• Analytical Science and Technology
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• v.12 no.5
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• pp.387-396
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• 1999

The retention behavior of Cot(II)-dithiocarbamate(DTC) chelates in reversed phase high performance liquid chromatography was investigated. Enthalpy and entropy of chelates transfer from the mobile phase to the stationary phase were calculated from retention data using van't Hoff plots. The dependence of In k' on enthalpy was decreased with increasing organic solvent ratio on the mobile phase. The compensation temperatures(${\beta}$) calculated from the slope of $-{\Delta}H^0$ vs In k' were in the range of 756.3-888.5 K. From these results. it was found that the retention mechanism of DTC chelates was invariant under the various temperatures and was largely affected by the solvophobie effect. Liniear relationship between S index and log k' in emprical retention equation, $log\;k^{\prime}=log\;{k_w}^{\prime}-S_{\varphi}$ showed that S index was influenced mainly by the interaction between DTC chelates and the mobile phase.

• Journal of the korean Society of Automotive Engineers
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• v.13 no.2
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• pp.67-87
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• 1991

The computer program which predicts the gas exchange process of multi-cylinder 4-Stroke cycle spark-ignition engine, can be great assistance for the design and development of new engine. In this study, the computer program was developed to predict the gas exchange process of multi-cylinder four stroke cycle spark ignition engine including intake and exhaust systems. When gas exchange process is to be calculated, the evaluation of the variation of the thermo-dynamic properties with time and position in the intake and exhaust systems is required. For the purpose, the application of the generalized method of characteristics to the gas exchange process is known as one of the method. The simulation model developed was investigated to the analysis of the branch system of multi-cylinder. The models used were the 2-zone expansion model and single zone model for in cylinder calculation and the generalized method of characteristic including area change, friction, heat transfer and entropy gradients for pipe flow calculation. The empirical constants reduced to least number as possible were determined through the comparison with the experimented indicator diagram of one particular operation condition and these constants were applied to other operating condition. The predicted pressures in cylinder were compared with the experimental results over the wide range of equivalence ratio and ignition timing. The predicted values have shown good agreement with the experimental results. The thermodynamic properties in the intake and exhaust system were predicted over the wide range of equivalence ratio and ignition timing. The obtained results can be summarized as follows. 1. Pressures in the exhaust manifold have a little influence on the equivalence ratio, a great influence on the ignition timing. 2. Pressures in the inlet manifold are nearly unchanged by the equivalence ratio and the ignition timing. 3. In this study, the behaviors of the exhaust temperature, gas in the exhaust manifold were ascertained.

• Journal of Korea Trade
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• v.25 no.5
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• pp.74-92
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• 2021

Purpose - Utilizing a large sample of Korean firms, this study examines international diversification impacts on corporate tax avoidance and whether firms affiliated with large business groups (known in Korean as "chaebol") reinforce the relationship between international diversification and tax avoidance. Design/methodology - This paper hypothesizes that 1) international diversification is likely to increase tax avoidance, 2) the positive effect of international diversification on tax avoidance is likely to be more pronounced for chaebol firms. We examine the hypotheses by using Korean firms listed in the Korean stock market between 2011 and 2016. We employ the number of foreign subsidiaries and the entropy index as proxies for international diversification and CASH ETR and GAAP ETR as proxies for tax avoidance. Findings - Our findings are summarized as follows. First, we have found that as firms are more internationally diversified, tax avoidance increases. It means that international diversification can be employed as a method of reducing the tax burden. Second, firms affiliated with chaebol are strengthened by the positive relation between international diversification and tax avoidance. It is interpreted that chaebol firms have more effective opportunities to reduce taxes than other firms. When entering foreign markets, they can share experience and resources to decrease taxation within the large business group. Originality/value - This study provides empirical evidence regarding the tax effect of international diversification. Unlike prior studies, international diversification is positively related to tax avoidance in Korea. In addition, we present additional evidence on the chaebol effects of international diversification on tax avoidance, in which they have an advantage to reduce taxes using transfer pricing through related party transactions, income shifting to low tax rate countries, and establishing subsidiaries in tax havens.

