• Title/Summary/Keyword: Business-to-Business

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A Study on the Cost Analysis of Service Export - K SME Case of MICE-related Industry - (서비스 수출원가 분석 - MICE 산업 관련 중소기업 사례연구 -)

  • Park, Moon-Suh
    • International Commerce and Information Review
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    • v.13 no.4
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    • pp.485-516
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    • 2011
  • Republic of Korea is small nation that is comprised of 0.7% of the world population and occupying just 0.07% of the world territory. Despite this, Republic of Korea once again proved herself to be as the world's major economic powerhouse by becoming the world's 7th largest exporter in 2010. However, the reality is that Republic of Korea is still significantly concerned about the volatile economic nature and anxiety that is spread across the globe since the global depression that began at the end of 2008 and the financial crisis that has been threatening the Euro-Zone recently. This has resulted in the nation reaching the limitation in significant economic growth and limited creation of jobs within the nation and due to such circumstances, the nation is becoming more aware of the fact that she needs to pay more attention on the service sector and service exports if she was to see a more positive economic outlook in the upcoming future. This research is aimed to analyse the cost that is associated with the service export sector, by examining a number of enterprises in relation to the MICE(Meeting Incentives Convention Exhibition) industry which certainly has both direct and indirect influences on the service exports of the nation Further, the prime goal of the research is to encourage the SMEs of Korea, who have substandard experience associated to foreign exports, to intensify and increase service exports and also the goal extends to the degree to suggest appropriate assistance measures to aid these enterprises to achieve such goals. This research is fundamentally designed and based on the literature research associated with the MICE industry and also, this research is premeditated through the analysis of the case of exports to Vietnam. As the result of research, it has been found out that SMEs in the MICE industry and those of in service export sector are reluctant or even feel insecure to attempt any kind of export of their services mainly due to; the lack of foreign market information and also the lack of experience associated with service exports. Furthermore, it has also been revealed that the difficulty to estimate the validity and profitability of service the export is a significant factor withholding those enterprises from attempting any service export to the foreign markets. Henceforth, in order to aid and stimulate service export to the foreign markets by these SMEs (including those in association with the MICE industry), it is imperative to prepare an one-stop service export assistance program which would provide the information associated with marketing, law and legislation, taxation system and financial area in regard to the global markets.

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Social Tagging-based Recommendation Platform for Patented Technology Transfer (특허의 기술이전 활성화를 위한 소셜 태깅기반 지적재산권 추천플랫폼)

  • Park, Yoon-Joo
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.53-77
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    • 2015
  • Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

Deep Learning Architectures and Applications (딥러닝의 모형과 응용사례)

  • Ahn, SungMahn
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.127-142
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    • 2016
  • Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

Personal Information Overload and User Resistance in the Big Data Age (빅데이터 시대의 개인정보 과잉이 사용자 저항에 미치는 영향)

  • Lee, Hwansoo;Lim, Dongwon;Zo, Hangjung
    • Journal of Intelligence and Information Systems
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    • v.19 no.1
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    • pp.125-139
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    • 2013
  • Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

A Study in an Effective Programs for Emergency Care Delivery System (응급의료 전달체계의 충실 방안)

