• 2023

This study utilized CART algorithm, a decision tree analysis method, to comparatively analyze factors affecting housing and residential environment satisfaction by region using data from Ministry of Land, Infrastructure and Transport's housing survey in 2020. First, in terms of residential environment satisfaction, accessibility to medical facilities and school district showed higher importance in metropolitan cities and areas compared to other regions, whereas safety from accident showed the opposite trait, showing difference between region. Second, housing characteristics were important in housing satisfaction, indoor environment level satisfaction and indoor safety and hygiene being important in almost all regions, while residential environment characteristics were more important in residential environment satisfaction and influencing factors were relatively evenly distributed. In order to generalize these regional characteristics, research using time series data needs to be conducted later.

• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.45-52
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• 2011

In the past, a patient went to the room where an ultrasound image diagnosis device was set, and then he or she was examined by a doctor. However, currently a doctor can go and examine the patient with a handheld ultrasound device who stays in a room. However, it was implemented with only fundamental functions, and can not meet the high performance required by the focusing algorithm of ultrasound beam which determines the quality of ultrasound image. In addition, low energy consumption was satisfied for the mobile ultrasound device. To satisfy these requirements, this paper proposes a high-performance and low-power single instruction, multiple data (SIMD) based multi-core processor that supports a representative beamforming algorithm out of several focusing methods of mobile ultrasound image signals. The proposed SIMD multi-core processor, which consists of 16 processing elements (PEs), satisfies the high-performance required by the beamforming algorithm by exploiting considerable data-level parallelism inherent in the echo image data of ultrasound. Experimental results showed that the proposed multi-core processor outperforms a commercial high-performance processor, TI DSP C6416, in terms of execution time (15.8 times better), energy efficiency (6.9 times better), and area efficiency (10 times better).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.745-750
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• 2008

In a cognitive radio based system, quality of service (QoS) for the secondary user must be maintained as much as possible even while that of the primary user is protected all he time. In particular, switching wireless links for the secondary user during the transmission of multimedia data causes delay and information loss, and QoS degradations occur inevitably. The efficient resource management scheme is necessary to support the seamless multimedia service to the secondary user. This paper proposes a novel frequency selection method based on Multi-Criteria Decision Making (MCDM), in which uncertain parameters such as received signal strength, cell load, data rate, and available bandwidth are considered during the decision process for the frequency selection with the fuzzy set theory. Through simulation, we show that our proposed frequency selection method provides a better performance than the conventional methods which consider the received signal strength only.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.123-138
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• 2017

This study aims to identify the characteristic variables of businesses that would impact the choice of their type in the 6th industry and analyze how they work. To this end, this study analyzed data of 752 businesses certified as belonging to the 6th industry in 2015 through the classification and regression tree (CART) algorithm in decision tree analysis. The results of analysis showed that the type of agricultural product processing affected shaping the type of the 6th industry at the early stage of growth while the type of agricultural product processing, the type of service, region and sales volumes at the stage of growth and service strategy and the type of agricultural product processing at the stage of maturity. These findings empirically identified key business factors that could support businesses in the 6th industry at each stage of growth and presented a direction forward for support of the 6th industry.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.45-66
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• 2018

Crowdfunding has become more popular than angel funding for fundraising by venture companies. Identification of success factors may be useful for fundraisers and investors to make decisions related to crowdfunding projects and predict a priori whether they will be successful or not. Recent studies have suggested several numeric factors, such as project goals and the number of associated SNS, studying how these affect the success of crowdfunding campaigns. However, prediction of the success of crowdfunding campaigns via non-numeric and unstructured data is not yet possible, especially through analysis of structural characteristics of documents introducing projects in need of funding. Analysis of these documents is promising because they are open and inexpensive to obtain. We propose a novel method to predict the success of a crowdfunding project based on the introductory text. To test the performance of the proposed method, in our study, texts related to 1,980 actual crowdfunding projects were collected and empirically analyzed. From the text data set, the following details about the projects were collected: category, number of replies, funding goal, fundraising method, reward, number of SNS followers, number of images and videos, and miscellaneous numeric data. These factors were identified as significant input features to be used in classification algorithms. The results suggest that the proposed method outperforms other recently proposed, non-text-based methods in terms of accuracy, F-score, and elapsed time.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.75-84
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• 2000

GIS has been building for various application fields with the aids of NGIS project, especially numerous municipal governments are building a UIS in the level of local governments' informatization. Although there are some difference between municipal governments' business, still many things are in common. So far, individual municipal governments have developed a UIS for their own use, which lead to duplicated development of the UIS. The component technology has been introduced to remove such duplicated efforts and it enabled maximizing the reusablilty of the UIS already developed. This paper proposes a component design for network analysis of the drinking water to calculate the amount of flow and the head loss. This component design provides the initial water amount to estimate the amount of the network flow and the head loss, thereby supports the decision making such as installation or extension of the pipe network. The process of the component design accompanies the business reengineering to support the standardized business work flow. Also, the design of the network analysis component uses the algorithms induced with UML specification. Based on the component design, the component development has been progressing and the network analysis system would be followed. In the near future, another component to integrate the network analysis and the business related to the drinking water needs to be developed.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.1-8
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• 2002

Atrial fibrillation(AF). chronic or paroxysmal is the most frequent arrhythmia in human subjects Duration of P wave in signal-averaged electrocardiography(SAECG) reflects intra-atrial conduction time and therefore. could be used as an electrophysiological marker for atrial conduction chance at the earthy stave. So we apply the analysis method using SAECG to diagnose Paroxysmal atrial fibrillation(PAF) . Subjects Participated for the study consisted of two groups: a control group(n=34) of normal healthy volunteers and a group of AF Patients(n=38) with a documented history of PAF but no other history of cardiac disease. We evaluated the effect of several filtering and determination methods to find the starting and ending feints of the P wavy on its duration. To increase the measurement reliability of P wave duration. the automatic detection method was proposed. Also. to increase the detection rate for PAF risk, the decision threshold value was optimized using receiver operation characteristics(ROC) curve. Results showed that the highest statistical difference (p〈0.001) of the P wane duration between controls and subjects was obtained at the Processing condition, using absolute threshold vague(8.75 $\mu N$) , a least mean square(LMS) high pass filter and 30 Hz cutoff frequency. The most outstanding difference(sensitivity 88 % specificity 64.4 %) between controls and subjects was obtained at the decision threshold value of 112 ms.