• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12B
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• pp.2094-2108
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• 2000

오류를 수반하는 통신망을 통한 멀티미디어 데이터의 응용은 최근 그 수요가 급증하고 있다. 하지만 그 구현은 많은 문제점들을 야기하는데, 전송된 비디오 데이터에 발생한 오류를 처리하는 문제가 그 중 하나이다. 이는 압축된 비트열에 발생한 오류가 영상의 시-공간 방향으로 심각한 전파 현상을 수반하기 때문이다. 이러한 심각한 오류 전파를 완화하기 위해 본 논문에서는 EREC라 알려진 오류 제한 기법을 적용하고, 적용된 EREC의 오류 전파 특성을 분석하였다. 이를 통해, 압축 부호화된 하나의 기본 블록 (매크로 블록)이 복호시 오류가 생길 확률을 추정하였으며, 추정된 확률의 근사를 통해 양 끝단(전송단과 수신단)에서의 비디오 화질 열화를 예측하였다. 추정 확률의 근사는 매 기본 블록에서 발생된 비트수에 대한 그 기본 블록이 복호시 오류가 생길 확률을 간단한 1차식을 통한 선형 회귀법으로 모델링 되었으며, 따라서 간단한 방법을 통해 양 끝단의 화질 열화를 효과적으로 예측할 수 있었다. 부호화된 비트열이 전송 오류에 보다 강인하게 되도록 하기 위해, 본 논문에서 개발된 화질 열화 모델을 양자화기 선택에 적용함으로써, 새로운 최적 양자화 기법을 제시하였다. 본 논문에서 제안된 최적 양자화 기법은, 기존의 양자기 최적화 기법들과는 달리, 복호단에서의 복원 영상 화질이 주어진 비트율에서 최적이 되도록 양자화를 수행한다. H.263 비디오 압축 규격에 적용한 제안 양자화 기법의 실험 결과를 통해, 제안 기법이 매우 적은 계산상의 부하를 비용으로 객관적 화질은 물론 주관적 화질까지 크게 개선할 수 있음을 확인할 수 있었다.내었다.Lc. lacti ssp. lactis의 젖산과 초산의 생성량은 각각 0.089, 0.003과 0.189, 0.003M이었다. 따라서 corn steep liquor는 L. fermentum와 Lc. lactis ssp, lactis 의 생장을 위해 질소 또는 탄소 공급원으로서 배지에 첨가 될 수 있는 우수한 농업 부산물로 판단되었다.징하며 WLWQ에 적용되는 몇 가지 제약을 관찰하고 이를 일반적인 언어원리로 설명한다. 첫째, XP는 주어로만 해석되는데 그 이유는 XP가 목적어 혹은 부가어 등 다른 기능을 할 경우 생략 부위가 생략의 복원 가능선 원리 (the deletion-up-to recoverability principle)를 위배하기 때문이다. 둘째, WLWQ가 내용 의문문으로만 해석되는데 그 이유는 양의 공리(the maxim of quantity: Grice 1975) 때문이다. 평서문으로 해석될 경우 WP에 들어갈 부분이 XP의 자질의 부분집합에 불과하므로 명제가 아무런 정보제공을 하지 못한다. 반면 의문문 자체는 정보제공을 추구하지 않으므로 앞에서 언급한 양의 공리로부터 자유롭다. 셋째, WLWQ의 XP는 주제어 표지 ‘는/-은’을 취하나 주어표지 ‘가/-이’는 취하지 못한다(XP-는/-은 vs. XP-가/-이). 이는 IP내부 에 비공범주의 존재 여부에 따라 C의 음운형태(PF)가 시성이 정해진다는 가설로 설명하고자 했다. WLWQ에 대한 우리의 논의가 옳다면, 본 논문은 다음과 같은 이론적 함의를 기닌다. 첫째, WLWQ의 존재는 생략에 대한 두 이론 즉 LF 복사 이론과 PF 삭제 이론

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.93-105
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• 2014

