• The Journal of Korean Physical Therapy
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• v.18 no.4
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• pp.61-70
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• 2006

Purpose: The purpose of this study was to investigate the activities of daily living (ADL) and instrumental activities of daily living (IADL) of residential home dwelling elderly and home for the aged dwelling elderly. In attempt to address medical professional caring the elderly, this comparative study examines the factors associated with dependence in the ADL and IADL in two samples of elderly people living in two different environments. Methods: The instrument of ADL and IADL widely used Katz ADL and IADL. Katz ADL and IADL was not a perfect fit for Korean. In concern with cultural factors Won developed K(Korean)-ADL and K-IADL scale reflecting Korean's own language expression and cultural factors in year of 2002. The assessment tool of this study was K-ADL and K-IADL. Differences of ADL and IADL were tested for statistical significance using group t-test and x2 test for comparisons between the residential home dwelling elderly and the home for the aged dwelling elderly. Results: Comparison of assessment for K-ADL and K-IADL in two different dwelling types was significant. Performance of ADL and IADL depend upon their living environment such as social status, number of children, income, present illness as well as age group. This study also showed significant differences of performance in some activities of ADL and IADL between the elderly who live in their own home and live in home for the aged. Comparison of performance of ADL and IADL in different dwelling types revealed that only one item of ADL was significant but only one item of IADL was not significant. It means that IADL is more difficult activities in the home for the aged dwelling elderly than the residential home dwelling elderly. The coupled elderly has more independent in some ADL and IADL activities compared with the single elderly. Conclusion: Using K-ADL and K-IADL is more convenient for Korean elderly. Medical professional consider some factors like dwelling style, social status, existing diseases and disabilities in order to care the elderly and train him/her activities of daily living as well as instrumental activities of daily living. Medical professional, especially physical and occupational therapist emphasize the training items which are bathing of ADL and grooming, housework, preparing meals, laundry, traveling, public transportation, shopping, using telephone and taking medicine of IADL based on the result of this study.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Korean Society of Forest Science
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• v.87 no.2
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• pp.131-144
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• 1998

Understanding of the reasons why users in recreation settings select particular trails may provide useful information for effective recreation resources management. This study investigated the present use of the major trails in Mudeung-san Provincial Park and the major influential factors on trail selection. This study was conducted in Mudeung-san Provincial Park stretching over Hwasun-gun and Damyang-gun of Chonnam Province and Kwang-ju city during August in 1996. Data were collected through on-site survey and mail-back questionnaire. 519(44.2%) out of 1173 survey samples were used for analysis. Reasons for selecting a particular trail were classified into 8 major factors. In overall, the order of the importance degree of the factors was 1) aesthetics of landscape, 2) safety(from physical and crime), 3) conditions for health, 4) quietness, 5) familiarity, 6) on the way to the destination, 7) convenience/social, 8) others' intention. There were differences in the degree of importance of each factor depending on trails, users' characteristics(eg., gender, age, group size, visit experience, etc.) and users' behavioral patterns. Recreation motivations were classified into 5 major factors. The order of the importance degree of the factors was 1) contacting with nature, 2) self-training, 3) solitude, 4) social interaction, 5) appreciating cultural properties. Regardless of trails, 'contacting with nature' was the most important factor, and the degree of importance in the other 4 motivational factors showed only a little differences in order according to the trails. There were correlations between major factors for trail selection and recreation motivations. The results of this study may provide foundational information for establishing effective management strategies through better understanding of the present use of trails and influential factors on trail selection. It can be used for reducing the present social and ecological problems caused by use concentration on certain trails and providing users with better quality of diverse recreational experiences. This study discussed the findings, and suggested some management strategies based on these information.

• Korean Journal of Occupational Health Nursing
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• v.4
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• pp.5-29
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• 1995

