• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.520-530
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• 2021

Objectives: This study aimed to identify the effects of music therapy on cognitive function, agitation, anxiety and depression in the elderly with dementia. Method: A comprehensive literature search was performed on PubMed, EMBASE, Cochrane Library, CINAHL, Web of Science, Google scholar and PsycINFO, for the period 2010 to 2019. In the meta-analysis, the standardized mean difference (Hedges' g) and 95% confidence interval were calculated as summary measure, and the random effect model and inverse variance method were applied using the RevMan 5.4 program. A total of 13 studies were included; all were determined to be acceptable, based on the Cochrane collaboration's tool for assessing risk of bias. Results: The effect size (Hedges' g) was 0.31 (95% CI: -0.02, 0.65) for cognition and -0.03 (95% CI: -0.17, 0.11) for agitation behavior as the primary outcomes, and 0.61 (95% CI: -1.17, -0.05) for anxiety and -0.44(95% CI: -0.88, 0.00) for depression as the secondary outcomes. Subgroup analysis by type of music intervention revealed that combined music therapy has a significantly increasing beneficial effect on cognition of dementia patients (g=0.45[95% CI: 0.03, 0.87]). Conclusion: Music therapy was determined to exert beneficial effects in reducing anxiety and depression, and combined music therapy demonstrated improved cognitive functions in elderly patients with dementia.

• Journal of Preventive Medicine and Public Health
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• v.33 no.3
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• pp.343-348
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• 2000

Objectives : This study was conducted to integrate the results of studies assessing the association between chronic noise exposure and blood pressure. Methods : Using a MEDLINE search with noise exposure, blood pressure and hypertension as key words, we retrieved articles from the literature that were published from 1980 to December 1999. The criteria for quality evaluation were as follows: 1) the study subjects must have been workers employed at a high noise level area 2) The paper should use average and cumulative noise exposure as method for exposure evaluation. 3) Blood pressure in each article should be reported in a continuous scale Among the 77 retrieved articles, six studies were selected for quantitative meta-analysis. Before the integration of the regression coefficients for the association between blood pressure and noise level, homogeneity tests were conducted. Results : All studies were a cross-sectional design and the study subjects were industrial workers. Five papers used a time-weighted average for noise exposure and only one paper calculated the cumulative noise exposure level. The measurement of blood pressure in the majority of studios were accomplished in a resting stale, and used an average of two or more readings. The homogeneity of studies was rejected in a fixed effect model, so we used the results in a random effect model. The results of the quantitative meta-analysis, the weighted regression coefficient of noise associated with systolic blood pressure and diastolic blood pressure were 0.05 (95% confidence interval [CI]: -0.03, 0.13) and 0.06 (95% CI: -0.01, 0.13), respectively. Conclusions : Our results suggested that chronic exposure to industrial noise does not cause elevated blood pressure.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.126-131
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• 2015

As flash-based storage systems have been actively employed in large-scale servers and data centers, reliability has become an indispensable element. One promising technique for enhancing reliability is static wear-leveling, which distributes erase operations evenly among blocks so that the lifespan of storage systems can be prolonged. However, increasing the capacity makes the processing overhead of this technique non-trivial, mainly due to searching for blocks whose erase count would be minimum (or maximum) among all blocks. To reduce this overhead, we introduce a new randomized block selection method in static wear-leveling. Specifically, without exhaustive search, it chooses n blocks randomly and selects the maximal/minimal erased blocks among the chosen set. Our experimental results revealed that, when n is 2, the wear-leveling effects can be obtained, while for n beyond 4, the effect is close to that obtained from traditional static wear-leveling. For quantitative evaluation of the processing overhead, the scheme was actually implemented on an FPGA board, and overhead reduction of more than 3 times was observed. This implies that the proposed scheme performs as effectively as the traditional static wear-leveling while reducing overhead.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.2
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• pp.193-201
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• 2010

