• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.5
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• pp.99-112
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• 2020

This study empirically analyzed the relationship with major influencing factors in enhancing innovation capability of start-ups and their influence on innovation performance. If the existing innovation competency studies were analyzed from a general corporate perspective, In this study, it was analyzed from the perspective of start-up companies with less than 7 years of founding. As a result of a survey on startups, learning competency among the sub-variables of innovation competency, R&D competency and marketing competency are significant positive (+) consistent with both organizational competence related to organizational culture and organizational goals, technology commercialization competency, and close product competency. Has been shown to affect. The technical competence part does not have a significant effect on the product competency. However, it could not be interpreted that the importance of these competencies was low. This is because although technical competence did not directly affect product competency, it was analyzed as a meaningful result in relation to R&D competency. In addition, the characteristics of the company were classified into technology orientation and market orientation, and the relationship between each sub-variable was analyzed. The technical competence of a technology-oriented company did not have a significant effect on the product competency, but it was found that it had an effective effect on the R&D capacity. It is also consistent with the research findings that the initial survival rate is low as the characteristics of start-ups are often based on technology and ideas. Based on these results, There is a difference in major innovation capabilities according to the growth stage of a company. From a practical point of view, I would like to present approaches and implications for strengthening the competence of start-ups.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.683-699
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• 2014

This study examines the key factors which influence the strategic consideration and intention of adoption of internet marketing of small-and-medium sized companies, based on theoretical consideration of innovation diffusion theory and previous studies related. The result of analysis shows that the degree of market competitiveness, customer dependency, the level of internet marketing knowledge and experience have significant effects on both of strategic consideration and adoption intention for internet marketing. Also, learning commitment has negative influence on strategic consideration only, while environmental dynamism does on adoption intention. Comprehensively, the findings implies that firms consider selection of internet marketing to reduce the severity of competitiveness and that firms which has more knowledge or experience about internet marketing seems to consider selection of internet marketing through the perception of the effects of internet marketing or possible access to internet marketing. Based on the findings that the level of internet marketing knowledge and experience have much significant effects on both of strategic consideration.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.50-61
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• 2024

The market for compact houses is growing due to the demand for floor plans prioritizing user needs. However, clients often have difficulty communicating their spatial requirements to professionals including architects because they lack the means to provide evidence, such as spatial configurations or cost estimates. This research aims to create a framework that can translate sketched data-driven spatial requirements into 3D building components in BIM models to facilitate spatial understanding and provide building performance analysis to aid in budgeting in the early design phase. The research process includes developing a process model, implementing, and validating the framework. The process model describes the data flow within the framework and identifies the required functionality. Implementation involves creating systems and user interfaces to integrate various systems. The validation verifies that the framework can automatically convert sketched space requirements into walls, floors, and roofs in a BIM model. The framework can also automatically calculate material and energy costs based on the BIM model. The developed frame enables clients to efficiently create 3D building components based on the sketched data and facilitates users to understand the space and analyze the building performance through the created BIM models.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2159-2165
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• 2013

Cancer registries help to establish and maintain cancer incidence reporting system, serve as a resource for investigation of cancer and its causes, and provide information for planning and evaluation of preventive and control programs. However, their wider role in directly enhancing oncology drug access has not been fully explored. We examined the value of cancer registries in oncology drug access in the Asia-Pacific region on three levels: (1) specific registry variable types; (2) macroscopic strategies on the national level; and (3) a regional cancer registry network. Using literature search and proceedings from an expert forum, this paper covers recent cancer registry developments in eight economies in the Asia-Pacific region - Australia, China, Hong Kong, Malaysia, Singapore, South Korea, Taiwan, and Thailand - and the ways they can contribute to oncology drug access. Specific registry variables relating to demographics, tumor characteristics, initial treatment plans, prognostic markers, risk factors, and mortality help to anticipate drug needs, identify high-priority research area and design access programs. On a national level, linking registry data with clinical, drug safety, financial, or drug utilization databases allows analyses of associations between utilization and outcomes. Concurrent efforts should also be channeled into developing and implementing data integrity and stewardship policies, and providing clear avenues to make data available. Less mature registry systems can employ modeling techniques and ad-hoc surveys while increasing coverage. Beyond local settings, a cancer registry network for the Asia-Pacific region would offer cross-learning and research opportunities that can exert leverage through the experiences and capabilities of a highly diverse region.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4884-4890
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• 2015

