• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.1-13
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• 2009

The purpose of this study was to look into the feasibility of site suitability focused on the potential for environmentally- and water-friendly recreation area development in a wide area(Nakdong River 35km) and to study new methods for providing basic data in regard to the recreation planning over a wide area as well as in application to other sites. The results of this study are as follows. Through classification by mesh method, the site of this study was classified into 42 grids, and by means of the analysis of evaluation indicators, 20 indicators were established and sorted into 4 types of significant recreation activity. According to the results of the analysis for each recreation activity type, there were 8 essentials for water-friendly recreation activity types based on water use while water-friendly recreation types for static activity included 12 sub-essentials. As a result of the first evaluation(the minimum required evaluation) by each classified grid, 32 of the 42 total grids were implemented by the minimum requirements. These grids were usually distributed evenly through the whole site. In terms of the second evaluation(specific site evaluation) results, 6 grids were highly suitable for recreational nature experiences and landscape ecological learning, 4 grids for developing water-friendly recreation for exercise, 1 grid for building water-friendly recreation based on water use, and 4 grids for planning water-friendly recreation for static activity. The results of the grid evaluation of this study could be extended to contiguous grids or reduced. Actual planning for a water-friendly recreation area must change the grid shape or size through boundary adjustments.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• 대한공업교육학회지
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• v.40 no.2
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• pp.155-174
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• 2015

The purposes of this study were to analyze the property of educational needs of teachers for educational contents of invention and intellectual property in secondary vocational education and provide fundamental data for the development of job training programs so as to develop the capabilities of teachers, the base for effective education of invention intellectual property in secondary vocational education. To achieve them, educational needs for the educational contents of invention intellectual property and the priority of the educational needs in secondary vocational education based on the recognition of the teachers were analyzed and suggested. Concrete results of this study can be suggested as follows. First, the average of educational needs of the teachers for the educational contents of invention intellectual property in secondary vocational education was 5.02. There were 23 items of the educational contents whose educational needs were higher than the average of the whole items and for those items and the average of each item, there were F4(The average of patent applications) 6.72, F5(Modification and supplementation of specification sheets) 6.46, F2(Writing of patent floor plans) 6.39, F3(Writing of patent specification sheets and abstraction) 6.31, A5(Invention method and activity) 6.27, E6(Invention design project) 6.15, H3(Invention commercialization) 5.97, F1(Patent information and application) 5.90, E5(Design obligation) 5.78, E3(Designing process of inventional design) 5.77, A4(Invention and problem solving) 5.57, G2(Patent investigation and classification) 5.47, C2(Thinking method of inventional problem solution) 5.45, E4(Production of inventional design product) 5.45, B5(Inventional patent project) 5.42, A2(Creativity development) 5.26, C4(Inventional problem solving project) 5.26, H4(Invention marketing) 5.26, H2(Analysis on invention commercialization) 5.20, D4(Invention and management) 5.16, C3(Problem solving activity) 5.14, E2(Inventional design devise and expression) 5.11, B3(Actuality of inventional method) 5.08 in order. Second, for the priority of educational needs of the teachers for the educational contents of invention intellectual property in secondary vocational education, there were 13 items of the educational contents for the first rank, 10 for the second rank and 17 for the third rank. The items of the educational contents for the first rank were A4(invention and problem solving), A5(inventional method and activity), B5(Invention patent project), C2(Thinking method of inventional problem solution), C4(Inventional problem solving project), E3(Inventional design process), E4(Production of inventional design product), E5(Design obligation), E6(Invention design project), F1(Patent information and application), F2(Writing of patent floor plan), F3(Writing of patent specification sheet and abstract), and H3(Invention commercialization. The items of the educational contents for the second rank were A2(Creativity development), B3(Actuality of inventional method), C3(Problem solving activity), D4(Invention and management), E2(Invention design devise and expression), F4(Range of patent demand), F5(Modification and supplementation of specification sheet), G2(Patent investigation and classification), H2(Analysis on invention commercialization), and H4(Invention marketing). The items for the third rank were the educational contents except the ones of the first rank and the second rank.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.14 no.2
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• pp.110-116
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• 2003