Occupational health services in Korea have been operated as dual types : one is operated by occupational health care manager and the other is health care agency without their own personnel. The performance of occupational health service should be different due to the variety of characteristics of health care manager and workplace, qualification of health care manager. This study is to analyze performance of occupational health care services with a particular consideration of job performance shape and efficiency, based on comparing those two types of health care management to show on the basic data for the settlement of more qualitative health care management system at workplace. For this study, total 391 places in Seoul and Inchon city area ; 154 places (39.4%) managed by designated health care manager and 237 places (60.6%) by the agency with their commission are selected as research samples. Tools for data collection are questionnares that have been investigated during the period of 20 September 1993-20 December 1993. Those data are compared with percentiles, mean, standard deviation and B/C ratio using SPSS PC program. Conclusions observed from the tests and each comparison could be summerized as follows : 1. Occupational health care have been accomplished at workplaces with designated people than with agencies people, and coverage rate of the occupational health care services has differences, due to management types. The reason of these results is due to visit only one or two times monthly by the agencies, while their own health care manager obsess, at the workplaces all the times. 2. Most of the expense for environmental control of all health care services expenditures shows that there is almost no fundamental improvement because more expenses are needed for procuring personal protective equipment and measuring work environment instead of environmental improvement. 3. It is investigated how much the cost of occupational health care services needs per worker, and calculated how much the cost needs per service hour per worker. The results from this show that the cost of occupational health services at workplaces with their own managers used less than the cost of health care agencies, eventually the former gives better services with less cost than the latter. 4. Benefit/Cost ratio is also produced by total benefit/total cost. The result from the above way reads 4.57 as a whole, while their own manager having workplaces reads 4.82 and the agencies do l.56. Even if their own manager performing workplaces spent more cost, this system produces more benefit than the agencies management. 5. The B/C ratio for medical organization such as local clinic, health care center and pharmacy shows more than or equal to at the workplaces controlled by the agencies. It is inferred that benefit would be much less than the cost used, with so being inefficient. 6. It is assumed that the efficiency ratio of health education is equal to reduction rate of workers medical organization visit. Estimated reduction rate 5%, 10%, 15%, show that the efficiency ratio of health education have an effect on producing benefits. It is estimated that more benefit can be produced if more qualitative education will be provided for enhancing health care efficiency. 7. Results of this study cannot be generalized because there are large scale of deviation in case of workplaces with less than 300 full time workers, but B/C ratio reads 2.69 as a whole and 3.25 at workplaces with their own health care manager are higher than 1.63 at the workplaces manged by the agencies. Finally, all the benefit concerning health care services could not be quantified, measured and shown on the value of money. This is a reason that a considerable part of benefits are so underestimated. This is also thought that measurement tools should be developed for measuring benefits of health care services with a comprehensive quantification. in the future. It is also expected that efficiency of occupational health care services should be investigated using cost-effectiveness analysis.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.5-15
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• 2001

For estimating paddy field area with Landsat TM images, two dates, May 31, 1991 (transplanting stage) and August 19, 1991 (heading stage) were selected by the data analysis of digital numbers considering rice cropping calendar. Four different estimating methods (1) rule-based classification method, (2) supervised classification(maximum likelihood), (3) unsupervised classification (ISODATA, No. of class:15), (4) unsupervised classification (ISODATA, No. of class:20) were examined. Paddy field area was estimated to 7291.19 ha by non-classification method. In comparison with topographical map (1:25,000), accuracy far paddy field area was 92%. A new image stacked by 10 layers, Landsat TM band 3,4,5, RVI, and wetness in May 31,1991 and August 19,1991 was made to estimate paddy field area by both supervised and unsupervised classification method. Paddy field was classified to 9100.98 ha by supervised classification. Error matrix showed 97.2% overall accuracy far training samples. Accuracy compared with topographical map was 95%. Unsupervised classifications by ISODATA using principal axis. Paddy field area by two different classification number of criteria were 6663.60 ha and 5704.56 ha and accuracy compared with topographical map was 87% and 82%. Irrespective of the estimating methods, paddy fields were discriminated very well by using two-date Landsat TM images in May 31,1991 (transplanting stage) and August 19,1991 (heading stage). Among estimation methods, rule-based classification method was the easiest to analyze and fast to process.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Korean Journal of Heritage: History & Science
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• v.54 no.4
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• pp.90-111
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• 2021

Seojangdaeyajodo is a drawing of Jangyongyoung's military night training on February 12 (lunar leap month), 1795. Focusing on the Seojangdaeyajodo, the positions and roles of the low-ranking soldier belonging to Jangyongyoung, and the composition and characteristics of military uniforms for each role were examined. The results ascertained by the historical research on the military uniforms are as follows. Deungronggun, noeja, sunryeongsu and daegisu who were placed in front of the king's Seojangdae were the low-ranking soldiers belonging to Jangyongyoung. The soldiers who escorted the king around Seojangdae were lowranking soldiers belonging to Jangyongyoung. The military uniform of the deungronggun was consisted of a jeolrip, a black heopsu, red gweja, indigo jeondae, white haengjeon and black shoes. The low-ranking soldier's heopsu suggested that it could also be a sochangui. He carried a sword and a red lantern. Noeja were divided into a sinjeonsu and a jujangsu. The military uniform of the noeja was consisted of a Jujeolrip, a black heopsu, red gweja, indigo jeondae, white haengjeon, and black shoes. Sunryeongsu were divided into a sinsigisu and a younggisu. The military uniform of the sunryeongsu was consisted of a jeongeon, a black heopsu, red gweja, indigo jeondae white haengjeon and black shoes. He carried a sword and a red lantern. The military uniform of the daegisu was consisted of a jeongeon, a black heopsu, blue gweja, indigo jeondae, white haengjeon and black shoes. He carried a sword and a flag. The soldiers surrounding Seojangdae and the seongjeonggun defending the fortress were the Chogun. The military uniform of the chogun was consisted of a jeolrip, a black heopsu, houi, indigo jeondae, white haengjeon and straw shoes. Houi was applying the five directional colors: the east is blue, the west is white, the south is red, and the north is black. He carried a sword and a gun. It was presented as an illustration of costumes that could produce contents by reflecting on these historical results. The basic principle of the illustration was to present the standards for 3D content production or actual production. Samples of form, color, and material according to the times and status were presented. The front, the side, and the back of each costume and the feature were presented, and the colors were presented in RGB and CMYK.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.44-48
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• 2019

