• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.35-47
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• 2001

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence. numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristic of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a methodology of motor vehicle emission calculation by using real-time traffic data was studied. A methodology for estimating emissions of CO at a test area in Seoul. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It was calculated speed-related mass of CO emission from traffic tail pipe of data from traffic system, and parameters are considered, volume, composition, average velocity, link length. And, the result was compared with that of a method of emission calculation by VKT(Vehicle Kilometer Travelled) of vehicles of category.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.530-534
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• 2006

조류발전은 조류 유속이 빠른 곳에 수차발전기를 설치하여 해수의 운동에너지로부터 전기를 생산하는 발전방식이다. 2001년부터 해양연구원에서는 울돌목의 우수한 조류발전 개발 여건을 바탕으로 조류에너지 실용화 기술을 개발하고 있다. 본 연구에서는 조류발전 시스템에 사용되는 헬리컬 수차의 효율을 현장실험을 바탕으로 판단하고자 하였다. 현장실험을 위하여 지름 2.2 m, 높이 2.5 m의 수차를 제작하고, 울돌목 협수로의 한 쪽 면에 쟈켓구조물을 설치하여 수차를 거치한다. 수차가 회전함에 따라 회전봉에 일정 마찰을 주어 토크와 RPM을 측정하고, 함께 측정된 유속자료를 이용하여 수차를 효율을 산정한다. 유속-수차효율, TSR(수차의 날개속도와 유속의 비)-수차효율의 상관관계로 실험결과를 고찰하였다. 1중 날개 수차인 경우에 유속 1.4에서 2.6 m/s 사이에서 최대효율이 30 - 35 % 정도였고, 2중 날개 수차에 대한 실험에서는 유속 1.4에서 2.6 m/s 사이에서 최대수차효율이 25 - 35 % 사이임을 알 수 있었다. TSR과 최대수차효율의 상관관계는 실험 case별로 조금씩 다르다. 전체적으로 1중 날개의 경우가 최대수차효율에서 2중 날개보다 TSR 값이 조금 큰 경향을 나타냄을 알 수 있다. 이것은 1중 날개가 2중 날개보다 가벼워 좀 더 큰 RPM을 발생시켜서 나타난 현상으로 생각된다. 현재의 실험결과들을 이용하여 TSR과 최대수차효율을 상관관계를 나타내는 모델식을 도출하였다. 현장시험결과를 종합하면, 현장조류발전 시설이 최소 600 kW의 전력이 생산되기 위해서는 지름 3 m, 높이 3.6 m 인 수차 3개가 하나의 축에 설치되어야하는 것으로 계산되었다. 정격유속이 4.8 m/s이고 수차의 지름이 3m 라면, 최적 전력발생시의 RPM은 1중 날개의 경우 79이고 2중 날개의 경우는 63정도임을 추정할 수 있었다.촬영하여 실시간으로 전송하기 때문에 홍수시 하천 상황에 대한 모니터링 목적으로 사용될 수 있다. 영상수위계는 우물통 등을 이용하는 기존 방법과 비교하여 구조물이 필요 없어 설치 비용이 저렴하고, 영상에 의한 하천 모니터링 기능을 자체적으로 가지고 있기 때문에 효율적이라고 할 수 있다.따른 4개의 평가기준과 26개의 평가속성으로 이루어진 2단계 기술가치평가 모형을 구축하였으며 2개의 개별기술에 대한 시범적용을 실행하였다.하는 것으로 추정되었다.면으로의 월류량을 산정하고 유입된 지표유량에 대해서 배수시스템에서의 흐름해석을 수행하였다. 그리고, 침수해석을 위해서는 2차원 침수해석을 위한 DEM기반 침수해석모형을 개발하였고, 건물의 영향을 고려할 수 있도록 구성하였다. 본 연구결과 지표류 유출 해석의 물리적 특성을 잘 반영하며, 도시지역의 복잡한 배수시스템 해석모형과 지표범람 모형을 통합한 모형 개발로 인해 더욱 정교한 도시지역에서의 홍수 범람 해석을 실시할 수 있을 것으로 판단된다. 본 모형의 개발로 침수상황의 시간별 진행과정을 분석함으로써 도시홍수에 대한 침수위험 지점 파악 및 주민대피지도 구축 등에 활용될 수 있을 것으로 판단된다. 있을 것으로 판단되었다.4일간의 기상변화가 자발성 기흉 발생에 영향을 미친다고 추론할 수 있었다. 향후 본 연구에서 추론된 기상변화와 기흉 발생과의 인과관계를 확인하고 좀 더 구체화하기 위한 연구가 필요할 것이다.게 이루어질 수 있을 것으로 기대된다.는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전

• The Journal of Industrial Distribution & Business
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• v.1 no.1
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• pp.33-41
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• 2010

