• Information Systems Review
• /
• v.18 no.1
• /
• pp.157-175
• /
• 2016

Under the market economy of capitalism, several limitations reveal the inequity and redistribution problem of wealth, inefficiency of over-manufacturing and over-consumption, pollution of the natural environment, and the constraint of human liberty and dignity. The new challenge of symbiotic relationships that encourage individual corporations coincides with the need to practice social responsibility and share values to overcome these limitations. Social economy and the social enterprises that simultaneously pursue the making of corporate private profits and the realization of social values have been suggested and disseminated as alternative social value creators. Furthermore, the concept of a sharing economy, which refers to the sharing of things rather than owning them, is growing traction as a new paradigm of capitalism. However, these efforts of social enterprises have fallen short against the conflicts between private profit and social values. This study deals with the case of a start-up social corporation, "Purun Bike Sharing Inc.," which is based on a regional sharing economy business model about bike rental services that use Information and Communication Technology (ICT). This corporation pursues harmonic management to achieve a balance between private profit and social value. Its corporate mission is to achieve sharing, coexistence, and contribution for public welfare. This mission is a possible idea for use in the local community network as a core key for sustainable social enterprises. The model can also be an alternative approach to overcome the structural friction in the social corporation. This study considers the case of Purun Bike Sharing as a sustainable way to practice a sharing economy business model based on a regional cooperation network, which can be combined with social value, and to apply ICT to a sharing economy system. It also examines the definition and current state of social enterprises and the sharing economy, and the cases of the sharing economy business model for the review of prior research.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.6 no.2
• /
• pp.107-117
• /
• 2004

If doubtlessly contributes much to agriculture and rural development. The roles can be summarized as; 1. to activate rural areas and to provide more comfortable and safe rural life with equivalent services to those in urban areas, facilitating distance education, tole-medicine, remote public services, remote entertainment etc. 2. To initiate new agricultural and rural business such as e-commerce, real estate business for satellite officies, rural tourism and virtual corporation of small-scale farms. 3. To support policy-making and evaluation on optimal farm production, disaster management, effective agro-environmental resource management etc., providing tools such as GIS. 4. To improve farm management and farming technologies by efficient farm management, risk management, effective information or knowledge transfer etc., realizing competitive and sustainable farming with safe products. 5. To provide systems and tools to secure food traceability and reliability that has been an emerging issue concerning farm products since serious contamination such as BSE and chicken flu was detected. 6. To take an important and key role for industrialization of farming or lam business enterprise, combining the above roles.

• Journal of Energy Engineering
• /
• v.18 no.4
• /
• pp.221-229
• /
• 2009

This study develops the optimized operation program which enables effective and economic operation between individual and connected branch offices by analyzing the current status and influential indicators of district heating companies' capital branch offices. Accordingly, the study examines the efficiency of optimized operation program. In doing so, this study has diagnosed and analyzed various factors, such as boilers, pumps, and relevant tags (temperature, pressure, fuel amount) through investigation of individual branch offices, and finally succeeded in developing wide-ranging data base by factor covering one-year time period. Additionally, after running the optimized operation program, different branch offices, optimum preference has turned out "incinerator receiving heat from KEPCO>CHP >PLBs>PLBw." Meantime, except the connected offices, there has been no big difference between actual and optimum operation program in branch offices. Meanwhile, the integrated optimum operation program has made it possible the most optimal result only via the connecting supply and demand heat without changing received Heat from KEPCO which is the same as total productive heat. The result has showed that the reduction percentage per day is 2.45~6.80%, and the reduction cost per day is 22,727~60,077 thousand won given the randomly selected sample days. In particular, winter time shows the highest demand with the largest reduction cost whereas summer time illustrates the lowest demand with the smallest reduction cost. Given this result, reduction cost per year compared to actual heat production cost for one year theoretically would be 84 hundred million won. Also, the economic effect showed that the reduction cost percentage per year is more than 2.74% on heat production cost per year for all capital branch offices.

• The Korean Journal of Air & Space Law and Policy
• /
• v.28 no.1
• /
• pp.3-54
• /
• 2013

Nowadays, major advanced countries in aviation technology are putting their effort to develop commercial and civil Unmanned Aircraft System(UAS) due to its highly promising market demand in the future. The market scale of commercial and civil UAS is expected to increase up to approximately 8.8 billon U.S. dollars by the year 2020. The usage of commercial and civil UAS covers various areas such as remote sensing, relaying communications, pollution monitoring, fire detection, aerial reconnaissance and photography, coastline monitoring, traffic monitoring and control, disaster control, search and rescue, etc. With the introduction of UAS, changes need to be made on current Air Traffic Management Systems which are focused mainly manned aircrafts to support the operation of UAS. Accordingly, the legislation for the UAS operation should be followed. Currently, ICAO's Unmanned Aircraft System Study Group(UASSG) is leading the standardization process of legislation for UAS operation internationally. However, some advanced countries such as United States, United Kingdom, Australia have adopted its own legislation. Among these countries, United States is most forth going with President Obama signing a bill to integrate UAS into U.S. national airspace by 2015. In case of Korea, legislation for the unmanned aircraft system is just in the beginning stage. There are no regulations regarding the operation of unmanned aircraft in Korea's domestic aviation law except some clauses regarding definition and permission of the unmanned aircraft flight. However, the unmanned aircrafts are currently being used in military and under development for commercial use. In addition, the Ministry of Land, Infrastructure and Transport has a ambitious plan to develop commercial and civil UAS as Korea's most competitive area in aircraft production and export. Thus, Korea is in need of the legislation for the UAS operation domestically. In this regards, I personally think that Korea's domestic legislation for UAS operation will be enacted focusing on following 12 areas : (1)use of airspace, (2)licenses of personnel, (3)certification of airworthiness, (4)definition, (5)classification, (6)equipments and documents, (7)communication, (8)rules of air, (9)training, (10)security, (11)insurance, (12)others. Im parallel with enacting domestic legislation, korea should contribute to the development of international standards for UAS operation by actively participating ICAO's UASSG.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.28 no.2
• /
• pp.75-85
• /
• 2010

