• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.429-437
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• 2019

This study was conducted to examine the effect of humidification and shading during cutting propagation on growth and development of strawberry (Fragaria x ananassa Duch.) 'Maehyang' plants at a propagation stage. The runner cuttings were stuck on Nov. 23, 2017 in propagation benches set in a Venlo-type glasshouse. Four shading treatments, no shading (control, C), 55% shading with white lawn (W55), 55% black shading net (B55), or 100% black plastic film (B100) with either an intermittent fog system (H) or without fog system. The shading and fog systems were removed 2 weeks after sticking of strawberry cuttings. A nutrient solution for strawberry, which was developed by Yamazaki, was supplied once a day with electrical conductivity (EC) $1.6dS{\cdot}m^{-1}$ and pH 5.8. Growth parameters such as plant height, longest root, crown diameter, leaf chlorophyll, leaf area and fresh and dry weight were measured at 7 days and 26 days after sticking. There was no significant difference in growth of above-aerial part of strawberry. The overall growth of the strawberry roots was better grew by providing fog than that not provide fog. The root fresh weight and root dry weight after 26 days after sticking of strawberry cutting was the best in the treatment that provided fog system without shading (CH). The longest root after 26 days after sticking of strawberry cutting was the best in the treatments that provided fog system with either 55% white lawn (W55H) and 55% black shading net (B55H). These results suggest that morphogenesis of these plants were affected by humidification and shading types. In a broader perspective, these results can be used to optimize studies of other crops grown from cuttings.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Korean Journal of Poultry Science
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• v.46 no.2
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• pp.65-75
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• 2019

A number of Korean native chicken(KNC) populations were registered in FAO (Food and Agriculture Organization) DAD-IS (Domestic Animal Diversity Information Systems, http://www.fao.org/dad-is). But there is a lack of scientific basis to prove that they are unique population of Korea. For this reason, this study was conducted to prove KNC's uniqueness using 25 Microsatellite markers. A total of 548 chickens from 11 KNC populations (KNG, KNB, KNR, KNW, KNY, KNO, HIC, HYD, HBC, JJC, LTC) and 7 introduced populations (ARA: Araucana, RRC and RRD: Rhode Island Red C and D, LGF and LGK: White Leghorn F and K, COS and COH: Cornish brown and Cornish black) were used. Allele size per locus was decided using GeneMapper Software (v 5.0). A total of 195 alleles were observed and the range was 3 to 14 per locus. The MNA, $H_{\exp}$, $H_{obs}$, PIC value within population were the highest in KNY (4.60, 0.627, 0.648, 0.563 respectively) and the lowest in HYD (1.84, 0.297, 0.286, 0.236 respectively). The results of genetic uniformity analysis suggested 15 cluster (${\Delta}K=66.22$). Excluding JJC, the others were grouped in certain cluster with high genetic uniformity. JJC was not grouped in certain cluster but grouped in cluster 2 (44.3%), cluster 3 (17.7%) and cluster8 (19.1%). As a results of this study, we can secure a scientific basis about KNC's uniqueness and these results can be use to basic data for the genetic evaluation and management of KNC breeds.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.63-86
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• 2010

