• Proceedings of the Korea Society of Poultry Science Conference
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• 2003.11a
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• pp.103-104
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• 2003

This experiment was conducted to investigate the effect of enhancement of protein, limit amino acid, Ca and P on egg Production and egg qualify in laying hens. 720 twenty five week of age (WOA) brown laying hen divided to two diet to seventy WOA. Conventional diet(control) formulated by NRC recommendation, 2,900 ME kcal/kg, 16.0 ％ crude protein, 0.768 ％ lysine, 0.332 ％ methionine, 3.5 ％ Ca and 0.275 available P. Enhanced diet(ED) formulated by increasing about ten percentage except ME and available P : 2,900 ME kcal/kg, 17.7 ％ crude protein, 0.845 ％ lysine, 0.368 ％ methionine, 3.99 ％ Ca and 0.275 available P. Overall egg Production were not difference by diets though the hens fed control diet tended to higher egg Production to sixty WOA. Average egg weight was heavier in the ED than control in all period(P＜0.05). Daily egg mass increased slightly in the ED, but there were not significantly difference. Average feed intake increased about 3g in the control compared to the ED. Feed conversion ratio significantly improved in the ED(P＜0.05). Egg shell breaking strength was stronger in ED by around sixty WOA and showed similar tendency after that. Egg shell thickness was certainly improved in ED. Haugh unit. egg shell color and egg yolk color were not difference by diets.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.69-80
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• 2007

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques with better performance.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.65-78
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• 2007

This paper proposes a new method of weighted template matching for machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. Template matching is vulnerable to random noises that generate ragged outlines of a pattern when it is binarized. This paper offers a method of chain code trimming in order to remove ragged outlines. The method corrects specific chain codes within the chain codes of the inner and the outer contour of a pattern. The experiment compares confusion matrices of both the template matching and the proposed weighted template matching with chain code trimming. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of IKEEE
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• v.23 no.2
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• pp.675-680
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• 2019

The coal-fired power plant is operated by a combined operation method, which is operated by sliding pressure operation under low load and by fixed pressure operation under high load for improved efficiency. The combined operation is divided into two and three valve open modes. Each plant is operated by selecting the turbine control valve mode in accordance with the manufacturer's recommendation, but is not really operating at the optimal sliding pressure operation according to load range, also Load range of each plant is configured differently. The internal efficiency of the high-pressure turbines is reduced due to loss of the turbine valves and the plant efficiency is reduced. To solve these problems, In this paper, the optimum load range is selected through the analysis method of thermal performance by each load in order to improve the optimum variable pressure operation load range by turbine control valve mode.

• Quality Improvement in Health Care
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• v.24 no.2
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• pp.41-61
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• 2018

Purpose: Falls are one of the most frequent health events in medical institutions, however, they can be predicted and prevented. The Quality Improvement Nurse Society clinical practice guideline Steering Committee developed the Clinical Practice Guideline for the assessment and prevention of falls in adult people. The purpose of this study was to assess the risk factors for falls in adults aged 19 years and older, to present an evidence for preventing falls, formulate a recommendations, and indicators for applying the recommendations. Methods: This clinical practice guideline was developed using a 23-step adaptation method according to the Handbook for clinical practice guideline developer (version 1.0) by National Evidence-based Healthcare Collaborating Agency. Evidence levels and recommendation ratings were established in accordance to SIGN 2011 (The Scottish Intercollegiate Guidelines Network). Results: The final 15 recommendations from four domains were derived from experts' advice; 1) assessment of risk factor for falls in adult 2) preventing falls and reducing the risks of falls or falls-related injury 3) management and reassessment after a person falls 4) leadership and culture. Conclusion: This clinical practice guideline can be used as a basis for evaluation and prevention of fall risk factors for adults, to formulate recommendations for fall risk assessment and fall prevention, and to present monitoring indicators for applying the recommendations.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.390-405
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• 2021

Gait analysis trackers have been developed to monitor daily gait patterns to improve users' running performance and reduce the risk of injuries. A variety of gait analysis trackers are available on the market(e.g., foot pods, insoles). Depending on the type of gait analysis tracker, users' discomfort or satisfaction as well as required properties may differ. Hence, the purpose of this study was to compare and analyze user experience of three different types of commercial shoe-mounted gait analysis trackers and their mobile applications in a laboratory environment using questionnaires based on actual experiences of each product. Ten males and ten females who regularly enjoy walking and running exercises participated in the experiment. After the participants set up the tracker and application themselves without support from researchers, ten to thirty minutes' exercise was permitted on each product. Following this, the participants answered questionnaires containing evaluation variables on the device and mobile application, as well as satisfaction, intention to use, recommendation, and purchase. In addition, they were asked questions about the attractive features and shortcomings of each device and application. The results showed that the PRO-SPECS^® smart insole was preferred over the others for ease of use, perceived durability, psychological burden of the design, and usefulness of the information provided by the application. Along with the results of questionnaire, this study also discussed strategies and recommendations for future product design and development.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.9-16
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• 2023

In the IoT environment, various objects mutually interactive, and various services can be composed based on this environment. In the previous study, we have developed a resource collaboration system to provide services by substituting limited resources in the user's personal device using resource collaboration. However, in the preceding system, when the number of resources and situations increases, the inference time increases exponentially. To solve this problem, this study proposes a method of classifying users and resources by applying the BIG5 user type classification model. In this paper, we propose a method to reduce the inference time by filtering the user's preferred resources through BIG5 type-based preprocessing and using the filtered resources as an input to the recommendation system. We implement the proposed method as a prototype system and show the validation of our approach through performance and user satisfaction evaluation.