• Journal of Wetlands Research
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• v.18 no.4
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• pp.404-412
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• 2016

This paper not only aims to establish a plan to acquire the water stage data in a constant and proper manner by using limited manpower and costs, but also establishes the fundamental technology for acquiring the water level observation data or the stage data. For this, this paper focuses on how to acquire the stage data, in a uniform manner, that can represent each basin by developing the technology for establishing the optimal observational network. For that, this paper identifies the current status of the stage gauge stations installed in the ChungJu dam including wetland basin mainly along the national rivers. Then, thus obtained factors are used to develop the representative unit hydrograph. After that, the data are converted into the probability density function. Then, the stations are calculated information transfer amount. As a last step, we establish the optimized stage gauge network by the location of the stage station and space impact that takes into account for the combinations of the number of the stations. In other words, we consider the combination of the stage gauge station with information transfer amount and spatial correlation analysis for estimation.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2013.11a
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• pp.26-27
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• 2013

산림생태계의 에너지, 물질, 정보의 교환 과정과 그 변화를 이해하려면 먼저 생태계의 구조와 기능이 어떻게 상호작용하는지를 이해해야 한다. 생태계의 기능은 한, 두 가지의 특징에 의해서만 이루어지는 것이 아니다. 그렇기 때문에 그 기능을 파악하고 적절히 이용하거나 대응하기 위해서는 한 생태계와 주변 환경 전체를 바라볼 수 있는 시스템 사고가 필요하다. 이에 우리는 생태계의 '구조'를 파악함으로써 생태계의 '상태'를 이해하고자 한다. 본 연구에서는 Ruddell and Kumar (2009)의 접근법을 따라, 어떻게 한 생태계의 상태를 파악할 수 있는가라는 질문을 광릉활엽수림에 적용하여 답하고자 한다. 즉, 우리는 산림생태계가 열린 복잡계라고 가정하고, 생태계 내에서 다양한 프로세스들 간의 시시각각 변하는 네트워크의 구조가 각 시점의 시스템의 상태를 나타내는 지표가 될 수 있다고 가정하였다. 이 연구에서는 그 구조적 특징을 정량화하여 나타내는데 초점을 맞추었다. 각각의 프로세스를 대표하는 상태 변수들 간의 정보 흐름의 양과 방향, 시간 규모를 계산해냄으로써 네트워크 구조를 파악하고자 하였다. 온대 산악지형 활엽수림인 GDK의 2008년 순생태계교환량(NEE), 총일차생산량(GPP), 생태계호흡량(RE), 현열플럭스(H), 잠열플럭스(LE), 하향단파복사(Rg), 강수량(Precipitation), 기압(Pressure), 기온(T), 포차(VPD)의 시계열 자료를 월별로 나누어 최장 18 시간 규모의 정보 흐름을 계산하였다. 정보 흐름의 구조를 파악하기 위하여 변수들 간의 전이엔트로피(Transfer entropy)와 상호정보(Mutual Information)를 계산하는 방법을 사용하였다. 또한 시계열 자료를 이용함으로써 변수들 간에 정보가 전달되는 시간 규모의 특성을 파악할 수 있었다. 최종적으로, 계산한 정보 흐름을 시각화하여 프로세스 네트워크 구조를 나타내었다. 결과는 월별로 생태계의 정보 흐름의 종류, 방향과 시간 규모, 그에 따른 프로세스 간 상호 작용의 특징 등을 보여준다. 이를 통해 계절적 환경 변화에 따라 시스템의 네트워크 구조와 상태가 어떻게 변화하는지 이해할 수 있을 것이다. 이 연구는 추후 우리 연구실에서 생산한 8 년 자료에 적용함으로써 다양한 날씨 및 기후변화와 환경 변화에 따라 생태계의 구조와 상태가 어떻게 변화하는지 연구하는 시작점이 될 것이다. 이 접근법은 단위나 차원에 무관하게 다양한 종류의 자료에 적용할 수 있는 반면에, 일관성 있게 정의된 시스템의 상태 및 그 상태를 구성하는 주요 하부 시스템들의 네트워크 상태를 이해하는데 이용될 수 있다. 본 연구는 비평형 열역학과 복잡계의 관점에서 바라 본 시스템 사고를 적용하려 하는 여러 연구 분야에 새로운 도전을 촉발할 좋은 선행연구가 될 것이라 기대된다.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.