• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1061-1065
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• 2004

The knowledge-based society of the 21st century requires effective education and learning methods in each professional field because the development of human resource determines its competence more than any other factors. It is highly desirable to develop an intelligent tutoring system, which meets ever increasing demands of education and learning. Such a system should be adaptive to each individual learner's demands as well as the continuously changing state of the learning process, thus enabling the effective education. The development of a learning environment based on learner modeling is necessary in order to be adaptive to individual learning variants. An intelligent learning environment is being developed targeting the heritage education, which is able to provide a customized and refined learning guide by storing the content of interactions between the system and the learner, analyzing the correlations in learning situations, and inferring the learning preference from the learner's learning history. This paper proposes a heritage learning system of Bulguksa temple, integrating the ontology-based learner modeling and the learning preference which considers perception styles, input and processing methods, and understanding process of information.

• ALGAE
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• v.27 no.4
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• pp.259-268
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• 2012

The genus Pterosiphonia is composed of 22 species worldwide and four of these species have been reported in the North-East Asia. In Korea, P. pennata originally described from the Mediterranean Sea has been previously recorded from the southern coast as a widespread species. In order to confirm the same species from Korea and Mediterranean, we observed the morphology of Korean Pterosiphonia specimen and analyzed rbcL sequences for inferring phylogenetic relationships among similar congeners. Korean entity was recognized as a new species, Pterosiphonia arenosa sp. nov. The new species is characterized by ecorticate axes with 7-10 pericentral cells, branches formed every two segments, and coalesced with main axis over 1-1.25 axial segments, and tetrasporangia formed in straight series on determinate branches of the upper parts of erect axes. A phylogenetic analysis of rbcL sequences demonstrated that P. arenosa was distinct from P. pennata found in Spain as well as other species. In conclusion, morphological and molecular sequence data indicated that P. arenosa sp. nov. has been previously misidentified as P. pennata in Korea.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.5
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• pp.594-604
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• 2017

The Korean National Institute of Fisheries Science (NIFS) has biannually (spring and fall, respectively) deployed a bottom trawl survey along the coastal areas for last decade, taking samples on a regular basis (i.e., a systematic sampling). Despite the availability of the survey data, NIFS has not yet officially reported the estimates of the groundfish population sizes as well as has not evaluated uncertainty of the estimates. The objectives of our study were to infer the relative size of a fish population, applying two different sampling techniques (namely simple and stratified sampling) with different observation units to the NIFS survey data, and to compare those two techniques in bias and precision. For demonstration purposes, we used data on Pacific cod (Gadus macrocephalus) collected by the 2011-2015 surveys, and the results of simple and stratified sampling showed that the point estimates and precision varied by observation unit as well as the sampling technique.

• Journal of Korean Library and Information Science Society
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• v.16
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• pp.129-157
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• 1989

Through this study, some considerations to be taken into account in order to construct the user model for the user friendly system which can provide each individuals user armed with varied intellectual level with the relevant information, can be summarized as follows : (1) The user' ability to use the system and users' subject knowledge, the distribution of the users' level knowledge should be considered for the decision of the typed of interaction between the users and the system. (2) the knowledge of the user models should include the following kinds of knowledge inharmony with one another, 1. Standard user knowledge which represents a general characteristic of user group, 2. individual user knowledge which represents an individual's unique characteristic, 3. Long-term user knowledge which represents the education level and subject background of users, 4. short-term user knowledge which represents the purpose of information science and information need by users (3) As knowledge generation technique, both the implicit method and explicit method should be a n.0, pplied, observation of the system during the interaction, and explicit method generates the knowledge by the user's answering the questions already made by the system. (4) The frame technique as the knowledge representation for the user-modelling in which user-knowledge is represented in a limited situation and in a qualitative aspects, can be recommended. The frame is adequated for the explanation of structured situation, and for the processing the present situation by inferring the previous experiences.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.776-780
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• 2009

As smartphones appear as suitable devices to implement ubiquitous computing recently, there are many researchers who study about personalized Intelligent services in smartphones. An intelligent synthetic character is one of them. This paper proposes a method generating behaviors of an intelligent synthetic character. In order to generate more natural behaviors for the character, the Bayesian networks are exploited to infer the user's states and OCC model is utilized to create the character's emotion. After inferring the contexts, the behaviors are generated through the behavior selection networks with using the information. A usability test verifies the usefulness of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.571-573
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• 2018

The loss of data collected from the sensor node in the wireless sensor network environment is caused by the hidden node of the sensor node and power shortage. In order to solve these problems, researches have been actively carried out to maintain the network effectively, but there is no study on the situation where the maintenance of the network is impossible. Therefore, research is needed to infer lost data in situations where network maintenance is impossible. In this paper, use particulate matter data of specific cities to deduce lost data. Analyze the accumulated data through machine learning and identify the possibility of inferring lost data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1329-1332
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• 1987

This paper describes a design of the expert system for diagnosis of abnormal gait patients. This system makes the rule-based representation that can easily extend the knowledge-base and naturally represent the uncertainty, and the inference engine that uses forward chaining which covers the reasoning from the first condition to the goal. The results of inferring various maladies using this system are as follows: 1) In cases of progressive muscular dystrophy, cerebral vascular accident, peripheral neuropathic lesion and peroneal nerve injury, the result of inference is the same as that of medical specialists' with 100% accuracy. 2) In cases of Neuritis, Paralysis agitan and Brain tumor, the accuracy of inference is less than 50% compared to that of medical specialists. With above results, we decide that the rule-based representations of some maladies ard accurate relatively, but that the correction and the extention of some rules and some methods of problem solving are required in order to construct the complete expert system for diagnosis of abnormal gait patients.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.731-740
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• 1991

This paper presents a knowledge based system to solve reactive power/voltage control problem in a power system. The methods to reduce inference time are proposed in inferring the solution of problem in the knowledge base which consists of heuristic rules and inowledge of experts. A set of indices drawn from the heuristic knowledge on the power system is utilized to make up for the defect of existing knowledge based systems which determine both the location and the amount of reactive power compensation devices. The concept of set of indices developed in this paper makes it possible to infer the amount of reactive power source only since the bus order list representing priority for the location of reactive power compensator to be switched on can be determined in advance. From the fact that there exists a relationship between the system voltage pattern and the reactive power pattern in operation, the pattern recognition technique is introduced to reduce the inference time in solving the severe voltage problem. To demonstrate the usefulness of the proposed knowledge based system, the IEEE 30 bus system is chosen as a sample system. The results of case study are also presented.

• Journal of KIISE
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• v.44 no.12
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• pp.1333-1337
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• 2017

Recommender systems (RS) that predict a set of items a target user is likely to prefer have been extensively studied in academia and have been aggressively implemented by many companies such as Google, Netflix, eBay, and Amazon. Data imputation alleviates the data sparsity problem occurring in recommender systems by inferring missing ratings and adding them to the original data. In this paper, we point out the drawbacks of existing approaches and make suggestions for data imputation techniques. We also justify our suggestions through extensive experiments.