Background and Objectives : Laryngeal cancer discrimination using voice signals is a non-invasive method that can carry out the examination rapidly and simply without giving discomfort to the patients. n appropriate analysis parameters and classifiers are developed, this method can be used effectively in various applications including telemedicine. This study examines voice analysis parameters used for laryngeal disease discrimination to help discriminate laryngeal diseases by voice signal analysis. The study also estimates the laryngeal cancer discrimination activity of the Gaussian mixture model (GMM) classifier based on the statistical modelling of voice analysis parameters. Materials and Methods : The Multi-dimensional voice program (MDVP) parameters, which have been widely used for the analysis of laryngeal cancer voice, sometimes fail to analyze the voice of a laryngeal cancer patient whose cycle is seriously damaged. Accordingly, it is necessary to develop a new method that enables an analysis of high reliability for the voice signals that cannot be analyzed by the MDVP. To conduct the experiments of laryngeal cancer discrimination, the authors used three types of voices collected at the Department of Otorhinorlaryngology, Pusan National University Hospital. 50 normal males voice data, 50 voices of males with benign laryngeal diseases and 105 voices of males laryngeal cancer. In addition, the experiment also included 11 voices data of males with laryngeal cancer that cannot be analyzed by the MDVP, Only monosyllabic vowel /a/ was used as voice data. Since there were only 11 voices of laryngeal cancer patients that cannot be analyzed by the MDVP, those voices were used only for discrimination. This study examined the linear predictive cepstral coefficients (LPCC) and the met-frequency cepstral coefficients (MFCC) that are the two major cepstrum analysis methods in the area of acoustic recognition. Results : The results showed that this met frequency scaling process was effective in acoustic recognition but not useful for laryngeal cancer discrimination. Accordingly, the linear frequency cepstral coefficients (LFCC) that excluded the met frequency scaling from the MFCC was introduced. The LFCC showed more excellent discrimination activity rather than the MFCC in predictability of laryngeal cancer. Conclusion : In conclusion, the parameters applied in this study could discriminate accurately even the terminal laryngeal cancer whose periodicity is disturbed. Also it is thought that future studies on various classification algorithms and parameters representing pathophysiology of vocal cords will make it possible to discriminate benign laryngeal diseases as well, in addition to laryngeal cancer.

• Journal of Science Education
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• v.36 no.2
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• pp.167-185
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• 2012

This study aimed at finding points of improvement in teaching expertise by analyzing the question patterns that appeared during teaching demonstrations which applied science teaching models prepared by a pre-service biology teacher. The question analysis frame for analyzing question types were categorized largely into the question types of Category 1 (questions in cognitive domain, questions with research function, questions in affective domain), Category 2 (repeated questions, questions for narrowing the range, practice questions), and Category 3 (questions on student activity progress, memory questions, and thinking questions). The results of analyzing question patterns from five different science teaching models revealed a high frequency of questions in the fields of cognition and memory. For the circular learning model, questions from the cognitive field appeared the most often, while, student activity progressive questions in particular were used mostly in the 'preliminary concept introduction stage' of the circular learning model and the 'secondary exploratory stage', in which experiments were conducted, and displayed the characteristics of these stages. The discovery learning model combined the courses of observation, measurement, classification and generalization, but, during teaching demonstrations, memory questions turned up the most, while the portion of inquisitive function questions was low. There were many questions from the inquisitive learning model, and, compared to other learning models, many exploratory function questions turned up during the 'experiment planning stage' and 'experiment stage'. Definitional questions and thought questions for the STS learning model turned up more than other learning models. During the change of concept learning model, the five concepts of students were stimulated and the modification of scientific concepts was very much aided by using many memory questions.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.63-77
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• 2015

Improve the rural views of the Government and the municipalities with the intention of rural landscape planning. However, rural scenery evaluation of landscape types and element categories, landscape scenes, such as the valuation of resources selected, form a coherent system and ecological point of view is not only a Visual perspective of evaluation, or evaluation is made. This study evaluates the rural landscape through the rural landscape for rurality is implemented can explain the image. Rural amenity 100 represented the rural landscape in rurality with photos of photos, and scenes, photos, location-based rural Vista rurality landscapes drawn components. Selected landscape photography scenic landscapes in the u.s. survey and image vocabularies through surveys is representative of the landscape in rurality of each Vista location-specific photos and images began extracted factors. As a result, the Distant view is a town and village in the rolling hills of the surrounding environment is an image behind the Vista Skyline, Farmland Rural village forests, Behind the hills, Individual housing roof, Housing arrangement, The number of household includes landscape components. Factor analysis 'openness', 'intimacy', 'activity', 'complexity', 'safety' was down to five factors, such as a description of the overall factors that force was 66.45 percent. Middle distance view is close to being out of town houses and village environment, an image in the skyline hills Vista, farmlands, village forests, rolling hills, behind the scenes of the housing component of the neck, the sperm will honor an individual factor analysis results are 'intimacy', 'safety', 'openness', 'specificity', 'complexity' five factors the whole description of the capacity factors compared to 67.24 percent. Close-range view is mainly in Vista village embraced the individual elements and an image of a harmonious location in the surroundings of the sperm, individual houses, fences, gates, courtyard, Garden, garage, agricultural facilities, including, but not limited to, factors assay but an 'intimacy', 'complexity', 'safety', 'activity', 'openness' five factors, such as a description of the overall factors that force was 65.29%. This research seeks to determine the extent to which rurality of rural landscape Vista by location attributes and elements and representative officer in photo selection and rurality to extract the image. To date, followed by a comprehensive study, which was presented through the readjustment could not overlook the Visual elements. The future rural development officer for the evaluation of rural landscape classification and quantitative and objective assessment will have to come up with ways to do.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.4 no.1
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• pp.114-123
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• 1993

