• Journal of Industrial Convergence
• /
• v.16 no.4
• /
• pp.33-46
• /
• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Journal of Korean Society of Environmental Engineers
• /
• v.33 no.6
• /
• pp.439-446
• /
• 2011

In this research, enhanced land-cover classification methods using high-resolution satellite image (HRSI) and GIS in terms of practicality and accuracy was proposed. It aims for understanding non-point pollutant origin/loading, assessment the efficiency of rainfall storage/infiltration facilities and sounds water-environment management. The result of applying enhanced land-cover classification methods to the urban region verifies that roof and road area are including various vegetations such as roof garden, flower bed in the median strip and street tree. This accounts for 3% of total study area, and more importantly it was counted as impervious area by GIS alone or conventional indoor work. The feasibility of the method was assessed by applying to rainfall-runoff analysis for three weak rainfall in the range of 7.1-10.5 mm events in 2000, Chiba, Japan. A good agreement between simulated and observed runoff hydrograph was obtained. In comparison, the hydrograph simulated with land-use parameters by the detailed land-use information of 10m grid had an error between 31%~71%, while enhanced method showed 4% to 29%, and showed the improvement particularly for reproducing observed peak and recession flow rate of hydrograph in weak rainfall condition.

• Journal of Korean Society of Forest Science
• /
• v.39 no.1
• /
• pp.35-46
• /
• 1978

The morphological characteristics of vascular bundle sheath occurring on the culm wall were investigated by using many species of Bambusa and Sasa in order to test new classification method for endomorphological charateristics of Bamboos. The results obtained were as follows. 1. As for the thickness of the culm wall in the culm, it was shown that the culm wall of the Bambusa becomes thinner in propertion to its nearness to the upper part of the tree, but no distinctive difference appeared in the Sasa. 2. It was shown that many species of Bambusa has a, b types but the Sasa had a' type and had a, b types. 3. It was shown that many species of Bambusa had e', h, and i types but the Sasa had not them and both of species had not f type. 4. It was shown that many species of Bambusa had c, d, e, and g types, but the Sasa had c, d, and e types and had not g type. 5. In the classification of Bambusa and Sasa, The method of the physiological classification was more effective than test of external observation, and it will encourage further study.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.41 no.1
• /
• pp.60-70
• /
• 2013

This study focused on analyzing classification system, technique methods, quality levels of periodic amendment characteristics in planting of standard specification. Through analyzing the above and comparing with foreign case study, this study suggests the improvement directions. The results improvement directions are as follows. 1. Many kinds of new construction were set up through the amendments of Landscape Standard Specification, but there are still needs to combine some construction categories because of mismatches between upper and lower categories. 2. Although the Landscape Standard Specification was revised to be more concrete, the contents there remains an ambiguous expression. So, standard specification is needed to revise a depth of earth ball or strength of support materials and quantify collect period of topsoil and application time. In addition, standards about following supervisor's instruction should be more detailed or deleted. 3. The standard specification has not been specified despite enactment and amendments reflecting the periodical paradigm and the needs of users, so it is still needed to revise. In addition, quality levels, planting periods, size of earth ball and performance criteria of tree materials are needed to revise. Each specific classification and construction methods were made by amendments of standard specification, but some standards are not clear and concrete. Therefore, the standard specification is needed to revise the classification system, technique methods, and problem deduction of quality levels and proposal of improvement. This study will be reference material when Landscape Standard Specification is revised.

• Biomedical Science Letters
• /
• v.10 no.4
• /
• pp.485-493
• /
• 2004

In the current field of Medical Informatics, the information increases, and changes fast, so we can access the various data types which are ranged from text to image type. A small number of technician digitizes these data to establish database, but it is needed a lot of money and time. Therefore digitization by many end-users confronting data and establishment of searching database is needed to manage increasing information effectively. New data and information are taken fast to provide the quality of care, diagnosis which is the basic work in the medicine. And also It is needed the medical database for purpose of private study and novice education, which is tool to make various data become knowledge. However, current medical database is used and developed only for the purpose of hospital work management. In this study, using text input, file import and object images are digitized to establish database by people who are worked at the medicine field but can not expertise to program. Data are hierarchically constructed and then knowledge is established using a tree type database establishment method. Consequently, we can get data fast and exactly through search, apply it to study as subject-oriented classification, apply it to diagnosis as time-depended reflection of data, and apply it to education and precaution through function of publishing questions and reusability of data.