This study evaluated the relative efficiency of mobile emission reduction countermeasures through a Data Envelopment Analysis (DEA) approach and determined the priority of countermeasures based on the efficiency. Ten countermeasures currently applied for reducing greenhouse gases and air pollution materials were selected to make a scenario for evaluation. The reduction volumes of four air pollution materials(CO, HC, NOX, PM) and three greenhouse gases($CO_2$, $CH_4$, $N_2O$) for the year 2027, which is the last target year, were calculated by utilizing both a travel demand forecasting model and variable composite emission factors with respect to future travel patterns. To estimate the relative effectiveness of reduction countermeasures, this study performed a super-efficiency analysis among the Data Envelopment Analysis models. It was found that expanding the participation in self car-free day program was the most superior reduction measurement with 1.879 efficiency points, followed by expansion of exclusive bus lanes and promotion of CNG hybrid bus diffusion. The results of this study do not represent the absolute data for prioritizing reduction countermeasures for mobile greenhouse gases and air pollution materials. However, in terms of presenting the direction for establishing reduction countermeasures, this study may contribute to policy selection for mobile emission reduction measures and the establishment of systematic mid- and long-term reduction measures.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.316-329
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• 2008

Recently the increased attention pays on the processing of multiple, relatively low quantity, high value-added products resulted in adoption of batch process in the chemical process industry such as pharmaceuticals, polymers, bio-chemicals and foods. As there are more possibilities of the improvement of operations in batch process than continuous processes, a lot of effort has been made to enhance the productivity and operability of batch processes. But the chemical process industry faces a range of uncertainties factors such as demands for products, prices of product, lead time for the supply of raw materials and in the production, and the distribution of product. And global competition has made it imperative for the process industries to manage their supply chains optimally. Supply chain management aims to integrate plants with their supplier and customers so that they can be managed as a single entity and coordinate all input/output flows (of materials, information) so that products are produced and distributed in the right quantities, to the right locations, and at the right time.The objective of this study is to solve the purchase, distribution, production planning and scheduling problem, which minimizes the total costs of production, inventory, and transportation under uncertainty. And development of SCM model in chemical industry including batch mode operations. Through that, the enterprise can respond to uncertainty. Also integrated process optimal planning and scheduling model for manufacturing supply chain. The result shows that, the advantage of supply chain integration are quality matters seen by customers and suppliers, order schedules, flexibility, cost reduction, and increase in sales and profits. Also, an integration of supply chain (production and distribution system) generates significant savings by trading off the costs associated with the whole, rather than minimizing supply chain costs separately.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.294-294
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• 2019

The system for predicting flood of river at Flood Control Office is made up of a rainfall-runoff model and FLDWAV model. This system is mainly operating to predict the excess of the flood watch or warning level at flood forecast points. As the demand for information of the management and operation of riverside, which is being used as a waterfront area such as parks, camping sites, and bike paths, high-level forecasts of watch and warning at certain points are required as well as production of lowland flood forecast information that is used as a waterfront within the river. In this study, a technology to produce flood forecast information in lowland areas of the river used as a waterfront was developed. Based on the results of the 1D hydraulic analysis, a model for performing spatial operations based on high resolution grid was constructed. A model was constructed for Andong district, and the inundation conditions and level were analyzed through a virtual outflow scenarios of Andong and Imha Dam.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Korea Port Economic Association
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• v.28 no.2
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• pp.75-93
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• 2012

The steel cargoes as the core raw materials for the manufacturing industry have important roles for increasing the handling volume of the port. In particular, steel cargoes are fundamental to vitalize Port of Incheon because they have recognized as the primary key cargo items among the bulk cargoes. In this respect, the IPA(Incheon Port Authority) ambitiously developed the port complex facilities including dedicated terminals and its hinterland in northern part of Incheon. However, these complex area has suffered from low cargo handling records and has faced operational difficulties due to decreased net profits. In general, the import and export steel cargo volumes are sensitively fluctuated followed by internal and external economy index. There is a scant of research for forecasting the steel cargo volume in Incheon port which used in various economy index. To fill the research gap, the aim of this research is to predict the steel cargoes of Port of Incheon using the well established methodology i.e. System Dynamics. As a result, steel cargoes volume dealt with in Incheon port is forecasted from about 8 million tons to about 10 million tons during simulation duration (2011-2020). The Mean Absolute Percentage Error (MAPE) is measured as 0.0013 which verifies the model's accuracy.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.135-145
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• 2008

