• Journal of Korean Academic Society of Home Health Care Nursing
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• v.5
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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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• v.25 no.2
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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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• v.13 no.2
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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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• v.23 no.3E
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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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• v.1 no.2
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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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• v.7 no.4
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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.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Journal of the military operations research society of Korea
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• v.23 no.2
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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.

• Journal of Korean society of Dental Hygiene
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• v.21 no.2
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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.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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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.