• Science of Emotion and Sensibility
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• v.27 no.3
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• pp.15-26
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• 2024

This study aimed to predict psychopathology based on personality measures via supervised machine learning methodology. We implemented the Singer-Loomis Type Deployment Inventory (SLTDI) for psychological typology and the Korean version of the Revised Symptom Checklist 90 (KSCL-95) for psychopathology. A total of 521 Korean adults from across the country participated in the online survey. Statistical analyses including correlation, k-means cluster analysis, classification, and regression-based decoding were performed. Results revealed four differentiated clusters on the spectrum of clinical severity. Moreover, SLTDI could distinguish between hypothesis-driven and data-driven clusters by chance. KSCL-95's three subcategories, as well as its validity, were accurately classified. Regression-based decoding results showed that their typology data significantly predicted social desirability, depression, anxiety, obsessive-compulsive disorder, PTSD, schizophrenia, stress vulnerability, and interpersonal sensitivity significantly. Overall, these findings suggest that personality tests could be utilized to screen for the severity of psychopathology and to implement prevention and early intervention strategies.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3101-3103
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• 2016

Background: According to the most recent estimation of GLOBOCAN, Cambodia has the highest incidence and mortality rate of cervical cancer in Southeast Asia. A screen-and-treat strategy using visual inspection with acetic acid (VIA test) and cryotherapy has been implemented in Cambodia's national cervical cancer screening program since 2013. However, where resources are available, cervical cytology with or without high-risk HPV DNA testing is the preferred screening method used in this country. Aim: This study aims to calculate the prevalence of abnormal cervical cytology and explain the possible factors contributing to a reduced quality of cervical cytology among women participating in a hospital-based cervical cancer screening program in Cambodia. Materials and Methods: A descriptive study was conducted using information from the cytology and pathology database in the Department of Pathology of Calmette Hospital between January 2012 and December 2015. Prevalence of abnormal cervical cytology, based on the Bethesda 2001 classification, was calculated. Data on the adequacy of cytological specimens were analyzed in order to explain the factors contributing to a reduced quality of cervical cytology interpretation. Results: Among 6,207 women who participated in the cervical cancer screening program at Calmette Hospital during 2012 and 2015, 388 (6.25%) had abnormal cytology, which could be classified into Atypical Squamous Cells of Undetermined Significance (92 cases; 1.48%), Atypical Squamous Cells - Cannot Exclude High-Grade Intraepithelial Lesion (13 cases; 0.21%), Atypical Glandular Cells (11 cases; 0.18%), Low-Grade Squamous Intraepithelial Lesion (221 cases; 3.56%), High-Grade Squamous Intraepithelial Lesion (26 cases; 0.42%), and Squamous Cell Carcinoma (25 cases; 0.40%). Unsatisfactory smears made up 12.2% of the total cases. The most frequently identified factor leading to unsatisfactory smears was the absence of cells from the transformation zone. Conclusions: The present study showed an overall prevalence of abnormal cervical cytology of 6.25%, which is comparable to that in many large population-based studies in the Asia Pacific region. Nevertheless, the remarkably high rate of unsatisfactory smears in this study justifies further improvement in specimen sampling among Cambodian gynecologists.

• Journal of the Korean Dietetic Association
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• v.19 no.4
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• pp.343-355
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• 2013

