• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.453-463
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• 2013

In this paper, we present an effective method and a system for the music summarization which automatically extract the chorus portion of a piece of music. A music summary technology is very useful for browsing a song or generating a sample music for an online music service. To develop the solution, conventional automatic music summarization methods use a 2-dimensional similarity matrix, statistical models, or clustering techniques. But our proposed method extracts the music summary by calculating BER(Bit Error Rate) between audio fingerprint blocks which are extracted from a song. But we could directly use an enormous audio fingerprint database which was already saved for a music retrieval solution. This shows the possibility of developing a various of new algorithms and solutions using the audio fingerprint database. In addition, experiments show that the proposed method captures the chorus of a song more effectively than a conventional method.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.174-183
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• 2014

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.99-106
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• 2009

Over recent decades, concept mapping has been used as a valuable Learning and Teaching tool. A number of studies have shown a positive impact on student learning. One of the disadvantages of this technique has been that assessing them or providing feedback to students is time consuming. We aim here to introduce ways of reducing the complexity of using concept map techniques in online activities. Several types of scoring methods for the concept map based assessment have been developed. In this paper, we describe the development of an automatic assessment system that implements those techniques. We contribute a design that uses semantic web technologies for both the management and the scoring of the concept maps.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.662-671
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• 2020

It is crucial to manage and maintain the geodetic reference coordinates of GNSS continuously operating reference stations (CORSs) in consideration of their fundamental roles in geodetic control and positioning navigation infrastructure. Earthquake-induced crustal displacement directly impacts the reference coordinates, so such events should be promptly detected, and appropriate action should be made to maintain the target accuracy, including update of the geodetic coordinates. To this end, this paper deals with online schemes for the detection of persistent shifts in the coordinate time-series produced by an automatic GNSS processing system. Algorithms were implemented to test filtered results, such as hypothesis tests of the innovation sequence of a Kalman filter and a cumulative sum (CUSUM) test. The results were assessed by the time-series of coordinates of 14 CORS for two years, including the 2011 Tohoku earthquake. The results show that the global hypothesis test is practical for detecting abrupt jumps, whereas CUSUM is effective for identifying persistent shifts.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.253-289
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• 1994

The purpose of this study is about the search pattern of LINNET (Library Information Network System) OPAC users by transaction log, maintained by POSTECH(Pohang University of Science and Technology) Central Library, to provide feedback information of OPAC system design. The results of this study are as follows. First, for the period of this analysis, there were totally 11, 218 log-ins, 40, 627 transaction logs and 3.62 retrievals per a log-in. Title keyword was the most frequently used, but accession number, bibliographic control number or call number was very infrequently used. Second, 47.02% of OPAC, searches resulted in zero retrievals. Bibliographic control number was the least successful search. User displayed 2.01% full information and 64.27% local information per full information. Third, special or advanced retrieval features are very infrequently used. Only 22.67% of the searches used right truncation and 0.71% used the qualifier. Only 1 boolean operator was used in every 22 retrievals. The most frequently used operator is 'and (&)' with title keywords. But 'bibliographical control number (N) and accessionnumber (R) are not used at all with any operators. The causes of search failure are as follows. 1. The item was not used in the database. (15, 764 times : 79.42%). 2. The wrong search key was used. (3, 761 times : 18.95%) 3. The senseless string (garbage) was entered. (324 times : 1.63%) On the basis of these results, some recommendations are suggested to improve the search success rate as follows. First, a n.0, ppropriate user education and online help function let users retrieve LINNET OPAC more efficiently. Second, several corrections of retrieval software will decrease the search failure rate. Third, system offers right truncation by default to every search term. This methods will increase success rate but should considered carefully. By a n.0, pplying this method, the number of hit can be overnumbered, and system overhead can be occurred. Fourth, system offers special boolean operator by default to every keyword retrieval when user enters more than two words at a time. Fifth, system assists searchers to overcome the wrong typing of selecting key by automatic korean/english mode change.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.195-201
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• 2020

There have been many developments and innovations in the educational environments in line with the rapidly evolving information age. E-Learning is a representative example of this rapid evolution. However, E-Learning is challenging to maintain students' concentration because of the low engagement level and limited interactions between instructors and students. Additionally, instructors have limitations in identifying learners' concentration. This paper proposes a system that can measure E-learning users' concentration levels by detecting the users' eyelid movement and the top of the head. The system recognizes the eyelid and the top of the head and measures the learners' concentration level. Detection of the eyelid and the top of the head triggers an event to assess the learners' concentration level based on the users' response. After this process, the system provides a normalized concentration score to the instructor. Experiments with experimental groups and control groups were conducted to verify and validate the system, and the concentration score showed more than 90% accuracy.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.167-183
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• 2006

This paper describes a metadata extraction technique based on natural language processing (NLP) which extracts personalized information from email communications between financial analysts and their clients. Personalized means connecting users with content in a personally meaningful way to create, grow, and retain online relationships. Personalization often results in the creation of user profiles that store individuals' preferences regarding goods or services offered by various e-commerce merchants. We developed an automatic metadata extraction system designed to process textual data such as emails, discussion group postings, or chat group transcriptions. The focus of this paper is the recognition of emotional contents such as mood and urgency, which are embedded in the business communications, as metadata.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.910-912
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• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.