Personalized services directly and indirectly acquire personal data, in part, to provide customers with higher-value services that are specifically context-relevant (such as place and time). Information technologies continue to mature and develop, providing greatly improved performance. Sensory networks and intelligent software can now obtain context data, and that is the cornerstone for providing personalized, context-specific services. Yet, the danger of overflowing personal information is increasing because the data retrieved by the sensors usually contains privacy information. Various technical characteristics of context-aware applications have more troubling implications for information privacy. In parallel with increasing use of context for service personalization, information privacy concerns have also increased such as an unrestricted availability of context information. Those privacy concerns are consistently regarded as a critical issue facing context-aware personalized service success. The entire field of information privacy is growing as an important area of research, with many new definitions and terminologies, because of a need for a better understanding of information privacy concepts. Especially, it requires that the factors of information privacy should be revised according to the characteristics of new technologies. However, previous information privacy factors of context-aware applications have at least two shortcomings. First, there has been little overview of the technology characteristics of context-aware computing. Existing studies have only focused on a small subset of the technical characteristics of context-aware computing. Therefore, there has not been a mutually exclusive set of factors that uniquely and completely describe information privacy on context-aware applications. Second, user survey has been widely used to identify factors of information privacy in most studies despite the limitation of users' knowledge and experiences about context-aware computing technology. To date, since context-aware services have not been widely deployed on a commercial scale yet, only very few people have prior experiences with context-aware personalized services. It is difficult to build users' knowledge about context-aware technology even by increasing their understanding in various ways: scenarios, pictures, flash animation, etc. Nevertheless, conducting a survey, assuming that the participants have sufficient experience or understanding about the technologies shown in the survey, may not be absolutely valid. Moreover, some surveys are based solely on simplifying and hence unrealistic assumptions (e.g., they only consider location information as a context data). A better understanding of information privacy concern in context-aware personalized services is highly needed. Hence, the purpose of this paper is to identify a generic set of factors for elemental information privacy concern in context-aware personalized services and to develop a rank-order list of information privacy concern factors. We consider overall technology characteristics to establish a mutually exclusive set of factors. A Delphi survey, a rigorous data collection method, was deployed to obtain a reliable opinion from the experts and to produce a rank-order list. It, therefore, lends itself well to obtaining a set of universal factors of information privacy concern and its priority. An international panel of researchers and practitioners who have the expertise in privacy and context-aware system fields were involved in our research. Delphi rounds formatting will faithfully follow the procedure for the Delphi study proposed by Okoli and Pawlowski. This will involve three general rounds: (1) brainstorming for important factors; (2) narrowing down the original list to the most important ones; and (3) ranking the list of important factors. For this round only, experts were treated as individuals, not panels. Adapted from Okoli and Pawlowski, we outlined the process of administrating the study. We performed three rounds. In the first and second rounds of the Delphi questionnaire, we gathered a set of exclusive factors for information privacy concern in context-aware personalized services. The respondents were asked to provide at least five main factors for the most appropriate understanding of the information privacy concern in the first round. To do so, some of the main factors found in the literature were presented to the participants. The second round of the questionnaire discussed the main factor provided in the first round, fleshed out with relevant sub-factors. Respondents were then requested to evaluate each sub factor's suitability against the corresponding main factors to determine the final sub-factors from the candidate factors. The sub-factors were found from the literature survey. Final factors selected by over 50% of experts. In the third round, a list of factors with corresponding questions was provided, and the respondents were requested to assess the importance of each main factor and its corresponding sub factors. Finally, we calculated the mean rank of each item to make a final result. While analyzing the data, we focused on group consensus rather than individual insistence. To do so, a concordance analysis, which measures the consistency of the experts' responses over successive rounds of the Delphi, was adopted during the survey process. As a result, experts reported that context data collection and high identifiable level of identical data are the most important factor in the main factors and sub factors, respectively. Additional important sub-factors included diverse types of context data collected, tracking and recording functionalities, and embedded and disappeared sensor devices. The average score of each factor is very useful for future context-aware personalized service development in the view of the information privacy. The final factors have the following differences comparing to those proposed in other studies. First, the concern factors differ from existing studies, which are based on privacy issues that may occur during the lifecycle of acquired user information. However, our study helped to clarify these sometimes vague issues by determining which privacy concern issues are viable based on specific technical characteristics in context-aware personalized services. Since a context-aware service differs in its technical characteristics compared to other services, we selected specific characteristics that had a higher potential to increase user's privacy concerns. Secondly, this study considered privacy issues in terms of service delivery and display that were almost overlooked in existing studies by introducing IPOS as the factor division. Lastly, in each factor, it correlated the level of importance with professionals' opinions as to what extent users have privacy concerns. The reason that it did not select the traditional method questionnaire at that time is that context-aware personalized service considered the absolute lack in understanding and experience of users with new technology. For understanding users' privacy concerns, professionals in the Delphi questionnaire process selected context data collection, tracking and recording, and sensory network as the most important factors among technological characteristics of context-aware personalized services. In the creation of a context-aware personalized services, this study demonstrates the importance and relevance of determining an optimal methodology, and which technologies and in what sequence are needed, to acquire what types of users' context information. Most studies focus on which services and systems should be provided and developed by utilizing context information on the supposition, along with the development of context-aware technology. However, the results in this study show that, in terms of users' privacy, it is necessary to pay greater attention to the activities that acquire context information. To inspect the results in the evaluation of sub factor, additional studies would be necessary for approaches on reducing users' privacy concerns toward technological characteristics such as highly identifiable level of identical data, diverse types of context data collected, tracking and recording functionality, embedded and disappearing sensor devices. The factor ranked the next highest level of importance after input is a context-aware service delivery that is related to output. The results show that delivery and display showing services to users in a context-aware personalized services toward the anywhere-anytime-any device concept have been regarded as even more important than in previous computing environment. Considering the concern factors to develop context aware personalized services will help to increase service success rate and hopefully user acceptance for those services. Our future work will be to adopt these factors for qualifying context aware service development projects such as u-city development projects in terms of service quality and hence user acceptance.

• Journal of Animal Environmental Science
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• v.15 no.1
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• pp.37-50
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• 2009