Purpose : This study achieved to search the effect of the circuit exercise and conventional exercise on walking ability(walking speed, endurance, dynamic balance, speed, endurance and pedestrian crossing) in chronic stroke. Methods : Since is diagnosed by stroke, to 30 chronic stroke patients who more than 1 year past the 15 circuit exercise group, the 15 conventional exercise group random the circuit exercise group applied circuit exercise 3th 8 weeks each week after neurological treatment because assigning and the conventional exercise group executed round trip walk exercise in parallel bar 3th 8 weeks each week after neurological treatment. The data of 25 patients who complete experimental course were statistically analysed. Results : The results of this dissertation were as following : 1) There were significantly increased after experimental of 10 meter walk test, 6 minutes walk test and Timed "Up and Go" test in circuit exercise group (p<.001). 2) There were significantly increased after experimental of 2, 4 and 6 lane road crossing mobility in Walking circuit exercise group(p<.01). 3) There were significantly differences after experimental of 10 meter walk test, 6 minutes walk test and Timed "Up and Go" test change quantity between circuit exercise group and conventional exercise group(p<.05). 4) There were correlations were found between the TUG test and 2, 4 and 6 lane road (2 lane road; r=.463, p<.01., 4 lane road; r=515, p<.01., 6lane road; r=.710, p<.01), and there were correlations were found between the 10 meter walk test and 6 minutes walk test(r=.595, p<.01), TUG test(r=.662, p<.01) and 6 lane road(r=.527, p<.01). Conclusion : Even if improvement of walk function through training consists in room, transfer of actuality pedestrian crossing is no change outside the room. Because it is much variable of the weather, seasonal factor, temperature, pedestrian number, state of underneath etc. outside the room. Then, in room after direction promotion of walk function to be promotion of walk function in actuality life and need development of connectable training method consider.

• Journal of health informatics and statistics
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• v.43 no.4
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• pp.290-299
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• 2018

Objectives: This study aimed to evaluate the change in weight and heart rate associated with the use of phentermine through meta-analysis based on the published literatures. Methods: Eight electronic databases, PubMed, EMBASE, Cochrane library, and five domestic databases were used to search the literature. Randomized controlled trials that evaluated the change in weight and heart rate with the use of phentermine compared with placebo were included in this study. The fixed-effect model weighted by the Mantel-Haenszel method was used in the meta-analysis, and the random-effects model was used when heterogeneity was present. Results: We included 12 studies comprising 677 patients. The change in weight observed with the use of phentermine (SMD = -1.37, 95% CI: -1.55, -1.19) was statistically significant compared with that observed with placebo. As per the subgroup analysis results, the change in weight by publication year, country, phentermine dosage, follow-up check was not heterogeneous. The change in heart rate observed with the use of phentermine (SMD = 0.64, 95% CI: 0.35, 0.92) was significant compared with that observed with placebo. Conclusions: Weight loss and increased heart rate were confirmed in phentermine compared with placebo.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.57-69
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• 1997

Objective：At the present time in Korea, for a considerable proportion of children and adolescent, delinquent behavior and violence has become as a way of life in their lives and a major social problem issue as well. The contributing factors to this problem were assumed to be the negative interaction between family environment and character of adolescent. The purpose of this study is to search the relationship between these constructs and juvenile delinquency. Method：Data were collected through questionnaire survey over a period of 2 months. Subjects served for this study consisted of 1,863 adolescents including 657 delinquent adolescents and 1,206 student adolescents in Korea, sampled from Korean student population and delinquent adolescent population confined in juvenile corrective institutions, using proportional stratified random sampling method. Their age ranged between 12 and 18 years. Data were analysed by IBM PC using SAS program. Statistical methods employed were Chi-square and principal component analysis. Results：The results of this study were as follows：Inconsistency by parental child rearing patterns tended to affect delinquent behavior among delinquent adolescents. On the other hand, adolescent students were consistently reared by their parent with democratic, flexible, trusting their children and reward-oriented attitudes. In comparison of both parents in the degree of influence on their children, it was revealed that paternal child rearing pattern was more influential on their children’s behaviors than maternal’s. The psychological instability of family, disharmonious parent-child relationships tended to be contributing to delinquent behavior among delinquent adolescents. Especially, It was an interesting finding that student’s mother is the higher employed than delinquent’s mother. However working mother was more prevalent in the student’ adolescents than in student adolescents in previous studies. The delinquent adolescents have more depressive trend, more complaints of psychosomatic symptoms, the higher degree of need frustration, the more maladaptive and antisocial personality pattern than student adolescents. Conclusion：Recently, many studies on association between family factor, character of adolescent and juvenile delinquent behavior have produced relatively consistent results. This study showed that family environment and character trait of adolescent also were linked with delinquent behavior such as smoking, drinking, runaway and physical assaults etc. The results of this survey may provide impetus for future speculation and study of correlation or reciprocal interaction between family factor, character trait of adolescent and delinquent behavior during adolescence and beyond.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.