• 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.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.171-180
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• 2001

Purpose : Expression of TIMP, intrinsic inhibitor of MMP, is regulated by signal transduction in response to genotoxins and is likely to be an important step in metastasis, angiogenesis and wound healing after ionizing radiation. Therefore, we studied radiation mediated TIMP expression and its mechanism in head and neck cancer cell lines. Materials and Methods : Human head and neck cancer cell lines established at Asan Medical Center were used and radiosensitivity $(D_0)$, radiation cytotoxicity and metastatic potential were measured by clonogenic assay, n assay and invasion assay, respectively. The conditioned medium was prepared at 24 hours and 48 hours after 2 Gy and 10 Gy irradiation and expression of TIMP protein was measured by Elisa assay with specific antibodies against human TIMP. hTIMP1 promoter region was cloned and TIMP1 luciferase reporter vector was constructed. The reporter vector was transfected to AMC-HN-1 and -HN-9 cells with or without expression vector Ras, then the cells were exposed to radiation or PMA, PKC activator. EMSA was peformed with oligonucleotide (-59/-53 element and SP1) of TIMP1 promoter. Results : $D_0$ of HN-1, -2, -3, -5 and -9 cell lines were 1.55 Gy, 1.8 Gy, 1.5 Gt, 1.55 Gy and 2.45 Gy respectively. n assay confirmed cell viability, over $94\%$ at 24hrs, 48hrs after 2 Gy irradiation and over 73% after 10 Gy irradiation. Elisa assay confirmed that cells secreted TIMP1, 2 proteins continuously. After 2 Gy irradiation, TIMP2 secretion was decreased at 24hrs in HN-1 and HN-9 cell lines but after 10 Gy irradiation, it was increased in all cell lines. At 48hrs after irradiation, it was increased in HN-1 but decreased in HN-9 cells. But the change in TIMP secretion by RT was mild. The transcription of TIMP1 gene in HN-1 was induced by PMA but in HN-9 cell lines, it was suppressed. Wild type Ras induced the TIMP-1 transcription by 20 fold and 4 fold in HN-1 and HN-9 respectively. The binding activity to -59/-53, AP1 motif was increased by RT, but not to SP1 motif in both cell lines. Conclusions : We observed the difference of expression and activity of TIMPs between radiosensitive and radioresistant cell line and the different signal transduction pathway between in these cell lines may contribute the different radiosensitivity. Further research to investigate the radiation response and its signal pathway of TIMPs is needed.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.

• Journal of Korea Soil Environment Society
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• v.1 no.1
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• pp.89-102
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• 1996

An integrated model is presented to describe underground flow and mass transport, using a multicomponent multiphase approach. The comprehensive governing equation is derived considering mass and force balances of chemical species over four phases(water, oil, air, and soil) in a schematic elementary volume. Compact and systemati notations of relevant variables and equations are introduced to facilitate the inclusion of complex migration and transformation processes, and variable spatial dimensions. The resulting nonlinear system is solved by a multidimensional finite element code. The developed code with dynamic array allocation, is sufficiently flexible to work across a wide spectrum of computers, including an IBM ES 9000/900 vector facility, SP2 cluster machine, Unix workstations and PCs, for one-, two and three-dimensional problems. To reduce the computation time and storage requirements, the system equations are decoupled and solved using a banded global matrix solver, with the vector and parallel processing on the IBM 9000. To avoide the numerical oscillations of the nonlinear problems in the case of convective dominant transport, the techniques of upstream weighting, mass lumping, and elementary-wise parameter evaluation are applied. The instability and convergence criteria of the nonlinear problems are studied for the one-dimensional analogue of FEM and FDM. Modeling capacity is presented in the simulation of three dimensional composite multiphase TCE migration. Comprehesive simulation feature of the code is presented in a companion paper of this issue for the specific groundwater or flow and contamination problems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.352-361
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• 2021