Objectives: Insomnia is one of the most prevalent sleep disorders. Recent studies suggest that cognitive and physical arousal play an important role in the generation of primary insomnia. Studies have also shown that information processing disorders due to cortical hyperactivity might interfere with normal sleep onset and sleep continuity. Therefore, focusing on central nervous system arousal and normalizing the information process have become current topics of interest. It has been well known that neurofeedback can reduce the brain hyperarousal by modulating patients' brain waves during a sequence of behavior therapy. The purpose of this study was to investigate effects of neurofeedback therapy on electroencephalography (EEG) characteristics in patients with primary insomnia. Methods: Thirteen subjects who met the criteria for an insomnia diagnosis and 14 control subjects who were matched on sex and age were included. Neurofeedback and sham treatments were performed in a random order for 30 minutes, respectively. EEG spectral power analyses were performed to quantify effects of the neurofeedback therapy on brain wave forms. Results: In patients with primary insomnia, relative spectral theta and sigma power during a therapeutic neurofeedback session were significantly lower than during a sham session ($13.9{\pm}2.6$ vs. $12.2{\pm}3.8$ and $3.6{\pm}0.9$ vs. $3.2{\pm}1.0$ in %, respectively; p < 0.05). There were no statistically significant changes in other EEG spectral bands. Conclusion: For the first time in Korea, EEG spectral power in the theta band was found to increase when a neurofeedback session was applied to patients with insomnia. This outcome might provide some insight into new interventions for improving sleep onset. However, the treatment response of insomniacs was not precisely evaluated due to limitations of the current pilot study, which requires follow-up studies with larger samples in the future.

• Korean Journal of Heritage: History & Science
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• v.54 no.2
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• pp.78-97
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• 2021

Seojangdaeyajodo is a drawing of military night training on February 12th (lunar leap month), 1795. Focusing on the Seojangdaeyajodo, the characteristics and of the costumes worn by various types of officials were examined. There were 34 officials located near King Jeongjo in and around Seojangdae, with 27 Dangsanggwan and 7 Danghagwan. They wore three types of costumes, including armor, yungbok, and military uniforms. All of the twelve armor wearers and the five officials wearing yungbok were dangsanggwan, and the military uniform wearers included eleven dangsanggwan and six danghagwan. For the shape of the armor, the armor relics of General Yeoban, suitable for riding horses, and the armor painting of Muyedobotongji were referenced, and the composition of the armor was based on practicality. The armor consists of a helmet, a suit of armor, a neck guard, armpit guards, arm guards, and a crotch guard. The color of the armor was red and green, which are the most frequently used colors in Seojangdaeyajodo. The composition of yungbok was jurip, navy cheollik, red gwangdahoe, socks made of leather, and suhwaja. The composition of the military uniform was a lined jeolrip, dongdari, jeonbok, yodae, jeondae, and suhwaja. There were differences in the fabrics used in dangsanggwan and danghagwan military uniforms. Dangsanggwan used fabric with depictions of clouds and jewels, and danghagwan used unpatterned fabric. Moreover, jade, gold, and silver were used for detailed ornamental materials in dangsanggwan. The weapons included bows and a bow case, a sword, a rattan stick, wrist straps, and a ggakji. In the records of the King Jeongjo period, various colored heopsu were mentioned; the colors of the dongdari and jeonbok of dangsanggwan and danghagwan were referenced in various colors. It was presented as an illustration of costumes that could be used to produce objects accurately reflecting the above historical results. The basic principle of the illustration was to present the modeling standards for 3D content production. Samples of form, color, and material of the corresponding times and statuses were presented. The front, the side, and the back of each costume and its accessories were presented, and the colors were presented in RGB and CMYK.