• Korean Journal of Adult Nursing
• /
• v.16 no.2
• /
• pp.316-326
• /
• 2004

Purpose: The purpose of this study was to identify effects of a self-management program on self-efficacy and compliance in patients with CHF. Hypothesis: 1) Patients with CHF who are provided with a self-management program will show higher self-efficacy scores than a control group. 2) Patients who are provided with a self-management program will show higher compliance scores than a control group. Method: This study was designed as a nonequivalent non-synchronized pre-posttest control group. There were eight patients in the experimental group, and twelve in the control group. According to NYHA classification, all patients belonged under the classesII to IV. Data were collected using the instruments developed by the researchers. Data were analyzed using descriptive statistics and Mann Whitney U test. Result: There were significant differences in self-efficacy scores and compliance scores between the experimental and control group. Conclusion: By utilizing the program, patients were able to monitor their symptoms routinely, comply with therapeutic regimen, and feel better able to positively influence their disease. Therefore, better compliance means fewer readmissions of patients with CHF.

• Annual Conference on Human and Language Technology
• /
• 1999.10e
• /
• pp.184-190
• /
• 1999

본 논문에서는 Text-to-Speech 시스템에서 사용할 억양 모델을 위해 음성 DB에서 모델 파라미터와 피치 검색테이블(lookup-table)을 추출하여 미리 구성하고, 합성시에는 이를 추정하여 최종 F0 값을 생성하는 자료기반 접근방식(data-driven approach)을 사용한다. 어절 경계강도(break-index)는 경계강도의 특성에 따라 고정적 경계강도와 가변적 경계강도로 세분화하여 사용하였고, 예측된 경계강도를 기준으로 억양구(Intonation Phrase)와 액센트구(Accentual Phrase)를 설정하였다. 특히, 액센트구 모델은 인지적, 음향적으로 중요한 정점(peak)을 정확하게 모델링하는 것에 주안점을 두어 정점(peak)의 시간축, 주파수축 값과 이를 기준으로 한 앞뒤 기울기를 추정하여 4개의 파라미터로 설정하였고, 이 파라미터들은 CART(Classification and Regression Tree)를 이용하여 예측규칙을 만들었다. 경계음조가 나타나는 조사, 어미는 정규화된(normalized) 피치값과 key-index로 구성되는 검색테이블을 만들어 보다 정교하게 피치값을 예측하였다. 본 논문에서 제안한 억양 모델을 본 연구실에서 제작한 음성합성기를 통해 합성하여 청취실험을 거친 결과, 기존의 상용 Text-to-Speech 시스템에 비해 자연스러운 합성음을 얻을 수 있었다.

• Journal of Forest and Environmental Science
• /
• v.37 no.4
• /
• pp.269-279
• /
• 2021

This study was carried out to introduce the forest cover types of the Baekdudaegan inhabiting the number of native tree species. In order to understand the vegetation distribution characteristics of the Baekdudaegan, a vegetation survey was conducted on the major 20 mountains of the Baekdudaegan. The vegetation data were collected from 3,959 sample points by the point-centered quarter method. Each mountain was classified into 4-7 forests by using various multivariate statistical methods such as cluster analysis, indicator species analysis, multiple discriminant analysis, and species composition analysis. The forests were classified mainly according to the relative abundance of Quercus mongolica. There was a total of 111 classified forests and these forests were integrated into the following nine forest cover types using the percentage similarity index and by clustering according to vegetation type: 1) Mongolian oak, 2) Mongolian oak and other deciduous, 3) Oaks (Mixed Quercus spp.), 4) Korean red pine, 5) Korean red pine and oaks, 6) ash, 7) mixed mesophytic, 8) subalpine zone coniferous, and 9) miscellaneous forest. Forests grouped within the subalpine zone coniferous and miscellaneous classifications were characterized by similar environmental conditions and those forests that did not fit in any other category, respectively.

• ETRI Journal
• /
• v.44 no.4
• /
• pp.613-623
• /
• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.141-143
• /
• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.