An underwater positioning method that can be applied to structures for underwater construction is being developed at the Korea Institute of Ocean Science and Technology. The method uses an extended Kalman filter (EKF) based on an inertial navigation system for precise and continuous position estimation. The observation matrix was configured to be variable in order to apply asynchronous measured sensor data in the correction step of the EKF. A Doppler velocity logger (DVL) can acquire signals only when attached to the bottom of an underwater structure, and it is difficult to install and recover. Therefore, a complex sensor device for underwater structure attachment was developed without a DVL in consideration of an underwater construction environment, installation location, system operation convenience, etc.. Its performance was verified through a water tank test. The results are the measured underwater position using an ultra-short baseline, the estimated position using only a position vector, and the estimated position using position/velocity vectors. The results were compared and evaluated using the circular error probability (CEP). As a result, the CEP of the USBL alone was 0.02 m, the CEP of the position estimation with only the position vector corrected was 3.76 m, and the CEP of the position estimation with the position and velocity vectors corrected was 0.06 m. Through this research, it was confirmed that stable underwater positioning can be carried out using asynchronous sensors without a DVL.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• The Journal of Information Technology
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• v.7 no.2
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• pp.93-104
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• 2004

Sports scene is characterized by large amount of global motion due to pan and zoom of camera motion, and includes many small objects moving independently. Some short period of sports games is thrilling to televiewers, and important to producers. At the same time that kinds of scenes exhibit exceptionally dynamic motions and it is very difficult to analyze the motions with conventional algorithms. In this thesis, several algorithms are proposed for global motion analysis on these dynamic scenes. It is shown that proposed algorithms worked well for motion compensation and panorama synthesis. When cascading the inter frame motions, accumulated errors are unavoidable. In order to minimize these errors, interpolation method of motion vectors is introduced. Affined transform or perspective projection transform is regarded as a square matrix, which can be factorized into small amount of motion vectors. To solve factorization problem, we preposed the adaptation of Newton Raphson method into vector and matrix form, which is also computationally efficient. Combining multi frame motion estimation and the corresponding interpolation in hierarchical manner enhancement algorithm of motion parameters is proposed, which is suitable for motion compensation and panorama synthesis. The proposed algorithms are suitable for special effect rendering for broadcast system, video indexing, tracking in complex scenes, and other fields requiring global motion estimation.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.117-124
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• 2007

This paper describes methodology that enables animators to create the facial expression animations and to control the facial expressions in real-time by reusing motion capture datas. In order to achieve this, we fix a facial expression state expression method to express facial states based on facial motion data. In addition, by distributing facial expressions into intuitive space using LLE algorithm, it is possible to create the animations or to control the expressions in real-time from facial expression space using user interface. In this paper, approximately 2400 facial expression frames are used to generate facial expression space. In addition, by navigating facial expression space projected on the 2-dimensional plane, it is possible to create the animations or to control the expressions of 3-dimensional avatars in real-time by selecting a series of expressions from facial expression space. In order to distribute approximately 2400 facial expression data into intuitional space, there is need to represents the state of each expressions from facial expression frames. In order to achieve this, the distance matrix that presents the distances between pairs of feature points on the faces, is used. In order to distribute this datas, LLE algorithm is used for visualization in 2-dimensional plane. Animators are told to control facial expressions or to create animations when using the user interface of this system. This paper evaluates the results of the experiment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2594-2603
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• 1997

A novel method of adaptive beam forming is presented in this paper. The proposed technique provides for a suboptimal beam pattern that increases the Signal to Noise/Interference Ratio (SNR/SIR), thus, eventually increases the capacity of the communication channel, under an assumption that the desired signal is dominant compared to each component of interferences at the receiver, which is precoditionally achieved in Code Division Multiple Access (CDMA) mobile communications by the chip correlator. The main advantages of the new technique are:(1)The procedure requires neither reference signals nor training period, (2)The signal interchoerency does not affect the performance or complexity of the entire procedure, (3)The number of antennas does not have to be greater than that of the signals of distinct arrival angles, (4)The entire procedure is iterative such that a new suboptimal beam pattern be generated upon the arrival of each new data of which the arrival angle keeps changing due tot he mobility of the signal source, (5)The total amount of computation is tremendously reduced compared to that of most conventional beam forming techniques such that the suboptimal beam pattern be produced at vevery snapshot on a real-time basis. The total computational load for generating a new set of weitht including the update of an N-by-N(N is the number of antenna elements) autocovariance matrix is $0(3N^2 + 12N)$. It can further be reduced down to O(11N) by approximating the matrix with the instantaneous signal vector.