This study analyzed the dietary habits and nutrient intake status of adult drinkers in Korea. Alcohol drinking patterns were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES). Among 4,968 persons, 91.5% were drinkers. Classification of their drinking patterns by the Alcohol Use Disorders Identification Test (AUDIT) score showed 64.5% of the drinkers were normal drinkers, 22.4% problem drinkers and 13.2% alcohol-dependent drinkers. Overall, 47% of the drinkers were considered alcohol-dependent in the Rapid Alcohol Problems Screen (RAPS4). Significant differences were found between those who abstained from alcohol (86.8%) and alcohol-dependent drinkers (68.9%); when asked about breakfast habits 73.4% of non-drinkers often had family meals, while only 55.4% of the alcohol-dependent drinkers had family meals. Dietary energy, alcohol energy, and total energy intake significantly increased for the alcohol-dependents (P<0.001). In addition, the intake of eight nutrients (protein, vitamin A, vitamin $B_1$, vitamin $B_2$, niacin, calcium, phosphorous and iron), significantly increased in the following order (least to highest): abstainers, normal drinkers, drinkers with a moderate addiction to alcohol and alcohol-dependent drinkers (P <0.05). Nutrient Adequacy Ratios (NAR) of all nutrients, except vitamin C, and the Mean nutrient Adequacy Ratio (MAR) significantly increased in the following order (least to highest): abstainers, normal drinkers, drinkers with a moderate addiction to alcohol and alcohol-dependent drinkers (P<0.05). The intake of vitamin $B_1$, vitamin $B_2$, and niacin per 1,000 kcal, according to drinking pattern, decreased in the order of abstainers, normal drinkers, drinkers with a moderate addiction to alcohol, and alcohol-dependent drinkers (P<0.001). The above results show that the nutrient intake of normal drinkers, drinkers with a moderate addiction to alcohol, and alcohol-dependent drinkers are higher than abstainers. However, overall intake of vitamin $B_1$, vitamin $B_2$, and niacin per 1,000 kcal was low. Therefore it is necessary to increase vitamin $B_1$, vitamin $B_2$, and niacin intake for drinkers.

• Korean Journal of Plant Resources
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• v.32 no.4
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• pp.303-311
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• 2019

Various colors of fruit skin and flesh are the most popular commercial criteria for peach classification. In order to breed new red-fleshed peach cultivar, many cross seedlings and generations should be maintained. Therefore it is necessary to develop early selection markers to screen seedlings with target traits to increase breeding efficiency. For the comparison of transcription profiles in peach cultivars differing in flesh color expression, two cDNA libraries were constructed. Differences in gene expression between red-fleshed peach cultivar, 'Josanghyeoldo' and white-fleshed peach cultivar, 'Mibaekdo' were analyzed by next-generation sequencing (NGS). Expressed sequence tag (EST) of clones from the two cultivars were selected for nucleotide sequence determination and homology searches. Putative single nucleotide polymorphisms (SNP) were screened from peach EST contigs by high resolution melting (HRM) analysis displayed specific difference between 8 red-fleshed peach cultivars and 24 white-fleshed peach cultivars. All 72 pairs of SNPs were discriminated and the HRM profiles of amplicons were established. In the study reported here, the development of SNP markers for distinguishing between red and white fleshed peach cultivars by HRM analysis offers the opportunity to use DNA markers. This SNP marker could be useful for peach marker assisted breeding and provide a good reference for relevant research on molecular mechanisms of color variation in peach cultivars.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1194-1202
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• 2012

Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields ($3,521km^2$) were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).

• Progress in Medical Physics
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• v.16 no.1
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• pp.10-15
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• 2005

IAEA's guidance levels have been provided for western people to the end. Guidance levels lower than the IAEA'S will be necessary in view of Korean people's proportions. Therefore, we need to develope the standard doses for Korean people. And we conducted a nationwide survey of patient dose from x-ray examinations in Korea. In this study, the 278 institutions were selected from Members Book of Korean Hospital Association. The valid response rate was approximately 57.9%. Doses were calculated from the questionnaires by NDD method. We obtained the results were as follows; 1) General radiographic equipments were distributed for 42.0%, fluoroscopic equipments 29.4%, dental equipments 13.2%, CT units 8.1 % and mamographic units 7.2%. 2) According to classification by rectification, three-phase equipments were 29.9%, inverter-type generators 29.5%, single-phase equipments 25.5%, constant voltage units 9.0% and unknown units 6.0%. 3) According to classification by receptor system, film-screen types were 46.8%, CR types 26.8%, DR types 17.7% and unknown types 8.9%. 4) The number of examinations were chest 49.2%, spine 16.8% and abdomen 12.7%. 5) Patient doses were head AP 3.44 mGy, abdomen AP 4.25 mGy and chest PA 0.39 mGy.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.