As a fundamental input to the travel demand forecasting, OD has been always a concern in obtaining the accurate link traffic volume. Numerous methods were applied thus far without a complete success. Some existing OD estimation techniques generally extract regular samples and expand those sample into population. These methods, however, leaves some to be desired in terms of accuracy. To complement such problems, research on estimating OD using additional information such as link traffic volume as well as sample link use rate have been accomplished. In this paper, a new approach for estimating static origin-destination (OD) using probe vehicle has been proposed. More specifically, this paper tried to search an effective sample rate which varies over time and space. In a sample test network study, the traffic volume error rate of each link was set as objective function in solving the problem. As a key result the MAE (mean absolute error) between expanded OD and actual OD was identified as about 5.28%. The developed methodology could be applied with similar cases. Some limitations and future research agenda have also been discussed.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.134-134
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• 2001

이동통신 망을 통한 멀티미디어 통신의 수요 급증으로, 차세대 이동통신 시스템에서는 패킷 교환에 기초한 망 구조가 사용될 것으로 예측된다. VOD(Voice Activity Detector)를 갖는 음성 단말은 데이터를 발생시키는 talk spurt(평균이 t$_1$인 지수분포를 따름)와 데이터를 발생시키지 않는 silence period(평균이 t$_2$인 지수분포를 따름)의 두가지 상태를 갖는 마코프 체인으로 모델링된다. Goodman at. al.은 음성 단말들이 talk spurt동안만 데이터를 전송하게 함으로써 더 많은 가입자들을 수용할 수 있는 PRMA(Packet Reservation Multiple Access) 기법을 제안되었다. PRMA 방식에서는 시간 축이 슬롯들로 구성되며 여러개의 슬롯들로 프레임이 형성된다. Silence period 상태에 있던 음성 단말은 talk spurt 상태가 되면 talk spurt의 첫 번째 데이터를 하나의 슬롯을 통해 전송하게 된다. 이때 단말들은 각 슬롯에서 데이터를 전송할 수 있는 확률을 나타내는 채널 접근 확률(channel access probability)에 의해 데이터를 전송하게 되며 전송에 성공하면 슬롯을 예약함으로서 다음 프레임부터는 동일한 위치의 슬롯을 통해 데이터들을 전송하게 된다. DS/CDMA(Direct Sequence/code Division Multiple Access)는 이동통신 단말의 수용 용량상의 이점, 소프트 핸드오버 능력, 보다 용이하게 셀 계획을 세울 수 있는 점 등에 의해 차세대 이동통신 망에서 채택될 예정이다. CDMA 시스템은 간섭(interference)에 의해 용량이 제한을 받게 되며, MAI(Multiple Access Interference)가 시스템의 성능에 많은 영향을 미치게 된다. Brand, et. al.은 간섭의 분산을 줄이기 위해 PRMA 개념을 DS/CDMA 환경으로 확장한 Joint CDMA/PRMA 프로토콜을 제안하였다. 이때 각 슬롯에서의 데이터 전송확률을 그 슬롯에서 예약상태에 있는 음성 단말의 수에 의존하게 하는 방식을 사용하였으며 데이터 전송확률을 나타내는 채널 접근 확률들을 시뮬레이션을 통해 유도하였다. 한편 음성 단말에게는 실시간 서비스를 제공해 주어야 하는 대신 데이터 단말에게는 실시간 서비스를 제공해 주지 않아도 되므로, 트래픽이 많을 때에는 음성 단말의 데이터 전송에 우선권을 주는 것이 바람직하다. 이를 위해서 Brand, et. al.은 채널 접근 확률을 각 슬롯의 트래픽 상태에 따라 적응적으로 산출하는 기법을 제안하였다. 본 연구에서는 Joint CDMA/PRMA의 성능이 채널 접근 함수의 효율성에 많이 의존하게 되므로 보다 효율적인 채널 접근 확률을 구하는 방법을 제안한다. 즉 채널 액세스 확률을 각 슬롯에서 예약상태에 있는 음성 단말의 수뿐만 아니라 각 슬롯에서 예약을 하려고 하는 단말의 수에 기초하여 산출하는 방법을 제안하고 이의 성능을 분석하였다. 시뮬레이션에 의해 새로 제안된 채널 허용 확률을 산출하는 방식의 성능을 비교한 결과 기존에 제안된 방법들보다 상당한 성능의 향상을 볼 수 있었다.