• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.427-434
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• 2000

사례기반 추론은 사후학습기법이기 때문에, 사례베이스의 크기가 커지면 추론의 수행시간이 증가하여 전체적인 성능을 저하시킨다. 본 연구에서는 이러한 단점을 극복하기 위하여 사례기반 시스템의 구현에 앞서 사례들이 저장되어 있는 사례베이스를 클러스터링 하였다. 클러스터링에 사용한 기법은 부분적 매칭에 의한 유사도를 기준으로 클러스터링을 하는 사례기반 클러스터링 기법이다. 도출된 클러스터 각각에 대해 가장 적합한 사례기반 시스템을 구축하여 고장진단의 분야에 적용하였다. 즉, 새로운 고장 사례가 입력되었을 때에 본 연구에서 구축된 시스템에서는 먼저 해당 클러스터를 판별한 후 그 클러스터에 적합한 사례기반 시스템으로 고장진단을 하게 되는 것이다. 그 결과, 하나의 사례기반 시스템을 구축하였을 때보다 수행시간이 감소하였으며, 적중률도 향상되었다.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1039-1041
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• 1998

This paper presents a practical fuzzy expert system to diagnose various faults occurred in local power systems. This integrated system can diagnose all faults occurred in a transmission network and substations. In this paper. the fuzzy reasoning of the diagnostic process is discussed in detail. The discrimination of false operations and non-operations of protective devices as well as the fault identification scheme are also analyzed together with the fuzzy inference process.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.147-153
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• 1997

Fault diagnosis technique in machining system which is one of engineering techniques absolutely necessary to automation of manufacturing system has been proposed. As a whole, diagnosis process is explained by two steps: sensor data acquisition and reasoning current state of system with the given sensor data. Flexible disk grinding process implemented in milling machine was employed in order to obtain empirical manufacturing process information. Resistance force data during machining were acquired using tool dynamometer known as sensor which is comparably accurate and reliable in operation. Tool status during the process was analyzed using influnece diagram assigning probability from the statistical analysis procedure.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.141-154
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• 2014

The adoption of International Financial Reporting Standards (IFRS) is the one of important issues in the recent accounting research because the change from local GAAP (Generally Accepted Accounting Principles) to IFRS has a substantial effect on accounting information. Over 100 countries including Australia, China, Canada and the European Union member countries adopt IFRS (International Financial Reporting Standards) for financial reporting purposes, and several more including the United States and Japan are considering the adoption of IFRS (International Financial Reporting Standards). In Korea, 61 firms voluntarily adopted Korean International Financial Reporting Standard (K-IFRS) in 2009 and 2010 and all listed firms mandatorily adopted K-IFRS (Korea-International Financial Reporting Standards) in 2011. The adoption of IFRS is expected to increase financial statement comparability, improve corporate transparency, increase the quality of financial reporting, and hence, provide benefits to investors This study investigates whether recognized accounts receivable discounting (AR discounting) under Korean International Financial Reporting Standard (K-IFRS) is more value relevant than disclosed AR discounting under Korean Generally Accepted Accounting Principles (K-GAAP). Because more rigorous standards are applied to the derecognition of AR discounting under K-IFRS(Korea-International Financial Reporting Standards), most AR discounting is recognized as a short term debt instead of being disclosed as a contingent liability unless all risks and rewards are transferred. In this research, I try to figure out industrial responses to the changes in accounting rules for the treatment of accounts receivable toward more strict standards in the recognition of sales which occurs with the adoption of Korea International Financial Reporting Standard. This study examines whether accounting information is more value-relevant, especially information on accounts receivable discounting (hereinafter, AR discounting) is value-relevant under K-IFRS (Korea-International Financial Reporting Standards). First, note that AR discounting involves the transfer of financial assets. Under Korean Generally Accepted Accounting Principles (K-GAAP), when firms discount AR to banks before the AR maturity, firms conventionally remove AR from the balance-sheet and report losses from AR discounting and disclose and explain the transactions in the footnotes. Under K-IFRS (Korea-International Financial Reporting Standards), however, most firms keep AR and add a short-term debt as same as discounted AR. This process increases the firms' leverage ratio and raises the concern to the firms about investors' reactions to worsening capital structures. Investors may experience the change in perceived risk of the firm. In the study sample, the average of AR discounting is 75.3 billion won (maximum 3.6 trillion won and minimum 18 million won), which is, on average 7.0% of assets (maximum 38.6% and minimum 0.002%), 26.2% of firms' accounts receivable (maximum 92.5% and minimum 0.003%) and 13.5% of total liabilities (maximum 69.5% and minimum 0.004%). After the adoption of K-IFRS (Korea-International Financial Reporting Standards), total liabilities increase by 13%p on average (maximum 103%p and minimum 0.004%p) attributable to AR discounting. The leverage ratio (total liabilities/total assets) increases by an average 2.4%p (maximum 16%p and minimum 0.001%p) and debt-to-equity ratio increases by average 14.6%p (maximum 134%p and minimum 0.006%) attributable to the recognition of AR discounting as a short-term debt. The structure of debts and equities of the companies engaging in factoring transactions are likely to be affected in the changes of accounting rule. I suggest that the changes in accounting provisions subsequent to Korea International Financial Reporting Standard adoption caused significant influence on the structure of firm's asset and liabilities. Due to this changes, the treatment of account receivable discounting have become critical. This paper proposes an intelligent diagnostic system for estimating negative impact on stock value with self-organizing maps and case based reasoning. To validate the usefulness of this proposed model, real data was analyzed. In order to get the significance of this proposed model, several models were compared to the research model. I found out that this proposed model provides satisfactory results with compared models.

