• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.71-92
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• 2010

As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.165-178
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• 2012

In most conventional video codecs, such as MPEG-2 and MPEG-4, inter coding is performed with the fixed motion vector resolution. When KTA software was developed, resolution for MVs can be selected in each slice. Although KTA codec uses a variety of resolutions for ME, the selected resolution is applied over the entire pixels in the slice and the statistical property of the local area is not considered. In this paper, we propose an adaptive decision scheme for motion vector resolution which depends on region, where MV search area is divided to multiple regions according to the distance from PMV. In each region, the assigned resolution is used to estimate MV. Each region supports different resolution for ME from other regions. The efficiency of the proposed scheme is affected from threshold values to divide the search area and the entropy coding method to encode the estimated MV. Simulation results with HM3.0 which is the reference software of HEVC show that the proposed scheme provides bit rate gains of 0.9%, 0.6%, and 2.9% in Random Access, Low Delay with B picture, and Low Delay with P picture structures, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.435-442
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• 2018

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.

• Korean Journal of Audiology
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• v.24 no.1
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• pp.29-34
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• 2020

Background and Objectives: Bilateral microphones with contralateral routing of signal (BiCROS) hearing aid is an option for hearing rehabilitation in individuals with asymmetric sensorineural hearing loss (ASNHL). The clinical factors influencing the trial and purchase of BiCROS were investigated. Subjects and Methods: We reviewed the medical records of 78 patients with ASNHL who were recommended to use BiCROS and analyzed the demographic and audiological factors influencing the trial and purchase of BiCROS. Results: Among the 78 patients, 52 (66.7%) availed of the free BiCROS trial and 21 (26.9%) purchased BiCROS. The mean pure tone audiometry (PTA) air conduction (AC) threshold of the better- and worse-hearing ears were 44.2±12.8 dB and 90.7±22.5 dB HL, respectively. The decision for trial or purchase of BiCROS was not influenced by age, sex, duration of hearing loss of the worse-hearing ear, or PTA AC threshold or speech discrimination score of both ears. The first and third quartiles of the PTA AC thresholds for the better-hearing ear of BiCROS buyers were 38.75 dB and 53.75 dB HL, respectively. The counterpart values for the worse-hearing ear were 72.50 dB and 118.75 dB HL, respectively. Conclusions: The clinical factors analyzed in this study were found to be irrelevant to the trial and purchase of BiCROS in patients with ASNHL. Nevertheless, the distribution range of the auditory thresholds of the subjects using BiCROS can be a useful basis for the counseling of patients with ASNHL and selection of candidates for BiCROS use.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.707-723
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• 2020

In this study, a decision tree type of quality control (QC) method was developed to improve the temporal-spatial representation and accuracy of the visibility data being operated by the Korea Meteorological Administration (KMA). The quality of the developed QC method was evaluated through the application to the 3 years (2016.03-2019.02) of 290 stations visibility data. For qualitative and quantitative verification of the developed QC method, visibility and naked-eye data provided by the KMA and QC method of the Norwegian Meteorological Institute (NMI) were used. Firstly, if the sum of missing and abnormal data exceeds 10% of the total data, the corresponding point was removed. In the 2nd step, a temporal continuity test was performed under the assumption that the visibility changes continuously in time. In this process, the threshold was dynamically set considering the different temporal variability depending on the visibility. In the 3rd step, the spatial continuity test was performed under the assumption of spatial continuity for visibility. Finally, the 10-minute visibility data was calculated using weighted average method, considering that the accuracy of the visibility meter was inversely proportional to the visibility. As results, about 10% of the data were removed in the first step due to the large temporal-spatial variability of visibility. In addition, because the spatial variability was significant, especially around the fog area, the 3rd step was not applied. Through the quantitative verification results, it suggested that the QC method developed in this study can be used as a QC tool for visibility data.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.596-610
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• 2007

In order to reduce the complexity of SVC encoding, we introduce a fast mode decision method in the enhancement layers of spatial scalability by selectively performing the inter-layer residual prediction of SVC. The Inter-layer residual prediction coding in Scalable Video Coding has a large advantage of enhancing the coding efficiency since it utilizes the correlation between two residuals from a lower spatial layer and its next higher spatial layer. However, this entails the dramatical increase in the complexity of SVC encoders. The proposed method is to analyze the characteristics of integer transform coefficients for the subtracted signal for two residuals from lower and upper spatial layers. Then it selectively performs the inter-layer residual prediction coding and rate-distortion optimizations in the upper spatial enhancement layer if the SAD values of residuals exceed adaptive threshold values. Therefore, by classifying the residuals according to the properties of integer-transform coefficients only with SAD of residuals between two layers, the SVC encoder can perform the inter-layer residual coding selectively, thus significantly reducing the total required encoding time. The proposed method results in reduction of the total encoding time with 51.5% in average while maintaining the RD performance with negligible amounts of quality degradation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.21-35
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• 2021

The purpose of this paper is to examine how demand for labor affects the job seeker's decision on the level of investment in education. In the current paradigm of economic growth in which innovations and technological developments generally weaken the strength demand for labor and increases the uncertainty related to employment, this paper provides a theoretical framework that can be used as a basic framework in understanding the decision of investment in education in varying conditions of demand for labor. The following are the findings of this paper. First, the level of investment in education can generally be regarded to be higher as the demand for labor exacerbates but for the job seekers with a certain characteristic. Second, the Arrow-Pratt absolute risk-aversion measure is the characteristic of the job seeker that determines in what direction the job seeker changes in the level of investment in education, For an arbitrary level of demand for labor there exists a certain threshold which determines the minimum degree of risk-aversion required for the job seeker's Arrow-Pratt should go over to increase the level of education as demand for labor weakens. Third, the job seekers lower the level of education even though the demand condition in labor markets weakens if the compensation function does not depend on the level of education. This is surprising because it turns out that one of the reasons why job seekers invest in education is that they want to be recognized in their compensation for their level of education even when more education still raises the probability of employment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.299-305
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• 2023

Bemisia tabaci is one of polyphagous insect pests that transmits Tomato Yellow Leaf Curl Virus (TYLCV) and Cassava Brown Streak Disease (CBSD). Insecticides are primarily applied to control B. tabaci, but it has limits due to the development of resistance. As a result, a fixed precision sampling plan was developed for its integrated pest management (IPM). The tomato plants were divided into top (more than 130cm from the ground), middle (70 cm to 100 cm above the ground), and bottom (50 cm or less above the ground) strata, before visual sampling of the larvae of B. tabaci. The spatial distribution analysis was conducted using Taylor's power law coefficients with pooled data of top, middle, bottom strata. Fixed precision sampling plan and control decision-making were developed with precision levels and action threshold recommended from published scientific papers. To assess the validation of the developed sampling plans, independent data not used in the analysis were evaluated using the Resampling Validation for Sampling Plan (RVSP) program.