  • Kwon Sook Hee
    • Journal of Korean Public Health Nursing
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    • v.9 no.1
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    • pp.83-102
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    • 1995
  • As the society is being industrialized, the fast-paced economic development that has caused substantial increase in cerebrovascular and coronary artery diseases and the industrial development and increased use of means of transportation have resulted in the rapid rise of incidents in external injuries as well. So the pubic has become acutely aware of the need for fast and effective emergency care delivery system. The goal of emergency care delivery system is to meet the emergency care needs of patients. The emergency care delivery system is seeking to efficiently satisfy the care needs of people. Therefore the purpose of this study is designed to develop an effective programs for emergency care delivery system in Korea. The following specific objectives were investigated. This emergency care delivery system must have the necessary man power, for transfering the patients, communication net work, and emergency care facilities. 1) Man power Emergency care requires n0t only specialized traning in the emergency treatment but also knowledge and experience i11 other related area, so emergency care personnel traning program should be designed in order to adapt to the specific need of emergency patients. It will be necessary to ensure professional personnel who aquires the sufficient traning and experience for emergency care and to look for legal basis. We have to develop re-educational programs for emergency nurse specialist. They should be received speciality of emergency nursing care so that they will work actively and positively in emergency part. Emergency medical doctor and nurse specialist should be given an education which is related in emergency and critical care. Emergency care personnel will continue to provide both acute and continuing care as partner with other medical team. 2) Transfering the patients. Successful management of pre-hospital care requires adequate traning for the emergency medical technician. Traning program should be required to participate in a actual first aids activites in order to have apportunities to acquire practical skills as well as theoretical knowledge. The system of emergency medical technician should be remarkablly successful with first responder firefighters. Establishing this system must add necessary ambulances operating at any given time. It will be necessary to standardize the ambulance size and equipment. Ambulance should be arranged with each and every fire station. 3) Communication net work. The head office of emergency commumication network should be arranged with the head office of fire station in community. It is proposed that Hot-line system for emergency care should be introduce. High controlled ambulance and thirtial emergency center should simultaneously equip critical-line in order to communication with each other. Ordinary ambulance and secondary emergency facility should also simultaneously equip emergency-line in order to communication with each other. 4) Emergency care facilities. Primary emergency care facilities should be covered with the ambulatory emergency patients-minor illness and injuires. Secondary emergency care facilities should be covered with the emergency admission patients. Third emergency care center should be covered with the critical patients who need special treatments and operation. Secondary and third emergency care facilities should employ emergency medical doctor and emergency nurse specialist to treat in-patients with severe and acute illness and multiple injuires. It should be fashioned for a system of emergency facilities that meets emergency patients needs. Provide incentives for increased number of emergency care facilities with traning in personal/clinical emergency care. 5) Finance It is recommended to put the finance of a emergency care on a firm basis. The emergency care delivery system should be managed by the government or accreditted organizations. In order to facilitate this relevant program the fund is needed for more efficient and effective emergency researchs, service, programs, and policy. 6) Gaining understanding and co-operation of pubic It is also important to undertake pubic education to improve understanding of first aids and C. P. R of individuals, communities and business. It is proposed that teachers and health officers be certified in C. P. R. The C. P. R education can be powerful influence save lives. Lastly appropriate emergency care information must be provided to the pubic for assisting them in choosing emergency care.

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An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost (비대칭 오류비용을 고려한 분류기준값 최적화와 SVM에 기반한 지능형 침입탐지모형)

  • Lee, Hyeon-Uk;Ahn, Hyun-Chul
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.157-173
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    • 2011
  • As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

Value of Information Technology Outsourcing: An Empirical Analysis of Korean Industries (IT 아웃소싱의 가치에 관한 연구: 한국 산업에 대한 실증분석)