In the age of globalization, effective and efficient corporate management is becoming more important as domestic and international business circumstances changes. In the middle of endless changes in business circumstances, fast reaction to customers and market, and offering customized goods and service became essential. In this respect, CRM designed to utilize customer information scientifically and systematically, has become an essential system and marketing strategy to enhance corporate competitiveness. CRM has placed the importance of customers in the front of marketing and has focused every process and business minds on customers. Recent change in the market and the trend of establishing and introducing CRM system has lead us to concentrate on the introduction of CRM in the financial institutions. This study searched for several views about CRM in academic and industrial papers. Through theoretical approach on CRM, the background of the introduction of CRM, the purpose of CRM, the characteristic and application of CRM, and the expected effect of CRM will be discussed. This study is focused on financial institutions where CRM is widely used. And through documents about examples of the introduction of CRM, the status of the establishment of domestic CRM and the necessity and trend of CRM will be discussed. Also the problem of CRM in the financial institutions and the improvement of CRM in domestic banks will be analyzed. When discussing CRM in the financial area, customers are the main source of corporate profit and through relationship management with the customers enhancing loyalty and maximizing profit can be obtained. Especially in CRM in financial institutions, maintaining existing customers makes higher profit ratio, so repurchasing and cross selling becomes important for obtaining lifetime value of existing customers who contribute to most of the profit of corporations. As a result, CRM should be completely customer oriented. CRM in financial institutions is not merely marketing work, but organizational competence which is made up of standardized work process through total process integration inside the corporation. Corporations which plan to introduce CRM should analyze the characteristics and conditions of corporations and establish purpose and strategy of CRM. And they need long term view to find out the factors which best fit for the introduction of CRM. To enable this, strategy composed of daily marketing activity and CRM concept is necessary. Also continued improvement through drill and training for operating organization should be followed to maintain CRM well. And corporate culture must settle customer as the center of corporate value. The race for introducing and improving CRM has already begun. CRM should not be regarded as a choice. It should be accepted as something essential. In this reality financial institutions should solve subdivision problem of customers and necessity of customers with the mind of 'customer's profit is my profit'. Customer focused management should not be emphasized only by words. Efforts like viewing from the customer's point must be nurtured to provide methods to help customers. That is, we should not just follow what is done in foreign countries. We should solve the problem of our customers according to the situation of our country, our industry, our corporation. Then we can gain the trust of customers, and the value derived from the customers will become the background of CRM which will lead the corporation to success.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• Korean Journal of Poultry Science
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• v.41 no.2
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• pp.99-104
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• 2014

The objective of this study is to analyze the production of extra-large egg and assess the impacts of exogenous factors in feeding the layer chicken. The main results of this study are as follows; First, feeding rations on the basics of statistics, internal maximum and minimum temperature and, the age at first egg affect the production of extra-large egg. Second, implicating the standardized coefficients from the conclusion of regression model estimating suggest that the amount of feed has the greatest impact on production followed by the age at first egg. Third, by using the elasticity of output and the volatility in the production, the result suggest that among the independent variable factors in the external volatility, the biggest one goes to feed ration, and the age at first egg follows. In order to control the production volatility in the extra-large egg production of the farms, it is necessary to manage an efficient feeding based on feed ration, age at first egg and, the maximum and minimum temperature inside the farm. Taken together, the results demonstrates that it should be concentrated by controlling the exogenous factors affecting extra large egg production and the management system construct, to increase extra-large egg production and the income of farmers at the same time.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.119-129
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• 2004

This paper is designed to report the results of development and validation procedures in relation to the Freeway Incident Management System (FIMS) prototype development as part of Intelligent Transportation Systems Research and Development program. The central core of the FIMS is an integration of the component parts and the modular, but the integrated system for freeway management. The whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Korean freeway system. After through review and analysis of vehicle detection data, the pilot site led to the utilization of different technologies in relation to the specific needs and character of the implementation. This meant that the existing system was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The system validation specifications have identified two component data collection and analysis patterns which were outlined in the validation specifications; the on-line and off-line testing procedural frameworks. The off-line testing was achieved using asynchronous analysis, commonly in conjunction with simulation of device input data to take full advantage of the opportunity to test and calibrate the incident detection algorithms focused on APID, DES, DELOS and McMaster. The simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.109-115
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• 2015

The new medical device technologies for bio-signal information and medical information which developed in various forms have been increasing. Information gathering techniques and the increasing of the bio-signal information device are being used as the main information of the medical service in everyday life. Hence, there is increasing in utilization of the various bio-signals, but it has a problem that does not account for security reasons. Furthermore, the medical image information and bio-signal of the patient in medical field is generated by the individual device, that make the situation cannot be managed and integrated. In order to solve that problem, in this paper we integrated the QR code signal associated with the medial image information including the finding of the doctor and the bio-signal information. bio-signal. System implementation environment for medical imaging devices and bio-signal acquisition was configured through bio-signal measurement, smart device and PC. For the ROI extraction of bio-signal and the receiving of image information that transfer from the medical equipment or bio-signal measurement, .NET Framework was used to operate the QR server module on Window Server 2008 operating system. The main function of the QR server module is to parse the DICOM file generated from the medical imaging device and extract the identified ROI information to store and manage in the database. Additionally, EMR, patient health information such as OCS, extracted ROI information needed for basic information and emergency situation is managed by QR code. QR code and ROI management and the bio-signal information file also store and manage depending on the size of receiving the bio-singnal information case with a PID (patient identification) to be used by the bio-signal device. If the receiving of information is not less than the maximum size to be converted into a QR code, the QR code and the URL information can access the bio-signal information through the server. Likewise, .Net Framework is installed to provide the information in the form of the QR code, so the client can check and find the relevant information through PC and android-based smart device. Finally, the existing medical imaging information, bio-signal information and the health information of the patient are integrated over the result of executing the application service in order to provide a medical information service which is suitable in medical field.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.