• Industry Promotion Research
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• v.6 no.4
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• pp.31-38
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• 2021

This study analyzed the non-cognitive domains (self-management competency, interpersonal relations competency) and cognitive domains (physical communication competency, comprehensive reasoning ability) among K-CESA for college students in the Division of Architecture at 𐩒𐩒 University and construction company workers, after which a training program for college students was designed. A K-CESA diagnostic evaluation was conducted on 25 construction company workers and 36 students in the senior and junior years of the division of Architecture. To identify the discrepancies among the two groups, "One-way ANOVA", a mean difference test, was performed and the Scheffe verification system was conducted as an after-measure. The empirical analysis of this study was verified at the significance level p <.05, and statistical processing was analyzed utilizing the SPSS WIN. 23.0 program. The major findings are as follows: first, the significant point of difference between the college students and construction company workers were located in five skills (goal-oriented planning and execution skills, cooperative skills, intervention skills, leadership skills, speaking skills, analytical reasoning skills); second, the education program was developed to improve the goal-oriented planning, execution ability and analytical reasoning ability through the expert-required analysis and study research. Through follow-up studies, I suggested that there is a need to develop courses that compare the competencies of various majors and workers in public institutions, corporations and other organizations.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.18 no.4
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• pp.701-705
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• 1996

There are many kind of diagnostic entities in submandibular or neck masses, and we can set up treatment plan and estimate treatment result, prognosis by accurate diagnosis. By reasoning medical and dental history, physical examination, anatomical consideration of masses in submandibular or neck area, location of masses, laboratory and radiographic studies, we can formulate a clinical diagnosis or differential diagnosis. Although a clinical diagnosis might suffice in some instances, a definitive(microscopic) diagnosis is frequently required for proper treatment. In order to get some information about making accurate diagnosis and setting up appropriate treatment plan, we did clinical study and histopathologic classification of 82 patients who visited and were operated for submandibular masses at Department of Oral and Maxillofacial Surgery in Seoul National University Hospital from 1988 to 1992. The result were as follows : 1. Submandibular masses occured most frequently in forties and fifties, and there was no sex predilection. 2. Chief complaints were in order of mass, swelling, pain and consistency were soft mass, mobile hard mass, firm mass, diffuse swelling in descending order. 3. Most frequent pathologic finding was lymphadenitis. 4. Site of submandibular masses were submandible, neck, submental, retromandible in descending order, and there was no predilection between left and right side. 5. Accuracy rate between clinical impression and result was 51.2%.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.16 no.2
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• pp.1-11
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• 2008

Increasing demand for improved reliability and survivability of mission-critical systems is driving the development of health monitoring and Automated Contingency Management (ACM) systems. An ACM system is expected to adapt autonomously to fault conditions with the goal of still achieving mission objectives by allowing some degradation in system performance within permissible limits. ACM performance depends on supporting technologies like sensors and anomaly detection, diagnostic/prognostic and reasoning algorithms. This paper presents the development of a generic prototype test bench software framework for developing and validating ACM systems for advanced propulsion systems called the Propulsion ACM (PACM) Test Bench. The architecture has been implemented for a Monopropellant Propulsion System (MPS) to demonstrate the validity of the approach. A Simulink model of the MPS has been developed along with a fault injection module. It has been shown that the ACM system is capable of mitigating the failures by searching for an optimal strategy. Furthermore, the concepts of Validation and Verification (V&V) of such systems are introduced with relevant examples.

• Computers and Concrete
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• v.2 no.6
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• pp.439-454
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• 2005

When deterioration of concrete is observed in a structure, it is highly desirable to determine the cause of such deterioration. Only by understanding the cause can an appropriate repair strategy be implemented to address both the cause and the symptom. In colder climates, bridge deck deterioration is often caused by chlorides from de-icing salts, which penetrate the concrete and depassivate the embedded reinforcement, causing corrosion. Bridge decks can also suffer from other deterioration mechanisms, such as alkali-silica reaction, freeze-thaw, and shrinkage. There is a need for a comprehensive and integrative system to help with the inspection and evaluation of concrete bridge deck deterioration before decisions are made on the best way to repair it. The purpose of this research was to develop a model to help with the diagnosis of concrete bridge deck deterioration that integrates the symptoms observed during an inspection, various deterioration mechanisms, and the probability of their occurrence given the available data. The model displays the diagnosis result as the probability that one of four deterioration mechanisms, namely shrinkage, corrosion of reinforcement, freeze-thaw and alkali-silica reaction, is at fault. Sensitivity analysis was performed to determine which probabilities in the model require refinement. Two case studies are included in this investigation.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.21-30
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• 2021

With the rapid development of artificial intelligence and big data, a lot of medical data is effectively used, and the diagnosis and analysis of diseases has entered the era of intelligence. With the increasing public health awareness, ordinary citizens have also put forward new demands for panic disorder health services. Specifically, people hope to predict the risk of panic disorder as soon as possible and grasp their own condition without leaving home. Against this backdrop, the smart health industry comes into being. In the Internet age, a lot of panic disorder health data has been accumulated, such as diagnostic records, medical record information and electronic files. At the same time, various health monitoring devices emerge one after another, enabling the collection and storage of personal daily health information at any time. How to use the above data to provide people with convenient panic disorder self-assessment services and reduce the incidence of panic disorder in China has become an urgent problem to be solved. In order to solve this problem, this research applies the context awareness to the automatic diagnosis of human diseases. While helping patients find diseases early and get treatment timely, it can effectively assist doctors in making correct diagnosis of diseases and reduce the probability of misdiagnosis and missed diagnosis.