  • Han, Kun-Soo;Lee, Kang-Bae
    • Asia pacific journal of information systems
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    • v.20 no.3
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    • pp.115-137
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    • 2010
  • Information technology (IT) outsourcing, the use of a third-party vendor to provide IT services, started in the late 1980s and early 1990s in Korea, and has increased rapidly since 2000. Recently, firms have increased their efforts to capture greater value from IT outsourcing. To date, there have been a large number of studies on IT outsourcing. Most prior studies on IT outsourcing have focused on outsourcing practices and decisions, and little attention has been paid to objectively measuring the value of IT outsourcing. In addition, studies that examined the performance of IT outsourcing have mainly relied on anecdotal evidence or practitioners' perceptions. Our study examines the contribution of IT outsourcing to economic growth in Korean industries over the 1990 to 2007 period, using a production function framework and a panel data set for 54 industries constructed from input-output tables, fixed-capital formation tables, and employment tables. Based on the framework and estimation procedures that Han, Kauffman and Nault (2010) used to examine the economic impact of IT outsourcing in U.S. industries, we evaluate the impact of IT outsourcing on output and productivity in Korean industries. Because IT outsourcing started to grow at a significantly more rapid pace in 2000, we compare the impact of IT outsourcing in pre- and post-2000 periods. Our industry-level panel data cover a large proportion of Korean economy-54 out of 58 Korean industries. This allows us greater opportunity to assess the impacts of IT outsourcing on objective performance measures, such as output and productivity. Using IT outsourcing and IT capital as our primary independent variables, we employ an extended Cobb-Douglas production function in which both variables are treated as factor inputs. We also derive and estimate a labor productivity equation to assess the impact of our IT variables on labor productivity. We use data from seven years (1990, 1993, 2000, 2003, 2005, 2006, and 2007) for which both input-output tables and fixed-capital formation tables are available. Combining the input-output tables and fixed-capital formation tables resulted in 54 industries. IT outsourcing is measured as the value of computer-related services purchased by each industry in a given year. All the variables have been converted to 2000 Korean Won using GDP deflators. To calculate labor hours, we use the average work hours for each sector provided by the OECD. To effectively control for heteroskedasticity and autocorrelation present in our dataset, we use the feasible generalized least squares (FGLS) procedures. Because the AR1 process may be industry-specific (i.e., panel-specific), we consider both common AR1 and panel-specific AR1 (PSAR1) processes in our estimations. We also include year dummies to control for year-specific effects common across industries, and sector dummies (as defined in the GDP deflator) to control for time-invariant sector-specific effects. Based on the full sample of 378 observations, we find that a 1% increase in IT outsourcing is associated with a 0.012~0.014% increase in gross output and a 1% increase in IT capital is associated with a 0.024~0.027% increase in gross output. To compare the contribution of IT outsourcing relative to that of IT capital, we examined gross marginal product (GMP). The average GMP of IT outsourcing was 6.423, which is substantially greater than that of IT capital at 2.093. This indicates that on average if an industry invests KRW 1 millon, it can increase its output by KRW 6.4 million. In terms of the contribution to labor productivity, we find that a 1% increase in IT outsourcing is associated with a 0.009~0.01% increase in labor productivity while a 1% increase in IT capital is associated with a 0.024~0.025% increase in labor productivity. Overall, our results indicate that IT outsourcing has made positive and economically meaningful contributions to output and productivity in Korean industries over the 1990 to 2007 period. The average GMP of IT outsourcing we report about Korean industries is 1.44 times greater than that in U.S. industries reported in Han et al. (2010). Further, we find that the contribution of IT outsourcing has been significantly greater in the 2000~2007 period during which the growth of IT outsourcing accelerated. Our study provides implication for policymakers and managers. First, our results suggest that Korean industries can capture further benefits by increasing investments in IT outsourcing. Second, our analyses and results provide a basis for managers to assess the impact of investments in IT outsourcing and IT capital in an objective and quantitative manner. Building on our study, future research should examine the impact of IT outsourcing at a more detailed industry level and the firm level.

Effect of economic growth, industrial structure, efficiency improvement, decarbonization of power sector and fuel substitution for the transition to low carbon society by 2050 (2050년 저탄소 사회로의 전환을 위한 경제성장, 산업구조, 효율개선, 전력 탈탄소화와 연료 대체의 효과)

