• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.193-204
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• 2022

This study analyzed the degree of influence of measurement and improvement of customer satisfaction level targeting kiosk users. In modern times, due to the development of technology and the improvement of the online environment, the probability that simple labor tasks will disappear after 10 years is close to 90%. Even in domestic research, it is predicted that 'simple labor jobs' will disappear due to the influence of advanced technology with a probability of about 36%. there is. In particular, as the demand for non-face-to-face services increases due to the Corona 19 virus, which is recently spreading globally, the trend of introducing kiosks has accelerated, and the global market will grow to 83.5 billion won in 2021, showing an average annual growth rate of 8.9%. there is. However, due to the unmanned nature of these kiosks, some consumers still have difficulties in using them, and consumers who are not familiar with the use of these technologies have a negative attitude towards service co-producers due to rejection of non-face-to-face services and anxiety about service errors. Lack of understanding leads to role conflicts between sales clerks and consumers, or inequality is being created in terms of service provision and generations accustomed to using technology. In addition, since kiosk is a representative technology-based self-service industry, if the user feels uncomfortable or requires additional labor, the overall service value decreases and the growth of the kiosk industry itself can be suppressed. It is important. Therefore, interviews were conducted on the main points of direct use with actual users centered on display color scheme, text size, device design, device size, internal UI (interface), amount of information, recognition sensor (barcode, NFC, etc.), Display brightness, self-event, and reaction speed items were extracted. Afterwards, using the questionnaire, the Kano model quality attribute classification of each expected evaluation item was carried out, and Timko's customer satisfaction coefficient, which can be calculated with accurate numerical values The PCSI Index analysis was additionally performed to determine the improvement priorities by finally classifying the improvement impact of the kiosk expected evaluation items through research. As a result, the impact of improvement appears in the order of internal UI (interface), text size, recognition sensor (barcode, NFC, etc.), reaction speed, self-event, display brightness, amount of information, device size, device design, and display color scheme. Through this, we intend to contribute to a comprehensive comparison of kiosk-based research in each field and to set the direction for improvement in the venture industry.

• Progress in Medical Physics
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• v.23 no.4
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• pp.309-316
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• 2012

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.172-180
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• 2007

Among the nuclear medicine imaging methods available today, $H_2^{15}O-PET$ is most widely used by cognitive neuroscientists to examine regional brain function via the measurement of regional cerebral blood flow (rCBF). The short half-life of the radioactively labeled probe, $^{15}O$, often allows repeated measures from the same subjects in many different task conditions. $H_2^{15}O-$ PET, however, has technical limitations relative to other methods of functional neuroimaging, e.g., fMRI, including relatively poor time and spatial resolutions, and, frequently, insufficient statistical power for analysis of individual subjects. However, recent technical developments, such as the 3-D acquisition method provide relatively good image quality with a smaller radioactive dosage, which in turn results in more PET scans from each individual, thus providing sufficient statistical power for the analysis of individual subject's data. Furthermore, the noise free scanner environment $H_2^{15}O$ PET, along with discrete acquisition of data for each task condition, are important advantages of PET over other functional imaging methods regarding studying state-dependent changes in brain activity. This review presents both the limitations and advantages of $^{15}O-PET$, and outlines the design of efficient PET protocols, using examples of recent PET studies both in the normal healthy population, and in the clinical population.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.310-321
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• 2016

This study aims to analyze the impact of elements for evaluating the festival, such as festival content, accessibility and convenience and festival image, on visitor satisfaction and behavioral intention, based on the actual case of Seoul Friendship Fair (SFF) which has been hosted by the City of Seoul every May since 1996. The SFF is participated by 64 embassies operating in Seoul and Seoul's 14 sister & friendship cities. Some 100,000 out of the total 350,000 visitors to the SFF are from overseas. The main elements of this study include survey responses about festival content and population statistics frequency analysis conducted over 277 festival visitors. As for factors, as a result of varimax rotation over main elements, technical statistics, credibility analysis and correlation analysis were done over each element group of five. Hypothesis was validated through regression analysis that the elements for evaluating local festivals have an impact on visitor satisfaction and behavioral intention. The suggestions and results of this study, both theoretical and practical, were presented to help the SFF differentiate itself and thereby become one of Seoul's most representative festivals.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.179-189
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• 2016

Recently, the convergence of knowledge based society and information telecommunication technologies has a rapid impact on politics, economics and various fields. Meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. Meta-analysis, can see the direction and size of the relationship between variables using the concept of the effect size. The factor determining behavioral intention of consumer in e-commerce can say that critically dependent variable. In a predictive factor determining behavioral intention is typical that perceived value and perceived risk. We conducted a meta-analysis and review of between perceived value, perceived risk and behavioral intention on e-commerce researches. This study focused a total of 33 research papers that established causal relationships in between perceived value, risk and behavioral intention on e-commerce published in Korea academic journals during 2000 and 2016. The result of the meta-analysis might be summarized that the effect size in the path from the perceived value to the behavioral intention with the effect size (r = .526), listed an explanatory power of 28%. In addition, it showed that the effect size in the path from perceived risk to the behavioral intention with the effect size (r = -.220), listed a negative explanatory power of 5%. Based on these findings, several theoretical and practical implications were suggested and discussed with the difference from previous researches.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.140-152
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• 2020

This study analyzed the satisfaction and utilization of the institutional applications used by child-education institutions from a parent's perspective. The research target is a parent who enlisted their children in private kindergarten institution located in Gyeong-gi province and final research outcomes are 443 in total. The difference in satisfaction and utilization have been identified with demographic descriptive statistics, students t-test, and F-test statistics analysis method. First, the result of T-test on the difference in satisfaction and utilization based on parents' employment status shows that statistically significant difference was found in convenience from overall satisfaction, communication from overall utilization and child-parent relationship. Second, the F-test of differences in satisfaction and utilization showed a statistically significant difference in education utilization variables, with all variables in educational background and motivation. In terms of utilization, parents' educational background and overall utilization, and children's age were confirmed to have significant differences in communication, children's relationship, and motivation in basic life. Third, the result of multiple regression analysis shows that all predictors, including ease of use, communication, and satisfaction with educational activities, have a statistically significant impact on utilization standard variables, relative contributions showed the highest level of communication satisfaction. Therefore this paper implicates that institutional applications can maximize educational effectiveness by checking the applicability of active communication and educational activities with parents and actively communicating with parents in early childhood through various educational content applications.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.209-220
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• 2004

The main feature of H.264 standard against conventional video standards is the high coding efficiency and the network friendliness. In spite of these outstanding features, it is not easy to implement H.264 codec as a real-time system due to its high requirement of memory bandwidth and intensive computation. Although the variable block size motion compensation using multiple reference frames is one of the key coding tools to bring about its main performance gain, it demands substantial computational complexity due to SAD (Sum of Absolute Difference) calculation among all possible combinations of coding modes to find the best motion vector. For speedup of motion estimation process, therefore, this paper proposes fast algorithms for both integer-pel and fractional-pel motion search. Since many conventional fast integer-pel motion estimation algorithms are not suitable for H.264 having variable motion block sizes, we propose the motion field adaptive search using the hierarchical block structure based on the diamond search applicable to variable motion block sizes. Besides, we also propose fast fractional-pel motion search using small diamond search centered by predictive motion vector based on statistical characteristic of motion vector.