• Journal of Korean Biological Nursing Science
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• v.1 no.1
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• pp.1-24
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• 1999

The purpose of this study was to define the content of requisite human structure and function knowledge needed for clinical knowledge of nursing practice. Subjects of human structure and function were divided into 10 units, and each unit was further divided into 21 subunits, resulting in a total of 90 items. Contents of knowledge of human structure and function were constructed from syllabus of basic nursing subjects in 4 college of nursing, and textbooks published by nurse scholars prepared with basic nursing sciences. The degree of need of 90 items was measured with a 4 point scale. The subjects of this study were college graduated 136 nurses from seven university hospitals in Seoul and three university hospitals located in Chonnam Province, Kyungbook Province, and Inchon. They have been working at internal medicine ward, surgical ward, intensive care unit, obstetrics and gynecology ward, pediatrics ward, opthalmology ward, ear, nose, and throat ward, emergency room, rehabilitation ward, cancer ward, hospice ward, and their working period was mostly under 5 years. The results were as follows: 1. The highest scored items of human structure and function knowledge necessary for nursing practice were electrolyte balance, blood clotting mechanism and anticoagulation mechanism, hematopoietic function, body fluid balance, function of plasma, and anatomical terminology in the order of importance. The lowest scored items of human structure and function knowledge necessary for nursing practice was sexual factors of genetic mutation. 2. The highest order of need according to unit was membrane transport in the living unit, anatomical terminology in movement and exercise unit, mechanism of hormone function in regulation and integration unit, component and function of blood in oxygenation function unit, structure and function of digestive system in digestive and energy metabolism unit, temperature regulation in temperature regulation unit electrolyte balance in body fluid and electrolyte unit, concept of immunity in body resistance unit, and genetics terminology in genetics unit. The highest order of importance according to subunit was membrane transportation in cell subunit, classification of tissues in tissue unit, function of skin and skin in skin subunit, anatomical derivatives of the skeleton subunit, classification of joints in joint subunit, an effect of exercise on muscles in muscle subunit, function of brain in nervous system subunit, special sense in sensory subunit mechanism of hormone function in endocrine subunit, structure and function of female reproductive system in reproductive system unit, structure and function of blood in blood unit, structure of heart, electrical and mechanical function in cardiovascular system unit, structure of respiratory system in respiratory system subunit, structure and function of digestive system in digestive system subunit, hormonal regulation of metabolism in nutrition and metabolism subunit, function of kidney in urologic system subunit, electolyte balance in body fluid, electolyte and acid-base balance subunit. 3. The common content of human structure and function knowledge need for all clinical areas in nursing was structure and function of blood, hematopoietic function, function of plasm, coagulation mechanism and anticoagulation mechanism, body fluid, electrolyte balance, and acid-base balance. However, the degree of need of each human structure and function knowledge was different depending on clinical areas. 4. Significant differences in human structure and function knowledge necessary for nursing practice such as skin and derivatives of the skin, growth and development of bone, classification of joint, classification of muscle, structure of muscle, function of muscle, function of spinal cord, peripheral nerve, structure and function of pancrease, component and function of blood, function of plasma, structure and function of blood, hemodynamics, respiratory dynamics, gas transport, regulation of respiration, chemical digestion of foods, absorption of foods, characteristics of nutrients, metabolism and hormonal regulation, body energy balance were demonstrated according to the duration of work. 5. Significant differences in human structure and function knowledge necessary for nursing practice such as classification of tissue, classification of muscles, function of muscles, muscle metabolism, classification of skeletal muscles, classification of nervous system, neurotransmitters, mechanism of hormone function, pituitary and pituitary hormone, structure and function of male reproductive organ, structure and function of female reproductive organ, component and function of blood, function of plasma, coagulation mechanism and anticoagulation mechanism, gas exchange, gas transport, regulation of respiration, characteristics of nutrients, energy balance, function of kidney, concept of immunity, classification and function of immunity were shown according to the work area. Based on these findings, all the 90 items constructed by Korean Academic Society of Basic Nursing Science should be included as contents of human structure and function knowledge.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.77-82
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• 2001

A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.5-24
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• 2012

The aim of this study is 1) to analyse the historical development and current condition of the subject based classification(SBC), 2) to clarify the function and to categorize the specific kind of SBC, for its conceptualization and the development plan. For this purpose, almost 30 cases, for the period 1937 through now, were analyzed concerning their terms used in the names and the specific kinds as SBC. In addition, the analysis was made regarding how the SBC fulfill the selected main functions as a classificatory scheme and how SBC is inter-related with the other knowledge organization systems(KOS) such as classification and subject heading. Based on the above analysis, the conclusion addressed that SBC could be defined in consideration of the detailed function, type, information environment, and interconnection among the KOS, and suggested the future development plan of SBC as a classification scheme.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• ETRI Journal
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• v.42 no.1
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• pp.67-77
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• 2020

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• Convergence Security Journal
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• v.20 no.4
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• pp.81-90
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• 2020

In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• 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.