• 대한간호학회지
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• 제26권1호
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• pp.177-193
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• 1996

For the purpose of integrating nursing diagnosis into the nursing curriculum, a descriptive survey research was done using the inductive method with questionnaires and a literature review. Research subjects included nurse educators, textbooks of adult nursing published in Korea, and the course outline for adult nursing used in one college of nursing. The Results show that there was common agreement on 39 nursing diagnosis which should be in cluded in the adult nursing curriculum, textbooks of adult nursing, and patient care on the medical-surgical units. The two existing nursing diagnosis classification systems(NANDA and Gordon's Human Response Patterns) show different basic frameworks and difficulties were discovered in integration of nursing diagnosis into the curriculum. To develop a conceptual framework for a nursing diagnosis classification system, diagnosis were classified into three categories ; health promotion, high risk problem, and actual problem on the basis of the framework used in adult nursing textbooks and Gordon's 11 Functional Health Patterns. Subconcepts for actual problems were classified as ; activity and rest, nutrition and elimination, perception and coordination, stress and coping. Progress in this study supports further development of a conceptual framework of nursing based on a nursing diagnosis classification system, from which improvement in nursing education and clinical practice can be expected.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1388-1389
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• 2015

Electropulsegraphy is a medical device that was invented by an orient medical physician and a few engineers to help the physicians to diagnose patients in more systematic way by analyzing waveforms generated from the device. Data generated form the device has been collected for over several decades, and undergoes functional upgrades today. The device generates 33 waveforms that reflect the states of patients. As one of those upgrading efforts, we strive to develop an intelligent algorithm that makes the diagnostic process automatically, which was previously done manually for a long period of time. The logistic regression algorithm is used for our classification problems, which is one of those well-known algorithms for various classification problems such as character recognition systems. Out of the 33 waveforms, we only use 5 waveform data (Type1 toType5) as training data sets to estimate the parameters of the logistic regression. And the parameters are used to classify waveform inputs chosen at random.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.309-322
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• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• Journal of Microbiology and Biotechnology
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• 제16권10호
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• pp.1650-1655
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• 2006

Bacteria belonging to the genus Paenibacillus are facultatively anaerobic endospore formers and are attracting growing ecological and agricultural interest, yet their genome information is very limited. The present study surveyed the genomic features of P. polymyxa ATCC $842^T$ using pulse-field gel electrophoresis of restriction fragments and sample genome sequencing of 1,747 reads (approximately 17.5% coverage of the genome). Putative functions were assigned to more than 60% of the sequences. Functional classification of the sequences showed a similar pattern to that of B. subtilis. Sequence analysis suggests nitrogen fixation and antibiotic production by P. polymyxa ATCC $842^T$, which may explain its plant growth-promoting effects.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
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• pp.101-106
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• 2004

Subcellular localization is a key functional char acteristic of proteins. With the number of sequences entering databanks rapidly increasing, the importance of developing a powerful tool to identify protein subcellular location has become self-evident. In this paper, we introduce a novel method for predic ting protein subcellular locations from protein sequences. The main idea was motivated from the observation that amino acid pair composition data is redundant. By classifying from multiple feature subsets and using many kinds of amino acid pair composition s, we forced the classifiers to make uncorrelated errors. Therefore when we combined the predictors using a voting scheme, the prediction accuracy c ould be improved. Experiment was conducted on several data sets and significant improvement has been achieve d in a jackknife test.

• 대한조선학회논문집
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• 제46권1호
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• pp.57-68
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• 2009

In automotive industries, reusability of product information is increasing through database construction of previous product data. The product data is stored by data quality management in product information systems. For naval ships, have the functional similarity by the ships of the same classification and class, that are built by series. Information of hull structures as well as embarked equipments are similar. So it would be effective to use database systems that are considered product information quality of previous ships in design and production processes. In this paper we discuss product information quality in database construction of naval ship product models. For this, we propose a basic concept and reference model for data quality verification. Based on this concept, A verification guideline is defined and it is applied for the case study of the digital naval ship which was built to the naval ship product model.

• Genomics & Informatics
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• 제7권2호
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• 한국멀티미디어학회논문지
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• 제11권5호
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• pp.710-727
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• 2008

서비스지향 컴퓨팅 시스템은 시스템의 기능적 단위인 서비스들을 재사용함으로 해서 개발 시간과 노력을 줄이는 특성 때문에 분산 환경이 일반화 되면서 더욱 중요하게 부각되고 있다. 서비스의 재사용은 서비스들 간의 느슨한 결합에 의하여 효과적으로 이루어질 수 있다. 그러나 상속 및 포함 관계와 같은 객체지향 시스템의 강한 연관 관계들은 객체들 간에 강한 결합을 생성한다. 상속 관계와 포함 관계가 없는 컴포넌트 기반의 시스템은 컴포넌트들 간에 느슨한 결합을 생성한다. 그리하여 컴포넌트 인터페이스들에 의해서 제공된 기능을 사용해서 실시간에 서비스지향 시스템의 서비스를 실현한다. 따라서 컴포넌트기반 시스템은 기능적 서비스 단위들을 효율적으로 제공하기 위하여 서비스지향 컴퓨팅 시스템으로 확장될 필요가 있다. 또한, 서비스지향 컴퓨팅 시스템을 지원하는 기존의 방법들은 서비스 계층의 명확한 분류 및 서비스 계층에 따른 명확한 서비스 식별 가이드라인 그리고 서비스 계층 간의 매핑 방법을 제시하지 않고 있다. 따라서 본 논문에서는 비즈니스 관점의 서비스와 구현 관점의 서비스를 계층으로 나누어 분류하고 서비스 식별 가이드라인 및 각 계층의 서비스들 간의 매핑을 제안한다. 즉, 우리는 서비스 계층과 다양한 크기의 서비스 식별 방법을 연구하고, 각 계층의 서비스들 간의 매핑 방법을 도출한다. 이를 기반으로 기존 컴포넌트 기반 시스템을 서비스 지향 컴퓨팅 시스템으로 확장할 수 있다.

• International journal of advanced smart convergence
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• 제11권1호
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.