The purpose of this study is to attempt new analysis on Soswaewon Garden(瀟灑園) where is Korea's traditional garden, focusing on which the tendency of its change is a relational-formation tool similar to the Eastern Mode of Thought, with paying attention to conversion as the new view of world. Accordingly, the aim is to reanalyze by connecting with Soswaewon Garden based on the theory of complexity, which tries to look at the whole through relationship rather than characteristics in individual components. Given summarizing findings, those are as follows. First, it was found that complexity shown in space and open system of physical dimension was characterized by 'event(situation)', 'non-determination' and 'homogeneous relationships between part and whole', and a variety of techniques introduced the nature positively. In particular, it was found that there were many cases of topographic usage, since the Soswaewon Garden selected its construction site proactively and was a product from architectural works in compliance with a given flow of natural topography. This has a nature of open text in the situation of emergent behaviors. Second, it was found that complexity shown in experiences and open system on the invisible dimension was characterized primarily by 'event(situation)' and 'relationships of interactive response between actors and environment', and various techniques appeared as a space for interactive combination of nature and daily experiences. This is typical of bilateral harmony based on interactions between subject and object, and between mankind and nature, and becomes also a space to accommodate temporary emergent behaviors in our life. Third, the compositional elements are reconstituted as space of organic property with dismantling steady relations. Especially, 'Soswaewon Garden's 48 poems(瀟灑園四十八詠)' will be the origin of the emotionally spatial experience to the current performers. Ultimately, the performer in the space of Soswaewon Garden simultaneously becomes a creator of space, and will generate new space with intertextuality with environment. Therefore, Soswaewon Garden becomes a place of binding me and the other together while maintaining mutual relationship based on organic thinking between a human being and nature and between the whole and a part.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.121-142
• /
• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.93-111
• /
• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Progress in Medical Physics
• /
• v.25 no.2
• /
• pp.100-109
• /
• 2014

In stereotactic body radiotherapy (SBRT), the accurate location of treatment sites should be guaranteed from the respiratory motions of patients. Lots of studies on this topic have been conducted. In this letter, a new verification method simulating the real respiratory motion of heterogenous treatment regions was proposed to investigate the accuracy of lung SBRT for Volumetric Modulated Arc Therapy. Based on the CT images of lung cancer patients, lung phantoms were fabricated to equip in $QUASAR^{TM}$ respiratory moving phantom using 3D printer. The phantom was bisected in order to measure 2D dose distributions by the insertion of EBT3 film. To ensure the dose calculation accuracy in heterogeneous condition, The homogeneous plastic phantom were also utilized. Two dose algorithms; Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB) were applied in plan dose calculation processes. In order to evaluate the accuracy of treatments under respiratory motion, we analyzed the gamma index between the plan dose and film dose measured under various moving conditions; static and moving target with or without gating. The CT number of GTV region was 78 HU for real patient and 92 HU for the homemade lung phantom. The gamma pass rates with 3%/3 mm criteria between the plan dose calculated by AAA algorithm and the film doses measured in heterogeneous lung phantom under gated and no gated beam delivery with respiratory motion were 88% and 78%. In static case, 95% of gamma pass rate was presented. In the all cases of homogeneous phantom, the gamma pass rates were more than 99%. Applied AcurosXB algorithm, for heterogeneous phantom, more than 98% and for homogeneous phantom, more than 99% of gamma pass rates were achieved. Since the respiratory amplitude was relatively small and the breath pattern had the longer exhale phase than inhale, the gamma pass rates in 3%/3 mm criteria didn't make any significant difference for various motion conditions. In this study, the new phantom model of 4D dose distribution verification using patient-specific lung phantoms moving in real breathing patterns was successfully implemented. It was also evaluated that the model provides the capability to verify dose distributions delivered in the more realistic condition and also the accuracy of dose calculation.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.139-157
• /
• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Science of Emotion and Sensibility
• /
• v.18 no.1
• /
• pp.59-66
• /
• 2015

To date, contents' usability of most multimedia devices has been focused on developer not on user, which made difficult in solving the problems or fulfilling the needs while people using real system. Although user-centered UX and UI researches have been studied and have resulted in innovation in some part, it does not show great effect on usability as it is not easy to interpret human emotions and needs and to apply those to system. Usability is the matter on how deeply smart devices can interpret and analyze human mind not on how much functions and technologies are improved. This study aims to help with usability improvement based on user when people use smart devices in multimedia environment. We studied the interaction between human and contents by analyzing the effect of human emotions and personalities on preference and consumption of contents' type. This study was done by assuming that proper analysis on human emotions may increase user satisfaction on multimedia environment. We analyzed contents preference by gender and emotion. The results showed that there is significant relationship between 'Happy' emotion and 'Comedy Program' preference and men are more prefer it than women. However, it does not reveal any significant relationship between 'Sad' emotion and contents preferences but women are slightly more prefer 'Comedy Program' than men. This result supports the Zillmann's 'mood based management', which suggests that the needs for pleasant contents are revealed to relieve sadness when people are in a sad mood. In addition, our finding corresponds with Oliver's insistence on meeting all four factors, insight, meaningfulness, understanding and reflection, rather than just pleasure for more satisfaction. This study focused on temporary emotional factors and contents and additionally on effect of users' emotion, personality and preference on type of contents consumption. This relationship between emotions and contents study would suggest the better direction for developing smart devices with great contents usability and user satisfaction in the future.