  • Park, Nyun-Bae;Hong, Sungjun;Park, Sang Yong
    • Journal of Energy Engineering
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    • v.23 no.4
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    • pp.61-72
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    • 2014
  • This paper analyzed transition pathways toward a low carbon society in Korea to meet global $2^{\circ}C$ climate target. Lower economic growth, industrial structure change, enhance of energy demand management, decarbonization of power sector, and replacement of low carbon fuel could reduce greenhouse gas (GHG) emission from fuel combustion in 2050 by 67% against in 2011, or by 74% against in BAU (Business-As-Usual). Lower economic growth contributes to 13% of cumulative emission reduction relative to BAU, industrial structure change 9%, enhance of energy demand management 72%, decarbonization of power sector 5% and replacement of low carbon fuel 1% respectively. Final energy consumption in 2050 needs to be reduced to 50% relative to 2011, or to 41% relative to BAU. Nuclear, coal and renewable energy represent 31%, 40%, 2% respectively among electricity generation in 2011, but 38%, 2%, 32% in 2050. CCS represents 23% of total generation in 2050. Emission intensity of electricity in 2050 was decreased to 19% relative to 2011, or to 24% relative to BAU. Primary energy in 2050 was decreased to 64% compared to 2011, or to 44% compared to BAU. Final energy consumption, primary energy supply and GHG emission from fuel combustion from 1990 to 2011 increased by 176%, 197%, 146%. Radical change from historical trend is required to transit toward a low carbon society by 2050. Appropriate economic growth, structural change to non-energy intensive industries, energy technology research, development and deployment (RD&D) in terms of enhancement of energy efficiency and low carbon energy supply technologies, and fuel change to electricity and renewable energy are key instruments.

An Efficient Heuristic for Storage Location Assignment and Reallocation for Products of Different Brands at Internet Shopping Malls for Clothing (의류 인터넷 쇼핑몰에서 브랜드를 고려한 상품 입고 및 재배치 방법 연구)

  • Song, Yong-Uk;Ahn, Byung-Hyuk
    • Journal of Intelligence and Information Systems
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    • v.16 no.2
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    • pp.129-141
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    • 2010
  • An Internet shopping mall for clothing operates a warehouse for packing and shipping products to fulfill its orders. All the products in the warehouse are put into the boxes of same brands and the boxes are stored in a row on shelves equiped in the warehouse. To make picking and managing easy, boxes of the same brands are located side by side on the shelves. When new products arrive to the warehouse for storage, the products of a brand are put into boxes and those boxes are located adjacent to the boxes of the same brand. If there is not enough space for the new coming boxes, however, some boxes of other brands should be moved away and then the new coming boxes are located adjacent in the resultant vacant spaces. We want to minimize the movement of the existing boxes of other brands to another places on the shelves during the warehousing of new coming boxes, while all the boxes of the same brand are kept side by side on the shelves. Firstly, we define the adjacency of boxes by looking the shelves as an one dimensional series of spaces to store boxes, i.e. cells, tagging the series of cells by a series of numbers starting from one, and considering any two boxes stored in the cells to be adjacent to each other if their cell numbers are continuous from one number to the other number. After that, we tried to formulate the problem into an integer programming model to obtain an optimal solution. An integer programming formulation and Branch-and-Bound technique for this problem may not be tractable because it would take too long time to solve the problem considering the number of the cells or boxes in the warehouse and the computing power of the Internet shopping mall. As an alternative approach, we designed a fast heuristic method for this reallocation problem by focusing on just the unused spaces-empty cells-on the shelves, which results in an assignment problem model. In this approach, the new coming boxes are assigned to each empty cells and then those boxes are reorganized so that the boxes of a brand are adjacent to each other. The objective of this new approach is to minimize the movement of the boxes during the reorganization process while keeping the boxes of a brand adjacent to each other. The approach, however, does not ensure the optimality of the solution in terms of the original problem, that is, the problem to minimize the movement of existing boxes while keeping boxes of the same brands adjacent to each other. Even though this heuristic method may produce a suboptimal solution, we could obtain a satisfactory solution within a satisfactory time, which are acceptable by real world experts. In order to justify the quality of the solution by the heuristic approach, we generate 100 problems randomly, in which the number of cells spans from 2,000 to 4,000, solve the problems by both of our heuristic approach and the original integer programming approach using a commercial optimization software package, and then compare the heuristic solutions with their corresponding optimal solutions in terms of solution time and the number of movement of boxes. We also implement our heuristic approach into a storage location assignment system for the Internet shopping mall.

Dynamic Decision Making using Social Context based on Ontology (상황 온톨로지를 이용한 동적 의사결정시스템)

  • Kim, Hyun-Woo;Sohn, M.-Ye;Lee, Hyun-Jung
    • Journal of Intelligence and Information Systems
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    • v.17 no.3
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    • pp.43-61
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    • 2011
  • In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.