• Proceedings of the KSR Conference
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• 2002.05a
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• pp.364-369
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• 2002

The purpose of this paper is to introduce the concept design and the structural strength of the double-deck rolling stock vehicle. Aluminum is very useful material for the carbody structure due to its characteristic of light weight. Large alumillum extrusion profiles(panels) have toe of merits such as easy production of complicated shapes, reduction of welding and cutting lines, and cutting down the labor cost. AED type is being applied to the standard EMUs and the EMUs Kwangju subway in Korea. Light material recommended the double-deck rolling stock vehicle because the center of gravity of the train is higher and its weight is heavier than those of the normal vehicle. So we applied the technology of the large aluminum extrusion profiles(panels) to the double-deck vehicle. We performed the structural strength analysis and examined its safety.

• IE interfaces
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• v.16 no.1
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• pp.111-116
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• 2003

Business integration has been considered as one of the most critical success factors that enable the firms to gain competitive edges. Despite this trend, it has also been found among not a few companies that the activities that should be functionally tied with are performed even independently. In this study, an integrated model of production planning and inventory has been developed. Computerization of the production planning activities is proposed and implemented. We also proposed the reasonable inventory levels of each item using historic data of the items, which are composed of safety stock from the given fill-rate, operating stock from the production patterns, and reserved stock from the production planning. This study has helped the firm to have clearer job definition of the related processes, to tightly control the inventory by setting and tracing the reasonable fill rates for every product, and to quickly respond to the market changes through the computerized production planning process.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Proceedings of the Technology Innovation Conference
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• 1999.12a
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• pp.159-177
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• 1999

Many companies still struggle with the issue of research and development(R&D) performance measurement, in particular, the nonfinancial performance measurement of R&D with coming of knowledge-based society, Of course, we would not deny the fact that financial measures play the central role in assessing the overall performance of R&D, The aim of this paper is to provide the new model to evaluate the quantitative value of technology (nonfinancial benefits). This new model is based on the technology stock(technology level) acquired in R&D process, That is, we take it for granted that the acquired technology below a certain level(<70% compare to the advanced country) can not be utilized in developing the new products or in proving the manufacturing processes, The evaluation model we create can explains the quantitative relation between the technology stock and the market value considering R&D expenditure to acquire the technology above certain level(>70%) and cost to prevent the technology obsolescence. The value of non-destructive testing technology, which is one of the electric Power technology, is measured quantitatively using our new model as a case study, We also discussed briefly the possibility of capitalization of the measured technology value.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.711-719
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• 2016

In this study, we derived an theoretical equation, using a simplified spring-mass model for the rolling stock, to obtain the overriding behavior of a leading vehicle, which is considered as the main factor in train accidents. To verify the derived equation, we created a simple 2D model based on the theoretical model, and a simple 3D model considering the characteristics of the power bogie. We then compared the theoretical results with the simulation results obtained using LS-DYNA. The maximum relative derivations in the vertical displacements at the first end-buffer, which is the most important point in overriding, were 3.5 [%] and 1.7 [%] between the two results. Further, we evaluated collision-induced overriding displacements using the theoretical equation for a rubber draft gear, a hydraulic buffer under various collision conditions. We have suggested a theoretical approach for the realization of overriding collision accidents or the energy absorption design of the front end of trains.

• Korean Journal of Food Science and Technology
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• v.41 no.3
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• pp.231-237
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• 2009

Trans free margarine stock (TFMS) was produced by lipase-catalyzed synthesis of fully hydrogenated soybean oil (FHSBO), avocado oil (AO) and palm oil (PO). A blend of FHSBO, AO, and PO with a 1:5:4 (30:150:120 g, respectively) ratio was interesterified with lipozyme RM IM(from Rhizomucor miehei) in a 1 L-batch type reactor at 65 for 12 hr, and the physicochemical and melting properties of TFMS were compared with commercial margarine. The solid fat content (%) of the TFMS was analyzed at 25, 30, and $35^{\circ}C$, respectively, while its melting point was $37.8^{\circ}C$. The trans fatty acid content of the TFMS was below 0.1%. It also had acid, saponification, and iodine values of 0.4, 173.9, and 58.6, respectively. In HPLC chromatograms of the TFMS, newly synthesized peaks of triacylglycerol molecules were observed by using reverse-phase HPLC with evaporative light-scattering detection. Normal-phase HPLC with UV detection was used to quantify tocopherols in the TFMS, indicating that its ${\alpha}-$, ${\gamma}-$ and ${\delta}$-tocopherol contents were 5.7, 2.1, and 1.7 mg/100 g, respectively.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.431-434
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• 2008

In the present study, direct quenching of alloyed steel after hot forging was simulated using commercial finite element program, $FORGE^{TM}$. A typical heat treatment of alloyed steels consists of quenching for hard martensite and subsequent tempering for toughness. In the practice, forgings which cool down to room temperature are heated to temperature of austenite regime. As investigated in the present study, direct quenching of hot forged stock would be beneficial in terms of energy saving. This process has already been propose and termed as ausforging or forged hardening. However, it is well known that quenching temperature would be the most critical factor to control heat treated forging properties. And it is very difficult to control quenching temperature when forged stock gets directly quenched after forging. In this study, we have calculated final forging temperature of stock. Also, quenching simulation was conducted using a series of material parameter which were also calculated using JMATpro, a commercial program for physical properties of materials.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.41-47
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• 2016

The rolling stock is composed of several cars. In order to operate in combination, it is necessary to connect the device, called coupler, between the rolling stocks. When the collision occurs between cars, couplers should be able to absorb the shock. Urban railway has used only rubber absorbers. But recently, the hydraulic buffer has been considered in general railway. In order to know the performance of the buffer it should be conducted to experiments. But whenever this combination change, we should experiments to know a lot of the dynamic behavior of each coupler. These experiments are generally replaced by the simulation, since a lot of time and cost consuming. The quasi-static map of hydraulic buffer obtained by the experiments is required for the simulation. However, the experiments for obtaining such a quasi-static map is costly and time consuming. In this paper, it proposes a method for deriving the quasi-static map of hydraulic buffer from the theoretical model.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.5
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• pp.28-39
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• 2003

This study was carried out to elucidate effective restoration measures for natural forest with recycling native tree un site from November 2001 to October 2002 to obtain a basic information for revegetation measure, eight experimental treatment was done and the length of stump, root-ball size of stump, antisepsis treatment of trunk cut, Planting season and contents of organic matter in soil were effective on regrowth of root-stock. Thirteen tree species including Quercus acutissima among twenty tree species showed outstanding sprout and survival rate(over 90 percent), Planting in November and combinated planting with 5 trees and 9 shrubs of root-stock per 100$m^2$ plot showed a good growth. And 10 percent of organic matter plot showed a good crown coverage.