• 가정∙방문간호학회지
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• 제5권
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• pp.47-60
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• 1998

This study was done to identify basic information in classifying nursing diagnoses and nursing interventions needed for the further development of computerized nursing care plans. Data were collected by reviewing charts of 123 home care clients who had active disease, for whom at least one nursing diagnosis was on the chart, and who had been discharged. Data included demographics, medical orders, nursing diagnoses and nursing interventions. The results of the study, which found the most frequent medical diagnoses to be cancer (40.7%) and brain injury (26.8%), showed that 'Impaired Skin Integrity'(18.3%), 'Risk for Infection'(15.0%), 'Altered Nutrition, Less than Body Requirements'(13.8%), and 'Risk for Impaired Skin Integ rity'(9.9%) were the most frequent nursing diagnoses. 'Pressure Ulcer Care'(28.4%) was the most frequent intervention for 'Impaired Skin Integrity', 'Infection Protection'(16.0%) for 'Risk of Infection', 'Nutrition Counseling'(26.8%) for 'Altered Nutrition' and 'Positioning'(22.0%) for 'Risk for Skin Integrity Impairment', Comparison of interventions with the Nursing Intervention Classification(NIC) showed that the most frequent interventions were in the domain 'Basic Physiological' (33.94%), followed by 'Behavioral'(27.8%), and 'Complex Physiological' (22.6%). Interventions related to teaching family to give care at home could not be classified in the NIC scheme. Examination of the frequency of NIC interventions showed that for the domain 'Activity & Exercise Management', 75% of the interventions were used, but for seven domains, none were used. For the domain 'Immobility Management', 93% of the times that an intervention was used, it was 'Positioning', for the domain 'Tissue Perfusion Management', 'IV Therapy' (59.1%) and for the domain 'Elimination Management', 'Tube Care: Urinary'(54.0%). The nursing diagnoses 'Altered Urinary Elimination' and 'Im paired Physical Mobility' were both used with these clients, but neither 'Fluid Volume Deficit' nor 'Risk of Fluid Volume Deficit' were used rather 'IV Therapy' was an intervention for 'Altered Nutrition, Less than Body Requirements', A comparison of clients with cancer and those with brain injury showed that interventions for the nursing diagnosis 'Impaired Skin Integrity' were more frequent for the clients with cancer, interventions for 'Risk of Infection' were similar for the two groups but for clients with cancer there were more interventions for' Altered Nutrition'. Examination of the nursing diagnoses leading to the intervention 'Positioning' showed that for both groups, it was either 'Impaired Skin Integrity' or 'Risk for Skin Integrity Impairment'. This study identified a need for further refinement in the classification of nursing interventions to include those unique to home care and that for the purposes of computerization identification of the nursing activities to be included in each intervention needs to be done.

• Smart Structures and Systems
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• 제25권2호
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• pp.255-264
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• 2020

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.

• International Journal of Contents
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• 제13권2호
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• pp.66-74
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• 2017

Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain's experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.

• The Journal of the Acoustical Society of Korea
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• 제23권3E호
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• Bioinformatics and Biosystems
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• 제1권2호
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• pp.123-127
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• 2006

TIM barrel domain is widely studied since it is one of most common structure and mediates diverse function maintaining overall structure. TIM barrel domain's function is determined by local structural environment at the C-terminal end of barrel structure. We classified TIM barrel domains by local structural alignment tool, LSHEBA, to understand characteristics of TIM barrel domain's functionalvariation. TIM barrel domains classified as the same cluster share common structure, function and ligands. Over 80% of TIM barrels in clusters share exactly the same catalytic function. Comparing clustering result with that of SCOP, we found that it's important to know local structural environment of TIM barrel domains rather than overallstructure to understand specific structural detail of TIM barrel function. Non TIM barrel domains were associated to make different domain combination to form a different function. The relationship between domain combination, we suggested expected evolutional history. We finally analyzed the characteristics of amino acids around ligand interface.

