• Korean Journal of Acupuncture
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• v.41 no.1
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• pp.27-32
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• 2024

Objectives : This study developed an AI-based patient chatbot and examined the usability and educational effectiveness of the chatbot in the context of Korean medicine education. Methods : The patient chatbot was developed using the AI chatbot builder 'Danbee', and a total of five experts were surveyed and interviewed to determine the usability, effectiveness, advantages, disadvantages, and improvement points of the chatbot. Results : The patient chatbot was found to have high usability and educational effectiveness. The advantages of the patient chatbot were 1) it provided students with practical experience in performing clinical skills, 2) it provided instructors with assessment materials while reducing their teaching burden, and 3) it could be effectively used for horizontal and vertical integration education. The disadvantages and improvements of the patient chatbot were 1) improving the accuracy of intention inference, 2) providing students with specific instructions for problem-solving activities, and 3) providing assessment results and feedback about students' activities. Conclusions : This study is significant in that it proposes a new training method to overcome the limitations of the existing doctor-patient simulation. It is hoped that this study will stimulate further research on the improvement of students' clinical skills using artificial intelligence.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.75-102
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• 2006

Three experiments were conducted using a verification task to examine good and poor readers' generation of causal inferences(with because sentences) and contrastive inferences(with although sentences). The unfamiliar, critical verification statement was either explicitly mentioned or was implied. In Experiment 1, both good and poor readers responded accurately to the critical statement, suggesting that both groups had the linguistic knowledge necessary to the required inferences. Differences were found, however, in the groups' verification latencies. Poor, but not good, readers responded faster to explicit than to implicit verification statements for both because and although sentences. In Experiment 2, poor readers were induced to generate causal inferences for the because experimental sentences by including fillers that were apparently counterfactual unless a causal inference was made. In Experiment 3, poor readers were induced to generate contrastive inferences for the although sentences by including fillers that could only be resolved by making a contrastive inference. Verification latencies for the critical statements showed that poor readers made causal inferences in Experiment 2 and contrastive inferences in Experiment 3 doting comprehension. These results were discussed in terms of context effect: Specific encoding operations performed on anomaly backgrounded in another passage would form part of the context that guides the ongoing activity in processing potentially relevant subsequent text.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.177-212
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• 1993

My research is based on the parser for ill-formed input by Mellish in a paper in ACL 27th meeting Proceedings. 1989. My system is composed of two parsers:WFCP and IFCP. When WFCP fails to give the parse tree for the input sentence, the sentence is identified as ill-formed and is parsed by IFCP for error detection and recovery at the syntactic level. My system is indendent of grammatical rules. It does not take into account semantic ill-formedness. My system uses a grammar composed of 25 context-free rules. My system consistes of two major parsing strategies:top-down expection and bottem-up satisfaction. With top-down expectation. rules are retrieved under the inference condition and expaned by inactive arcs. When doing bottom-up parsing. my parser used two modes:Left-Right parsing and Right-to-Left parsing. My system repairs errors sucessfully when the input contains an omitted word or an unknown word substitued for a valid word. Left- corner and right-corner errors are more easily detected and repaired than ill-formed senteces where the error is in teh middle. The deviance note. with repair details, is kept in new inactive arcs which are generated by the error correction procedure. The implementation of my system is quite different from Mellish's. When rules are invoked. my system invokes all rules with minimal inference. My bottom up parsing strategy uses Left-to-Right mode and Right-to-Left mode. My system is bottom-up-parsing-oriented like the chart parser. Errors are repaired in two ways:using top-down hypothesis, and using Need-Chart which keeps the information of expectation and complection of expanded goals by rules. To reduce the number of top-down cycles. all rules are invoked simultaneously and this invocation information is kept in Need-Chart. This idea will be extended for the implementation of multiple error recovery system.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.177-183
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• 2002