These days most of people possesses an average of one to two mobile devices in the world and a wireless network market is gradually expanding. Wi-Fi preference are increasing in accordance with the use growth of mobile devices. A number of areas such as public agencies, health care, education, learning, and content, manufacturing, retail create new values based on Wi-Fi, and the global network is built and provides complex services. However, There exist some attacks and vulnerabilities like wireless radio device identifier vulnerability, illegal use of network resources through the MAC forgery, wireless authentication key cracking, unauthorized AP / devices attack in the next generation radio network environment. In addition, advanced security technology research, such as authentication Advancement and high-speed secure connection is not nearly progress. Therefore, this paper designed a secure communication system for message protection in next-generation wireless network environments by device identification and, designing content classification and storage protocols. The proposed protocol analyzed safeties with respect to the occurring vulnerability and the securities by comparing and analyzing the existing password techniques in the existing wireless network environment. It is slower 0.72 times than existing cypher system, WPA2-PSK, but enforces the stability in security side.

• Journal of Fashion Business
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• v.19 no.6
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• pp.82-100
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• 2015

Mobile fashion apps present much opportunity for marketers to engage consumers, however not all apps provide enough functions for their targeted audience. This study aims to determine how mobile fashion apps can be used to build brand community with consumer engagement. Qualitative data on fashion mobile apps were collected from the Apple app store and Android market during the spring and summer of 2015. A total of 110 fashion mobile apps were collected;, 50 apps were identified as apparel brands that either manufacture or sell apparel to consumers, which we categorized as "brand" fashion apps, and the remaining 60 were categorized as "non-brand" fashion apps. The result of the study can be summarized as below. The 60 non-brand fashion apps were grouped into 5 app types: shopping, searching, sharing, organizational, and informational. The main functions are for informational use and shopping needs, since at least half (31 apps) are used for either retrieving information or for shopping. However, in contrast, social networking and location were infrequent and not commonly utilized by these apps. The most common type of non-brand fashion apps available were shopping apps;, many shopping apps enable users to shop from several different websites and save their items into one universal shopping cart so that they only check out once. Most of these apps are informational and help consumers make more informed decisions on purchases;, in addition many offer location services to help consumers find these items in store. While these apps perform several functions, they do not link to social media. The 50 brand apps were grouped into 5 brand types: athletic, casual, fast fashion, luxury, and retailer. These apps were also checked for attributes to determine their functionality. The result shows that the main functions of brand fashion apps are for information (82% of the 50 apps) as well as location searching (72% of 50 apps). Conversely, these apps do not offer any photo sharing, and very few have organizational or community functions. Fashion mobile apps and m-marketing elements: To build brand community, mobile apps can be designed to motivate consumer's engagement with brands. The motivations of fashion mobile apps are useful in developing fashion mobile apps. Entertainment motives can be fulfilled with multimedia attributes, functionality motives are satisfied with organizational and location-based features, information motives with informational service, socialization with community and social network, learning and intellectual stimulation from informational attributes, and trend following through photo sharing. The 8 key attributes of mobile apps can correspond to the 4 m-marketing elements (i.e., Informative content, multimedia, interactions, and product promotions) that are further intertwined with m-branding elements. App Attributes and M-Marketing aim to Build Brand Community;, the eight key attributes can impact on 4 m-branding elements, which further contribute to building brand community by affecting consumers' perceptions of brands preference and advocacy, and their likelihood to be loyal.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.129-137
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• 2020

Currently, The autonomous vehicle market is researching and developing four-level autonomous vehicles beyond the commercialization of three-level autonomous vehicles. Because unlike the level 3, the level 4 autonomous vehicle has to deal with an emergency directly, the most important aspect of a four-level autonomous vehicle is its stability. In this paper, we propose an Optimized Vehicle Routing System (OVRS) that determines the route with the lowest probability of an accident at the destination of the vehicle rather than an immediate response in an emergency. The OVRS analyzes road and surrounding vehicle information collected by The RSU communication to predict road hazards, and sets the route for the safer and faster road. The OVRS can improve the stability of the vehicle by executing the route guidance according to the road situation through the RSU on the road like the network routing method. As a result, the RPNN of the ASICM, one of the OVRS modules, was about 17% better than the CNN and 40% better than the LSTM. However, because the study was conducted in a virtual environment using a PC, the possibility of accident of the VPDM was not actually verified. Therefore, in the future, experiments with high accuracy on VPDM due to the collection of accident data and actual roads should be conducted in real vehicles and RSUs.