• Journal of Information Processing Systems
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• 제7권4호
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• 지능정보연구
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• 제28권4호
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• pp.251-273
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• 2022

최근 텍스트 분석을 딥러닝에 적용한 연구가 꾸준히 이어지고 있으며, 특히 대용량의 데이터 셋을 학습한 사전학습 언어모델을 통해 단어의 의미를 파악하여 요약, 감정 분류 등의 태스크를 수행하려는 연구가 활발히 이루어지고 있다. 하지만 기존 사전학습 언어모델이 특정 도메인을 잘 이해하지 못한다는 한계를 나타냄에 따라, 최근 특정 도메인에 특화된 언어모델을 만들고자 하는 방향으로 연구의 흐름이 옮겨가고 있는 추세이다. 도메인 특화 추가 사전학습 언어모델은 특정 도메인의 지식을 모델이 더 잘 이해할 수 있게 하여, 해당 분야의 다양한 태스크에서 성능 향상을 가져왔다. 하지만 도메인 특화 추가 사전학습은 해당 도메인의 말뭉치 데이터를 확보하기 위해 많은 비용이 소요될 뿐 아니라, 고성능 컴퓨팅 자원과 개발 인력 등의 측면에서도 많은 비용과 시간이 투입되어야 한다는 부담이 있다. 아울러 일부 도메인에서 추가 사전학습 후의 성능 개선이 미미하다는 사례가 보고됨에 따라, 성능 개선 여부가 확실하지 않은 상태에서 도메인 특화 추가 사전학습 모델의 개발에 막대한 비용을 투입해야 하는지 여부에 대해 판단이 어려운 상황이다. 이러한 상황에도 불구하고 최근 각 도메인의 성능 개선 자체에 초점을 둔 추가 사전학습 연구는 다양한 분야에서 수행되고 있지만, 추가 사전학습을 통한 성능 개선에 영향을 미치는 도메인의 특성을 규명하기 위한 연구는 거의 이루어지지 않고 있다. 본 논문에서는 이러한 한계를 극복하기 위해, 실제로 추가 사전학습을 수행하기 전에 추가 사전학습을 통한 해당 도메인의 성능 개선 정도를 선제적으로 확인할 수 있는 방안을 제시한다. 구체적으로 3개의 도메인을 분석 대상 도메인으로 선정한 후, 각 도메인에서의 추가 사전학습을 통한 분류 정확도 상승 폭을 측정한다. 또한 각 도메인에서 사용된 주요 단어들의 정규화된 빈도를 기반으로 해당 도메인의 특수성을 측정하는 지표를 새롭게 개발하여 제시한다. 사전학습 언어모델과 3개 도메인의 도메인 특화 사전학습 언어모델을 사용한 분류 태스크 실험을 통해, 도메인 특수성 지표가 높을수록 추가 사전학습을 통한 성능 개선 폭이 높음을 확인하였다.

• 한국국방경영분석학회지
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• 제23권2호
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• pp.45-71
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• 1997

There has been considerable research recently on uncertainty handling in the fields of artificial intelligence and knowledge-based system. Various numerical and non-numerical methods have been proposed for representing and propagating uncertainty in knowledge-based system. The Bayesian method, the Dempster-Shafer's Evidence Theory, the Certainty Factor model and the Fuzzy Set Theory are most frequently appeared in the knowledge-based system. Each of these four methods views uncertainty from a different perspective and propagates it differently. There is no single method which can handle uncertainty properly in all kinds of knowledge-based systems' domain. Therefore a knowledge-based system will work more effectively when the uncertainty handling method in the system fits to the system's environment. This paper proposed a framework for selecting proper uncertainty handling methods in knowledge-based system with respect to characteristics of problem domain and cognitive styles of experts. A schema with strategic/operational and unstructured/structured classification is employed to differenciate domain. And a schema with systematic/intuitive and preceptive/receptive classification is employed to differenciate experts' cognitive style. The characteristics of uncertainty handling methods are compared with characteristics of problem domains and cognitive styles respectively. Then a proper uncertainty handling method is proposed for each category.

• 한국치위생학회지
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• 제21권2호
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• pp.193-201
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• 2021

Objectives: We evaluated the learning objectives of dental hygiene courses based on Bloom's learning objectives, and analyze the degree of match with the dental hygienist's job for each detailed subject. Methods: The 5th edition of 'Dental hygiene and learning objectives' was analyzed by subject based on Bloom's cognitive domain classification from March 10 to April. In addition, the degree of match between the contents of the secondary job analysis of the dental hygienist and the learning objectives for each detailed subject were analyzed. Results: The total number of dental hygiene learning objectives was 2,975 (2,762 theory, 52 practice). Among the cognitive domains, the comprehension domain was the most common (79.8%), and the skill domain was very low (4.9%). In the job for each detailed subject of dental hygiene, the most frequently performed was 'dental prophylaxis and practice' with 103 subjects. Conclusions: Overall, dental hygiene learning objectives are mostly theory-oriented, so it is necessary to expand and improve in the direction related to the jobs that clinical dental hygienists perform in the field. In addition, it is necessary to continuously develop timely learning goals, and prepare active strategies for developing high-quality items.

• 한국정보처리학회논문지
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• 제6권11호
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.