Current state-of-the-art of VMS control is based upon simple knowledge-based inference engine with message set and each message's priority. And R&Ds of the VMS control are focused on the accurate detection and estimation of traffic condition of the subject roadways. However VMS display itself cannot achieve a desirable traffic allocation among alternative routes in the network In this context, VMS display strategy is the most crucial part in the VMS control. VMS itself has several limitations in its nature. It is generally known that VMS causes overreaction and concentration problems, which may be more serious in urban network than highway network because diversion should be more easily made in urban network. A feedback control algorithm is proposed in this paper to address the above-mentioned issues. It is generally true that feedback control approach requires low computational effort and is less sensitive to models inaccuracy and disturbance uncertainties. Major features of the proposed algorithm are as follows: Firstly, a regulator is designed to attain system optimal traffic allocation among alternative routes for each VMS in the network. Secondly, strategic messages should be prepared to realize the desirable traffic allocation, that is, output of the above regulator. VMS display strategy module is designed in this context. To evaluate Probable control benefit and to detect logical errors of the Proposed feedback algorithm, a offline simulation test is performed using real network in Daejon, Korea.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.173-177
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• 2009

With the advent of light-weight daily physiological signal monitoring sensors, intelligent inference and analysis method for physiological signal monitoring application, commercialized products and services are released. However, practical constraints still remain for daily physiological signal monitoring. Most devices provide rough health check function and analyze with randomly sampled measurements. In this work, we propose the probabilistic modeling of physiological signal analysis. This model represent the relationship between previous user measurement (history), other group`s type, model and current observation. From the experiment, we found that the personalized analysis with long term regular data shows reliable result and reduces the analyzing errors. In addition, participants agree that the personalized analysis shows reliable and adaptive information than other standard analysis method.

• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.81-92
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• 2000

Elliptical fragments are common in natural language dialogues between humans. Since most elliptical fragments should be interpeted within the context. it is not easy for computers to recognize the speaker's intention from the elliptical fragments. In t this paper we propose a model to recognize speaker's intention from elliptical fragments 1 in Korean by expanding the tripartite plan-based model proposed by Lambert. We add new discourse recipes to define user's discourse actions through elliptical fragments. In order to use plan inference process. we must represent utterances as actions. e. g .. r e elliptical fragments are represented as surface speech acts. In surface speech act representation. we include the information of 'Josa' (case markers in Korean), because t the information of 'Josa' plays a very important role in analysing speakers' intention in Korean. Finally. by using an object and discourse focus theory, the system can recognize the intention that a user is trying to compare between two plans by uttering elliptical fragments

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.63-88
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• 2018

This research evaluated differences of classification performance for feature selection methods using LDA topic model and Doc2Vec which is based on word embedding using deep learning, feature corpus sizes and classification algorithms. In addition to find the feature corpus with high performance of classification, an experiment was conducted using feature corpus was composed differently according to the location of the document and by adjusting the size of the feature corpus. Conclusionally, in the experiments using deep learning evaluate training frequency and specifically considered information for context inference. This study constructed biomedical document dataset, Disease-35083 which consisted biomedical scholarly documents provided by PMC and categorized by the disease category. Throughout the study this research verifies which type and size of feature corpus produces the highest performance and, also suggests some feature corpus which carry an extensibility to specific feature by displaying efficiency during the training time. Additionally, this research compares the differences between deep learning and existing method and suggests an appropriate method by classification environment.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6609-6612
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• 2014

Opisthorchis viverrini is an ongoing public health problem in Northeast Thailand. Despite continuous efforts for decades by healthcare organizations to overcome this problem, infection rates remain high. To enable related personnel to identify and address the various issues effectively, a cross-sectional study was performed to investigate prevalence and risk factors for opisthorchiasis. The target group was 3,916 Thai residents of Northeast Thailand who were 15 or over. Participants were recruited using the 30 clusters sampling technique. The data were gathered through questionnaires, focus group discussions, in-depth interviews, and stool examinations for parasite eggs (using the Modified Kato Katz method). The data were analyzed using descriptive and inference statistics; in order to ascertain the risk factors and test them using the odds ratio and multiple logistic regressions. The prevalence of opisthorchiasis was 22.7% (95%CI: 0.26 to 0.24). The province with the highest prevalence was Nakhorn Phanom (40.9%; female to male ratio =1:1.2). The age group with the highest prevalence was 40-49 year olds. All age groups had a prevalence >20%. Four of seven provinces had a prevalence >20%. The factors related to opisthorchiasis were (a) sex, (b) age (especially > 50), (c) proximity and duration living near a water body, and (d) eating raw and/or fermented fish. In order to reduce the prevalence of opisthorchiasis, the focus in populations living in upper Northeast Thailand should be changing their eating behaviors as appropriate to their tradition and context.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.