The purpose of this study was to investigate the effects of on-farm management systems(including the farm size, stocking density of growing-finishing phase, proportion of finisher diet and type of growing-finishing building) and pre-slaughter handling(including the transportation time and loading time) on carcass grade, the incidence of PSE pork, the meat quality score for intra-muscle fat, inter-muscle fat, subcutaneous fat and score for elasticity of market pigs. For this study, 248,787 pigs of 53 different farms were used to establish the pork quality assurance program and to meet the comsumer's need. The results are summarized as follows. 1. The farm size had significant influence on carcass grade, in which showing the higher grade by increasing the farm size. However, the incidence of PSE pork were not significantly differences among the farm size. The meat quality score for intra-muscle fat, inter-muscle fat, subcutaneous fat and score for elasticity of market pigs were not significant influenced by farm size. 2. The stocking density of growing-finishing phase was statistically significant for carcass grade, in which showing the higher grade in mid density group. However, the incidence of PSE pork was higher in high density group. The meat quality score for intra-muscle fat, inter-muscle fat and elasticity of market pigs were greater in mid density groups, but not influenced by stocking density for subcutaneous fat score. 3. The carcass grade and the incidence of PSE pork were not significantly influenced by transportation time. However, transportation time significantly affected the meat quality score, the meat quality score for intra-muscle fat, inter-muscle fat, subcutaneous fat and elasticity of the carcasses were superior in more than 1 hour transportation groups. 4. At any time loading, the carcass grade and PSE incidence were not significantly differences. However, the meat quality score for intra-muscle fat, inter-muscle fat and elasticity of market pigs were superior in before 10 AM groups, but not influenced by loading time for subcutaneous fat score. 5. The proportion of finisher diet had not significant influence on carcass grade, but PSE incidence affected by proportion of finisher diet, 21 percent or more group was higher PSE incidence. The meat quality score for intra-muscle fat and inter-muscle fat of the carcasses were superior in 21 percent or more fed finisher diet groups, but elasticity score of carcass had not influenced by the proportion of finisher diet. 6. The type of growing-finishing building was affected the carcass grade and PSE incidence of the market pigs, carcass grade and PSE incidence were superior in enclosed mechanical ventilation building groups. The meat quality score for intra-muscle fat and inter-muscle fat of the carcasses were not significantly differences by the type of finishing building, but the score of subcutaneous fat and elasticity of carcass were superior in opened natural ventilation building groups. In conclusion, the results of this study suggest that we have more precise on-farm management practice and the knowledge related to pre-slaughter handling skills to reduce the stress and improve the status of welfare of market pigs.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.3
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• pp.253-262
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• 1995

To develop a device for measuring fish freshness which could be move accurate and reliable than used freshness measuring systems. A new device based on digital circuit was designed using a microcomputer. The device was composed of a sensor part, 8096 microprocessor and a segment display. The effectiveness of device has been evaluated by the coefficient of correlation among the measured freshness stores such as electrical Q-value, K-value and amount of volatile basic nitrogen (VBN) of plaice, Paralichthys Olivaceus, during storage at $-3^{\circ}C,\;0^{\circ}C,\;5^{\circ}C,\;10^{\circ}C,\;and\;25^{\circ}C$. Q-values measured by a new device were more closely correlated with K-value (r=-0.978-\;-0.962,\;p<0.05) and VBN (r=-0.888-\;-0.988,\;p<0.05) in case of plaice meat. If more data would achieve using various fishes, this new designed device could be a valuable kit in fish market by its compact portability.

• Radiation Oncology Journal
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• v.25 no.2
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• pp.79-92
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• 2007

[ $\underline{Purpose}$ ]: For the first time, a nationwide survey in the Republic of Korea was conducted to determine the basic parameters for the treatment of esophageal cancer and to offer a solid cooperative system for the Korean Pattern of Care Study database. $\underline{Materials\;and\;Methods}$: During $1998{\sim}1999$, biopsy-confirmed 246 esophageal cancer patients that received radiotherapy were enrolled from 23 different institutions in South Korea. Random sampling was based on power allocation method. Patient parameters and specific information regarding tumor characteristics and treatment methods were collected and registered through the web based PCS system. The data was analyzed by the use of the Chi-squared test. $\underline{Results}$: The median age of the collected patients was 62 years. The male to female ratio was about 91 to 9 with an absolute male predominance. The performance status ranged from ECOG 0 to 1 in 82.5% of the patients. Diagnostic procedures included an esophagogram (228 patients, 92.7%), endoscopy (226 patients, 91.9%), and a chest CT scan (238 patients, 96.7%). Squamous cell carcinoma was diagnosed in 96.3% of the patients; mid-thoracic esophageal cancer was most prevalent (110 patients, 44.7%) and 135 patients presented with clinical stage III disease. Fifty seven patients received radiotherapy alone and 37 patients received surgery with adjuvant postoperative radiotherapy. Half of the patients (123 patients) received chemotherapy together with RT and 70 patients (56.9%) received it as concurrent chemoradiotherapy. The most frequently used chemotherapeutic agent was a combination of cisplatin and 5-FU. Most patients received radiotherapy either with 6 MV (116 patients, 47.2%) or with 10 MV photons (87 patients, 35.4%). Radiotherapy was delivered through a conventional AP-PA field for 206 patients (83.7%) without using a CT plan and the median delivered dose was 3,600 cGy. The median total dose of postoperative radiotherapy was 5,040 cGy while for the non-operative patients the median total dose was 5,970 cGy. Thirty-four patients received intraluminal brachytherapy with high dose rate Iridium-192. Brachytherapy was delivered with a median dose of 300 cGy in each fraction and was typically delivered $3{\sim}4\;times$. The most frequently encountered complication during the radiotherapy treatment was esophagitis in 155 patients (63.0%). $\underline{Conclusion}$: For the evaluation and treatment of esophageal cancer patients at radiation facilities in Korea, this study will provide guidelines and benchmark data for the solid cooperative systems of the Korean PCS. Although some differences were noted between institutions, there was no major difference in the treatment modalities and RT techniques.