The authors applied Toddler Temperament Scale(TTS) to ADHD patient group, other disease group and normal control group to elucidate temperamental characteristics of ADHD patients in their toddler stage. 1) ADHD group showed significant difference from the control group in 'activity', 'rhythmicity', 'approach', 'adaptability', 'intensity', 'persistence' and 'threshold' categories. 2) ADHD group and other disease showed significant difference from the control group in 'adaptability' and 'persistence' categories. 3) ADHD group was significantly different from other disease group or control group in 'activity' and 'approach' categories. 4) In total scores of TTS, ADHD group and other diease group showed significant difference from control group. 5) Classification ratio of TTS for ADHD by discriminant analysis was 84.3%. Above results suggest that the ADHD patients may reveal characteristic traits in their toddler stage Further research should be focused on the development and refinement of assessment tools for the early detection and prevention for ADHD.

• Journal of the HCI Society of Korea
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• v.13 no.2
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• pp.5-20
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• 2018

The fully autonomous vehicle, which has been actively studied in a worldwide before commercialization, is expected to become a living space by securing time and space compared to existing automobile. For this reason, interior design of fully autonomous vehicle has become very important. To enhance passenger's experience and satisfaction in fully autonomous vehicle, it is necessary to design an optimized space that can support in-vehicle activities. For this purpose, efforts to analyze the passenger's in-vehicle activities should be preceded. However, there were limited studies that define space and in-Vehicle activities of fully autonomous vehicle in Korea. The purpose of this study is to suggest the guideline of the interior design of fully autonomous vehicle by analyzing and classifying the scope of activities that the passenger can perform within the vehicle. As a method of the study, literature studies on future concept cars, human lifetime behavior and consumer needs had been conducted. As a result in-vehicle activities could be applied in a fully autonomous vehicle. Four in-vehicle activities 'work', 'home life and personal care', 'relaxation' and 'conversation and hobby' had been derived through the analysis of in-vehicle activities. Based on the results, the interior design of fully autonomous vehicle guideline has been suggested. The study is significant because the result of the study can act as a basic study which considers the activities in the fully autonomous vehicle environment.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.252-271
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• 2020

In this study, we investigated physical and chemical properties such as grain size, heavy metal pollution, magnetic properties, and their environmental impacts of road dusts (RD) collected from 14 sampling points in Daebul industrial Complex. Heavy metal concentrations in RD were in the order of Fe>Zn>Cu>Pb>Cr>Ni>As>Cd>Hg, and this pollution pattern was related to major industries and traffic activities in this area. The results of the correlation analysis between heavy metal elements and particle size in RD showed that Fe and all of analyzed heavy metals had a significant correlation with each other and metal concentrations had a significantly negative correlation (p<0.05). However, due to the input of large metal particles some heavy metal concentrations in the particle fraction of >1000 ㎛ were highest. Pollution load per unit area of this fraction was the highest among the grain size fractions. Cr, Ni, Cu, Zn, Cd, Pb levels in RD decreased and the levels of Cr, Ni, Cu, Zn, Cd, and Pb were reduced to 85 (As) -22 (Ni)% of the whole after removal of MFs fraction from RD. The mean heavy metal levels in the study area did not exceed the soil contamination guide value of Korea, indicating that heavy metal levels in RD were not a concern. However, at some sampling points, Zn concentrations were exceeded the soil contamination guide value for the 3rd areas of Korea and this result indicated that further studies of the impact of RD on the surrounding environment through re-suspension or non-point pollution, and of effective management methods are required.