• Journal of Oral Medicine and Pain
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• v.30 no.4
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• pp.433-446
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• 2005

The purpose of this study was to investigate the sociopsychological factors which might influence the course of the temporomandibular disorders and to develop models for prediction of treatment outcome related to pain, dysfunction and sound. For this study, 268 patients with temporomandibular disorders were selected from the patients presented to department of Oral Medicine, Wonkwang university dental hospital. Chief complaints of these subjects were largely grouped into three categories such as pain, dysfunction and sound, and 10 cm visual analogue scale(VAS) was used to record the state of the three complaints every visit and Treatment Index(VAS TI) was calculated from VAS. All the forty-two items obtained from clinical examination or questionnaire were statistically processed with $SPSS^{(R)}$ windows. The results of this study were as follows: 1. The items showed a difference between male and female subjects were maximum mouth opening, palpation score, jaw jerk during mandibular movement, and sleep disturbance. Among questionnaires such as SRRS, HAD scale and PSQI, the HAD scales showed the most highly significant correlation with the each scale item of the SCL-90R. 2. Among the groups classified by VAS TI, the group with the lowest VAS TI showed the highest VAS score in the start of treatment but showed the lowest score in the end of treatment, without respect to the type of chief complaint. From these results, it is assumed that the active treatment duration for the subjects with lower VAS score in the first visit would be longer than the subjects with higher score with the somewhat poor treatment outcome. 3. With regard to all the three complaints, the items showing significant effect in the model for prediction of treatment outcome were from questionnaire, except one item, maximum mouth opening, which suggest that the sociopsychological factors would be strongly related to development and progress of the symptoms.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Geotechnical Engineering
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• v.12 no.4
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• pp.157-178
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• 1996

Current design methods for reinforced earth structures take no account of the magnitude of the strains induced in the tensile members as these are invariably manufactured from high modulus materials, such as steel, where straits are unlikely to be significant. With fabrics, however, large strains may frequently be induced and it is important to determine these to enable the stability of the structure to be assessed. In the present paper internal design method of analysis relating to the use of fabric reinforcements in reinforced earth structures for both stress and strain considerations is presented. For the internal stability analysis against rupture and pullout of the fabric reinforcements, a strain compatibility analysis procedure that considers the effects of reinforcement stiffness, relative movement between the soil and reinforcements, and compaction-induced stresses as studied by Ehrlich 8l Mitchell is used. I Bowever, the soil-reinforcement interaction is modeled by relating nonlinear elastic soil behavior to nonlinear response of the reinforcement. The soil constitutive model used is a modified vertsion of the hyperbolic soil model and compaction stress model proposed by Duncan et at., and iterative step-loading approach is used to take nonlinear soil behavior into consideration. The effects of seepage pressures are also dealt with in the proposed method of analy For purposes of assessing the strain behavior oi the fabric reinforcements, nonlinear model of hyperbolic form describing the load-extension relation of fabrics is employed. A procedure for specifying the strength characteristics of paraweb polyester fibre multicord, needle punched non-woven geotHxtile and knitted polyester geogrid is also described which may provide a more convenient procedure for incorporating the fablic properties into the prediction of fabric deformations. An attempt to define improvement in bond-linkage at the interconnecting nodes of the fabric reinforced earth stracture due to the confining stress is further made. The proposed method of analysis has been applied to estimate the maximum tensions, deformations and strains of the fabric reinforcements. The results are then compared with those of finite element analysis and experimental tests, and show in general good agreements indicating the effectiveness of the proposed method of analysis. Analytical parametric studies are also carried out to investigate the effects of relative soil-fabric reinforcement stiffness, locked-in stresses, compaction load and seepage pressures on the magnitude and variation of the fabric deformations.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Fire Science and Engineering
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• v.22 no.3
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• pp.252-257
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• 2008

With the rapid exuberant growth of the forest, the number and size of forest fires and the costs of wildland fires have increased. The flame spread rate of forest fires is depending on the environmental variables like the wind velocity, moisture of grassland, etc. If we know the effects of the environmental variables on the fire growth, it is useful for wildland fiIre suppression. But analysis of the spread rate of wildland fire for these effects have not been established. In this study, the effects of wind velocity and height of grassland fuel have been investigated using the WFDS which is developed at NIST for prediction of the spread of wildland fires. The results showed that the relation between the height of the fuel and the spread rate of the head fires is, and the spread rates related to the wind velocity are predicted 17% less than the experimental results of Australia. When the wind velocity is over 7.5m/s, the concentration of pyrolyzed gas phase fuel is getting low due to fast movement of pyrolyzed gas, the flame spread rate becomes slow.

• Clean Technology
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• v.26 no.3
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• pp.228-236
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• 2020

Korea has an economy based on manufacturing industrial fields, which produce high amounts of hazardous wastes, in spite of few landfill candidates, and a significant concern for fine airborne particulates; therefore, traditional waste management is difficult to apply in this country. Moreover, waste collection and accumulation have recently been intensified by the waste import prohibitions or regulations in developing nations, the universalization of delivery services in Korea, and the global COVID-19 crisis. This study thus presents a domestic waste management strategy that aims to address the recent issues on waste. The contents of the strategy as the main results of the study include the (1) improvement of the compatibility of the classification codes between the domestic hazardous waste and the international ones such as those of the Basel Convention; (2) consideration of the mixed hazard indices to represent toxicity from low-content components such as rare earth metals often contained in electrical and electronic equipment waste; (3) management application based on risks throughout the life cycles of waste; (4) establishment of detailed material flow information of waste by integrating the Albaro system, Pollutant Release and Transfer Register (PRTR) system, and online trade databases; (5) real-time monitoring and prediction of the waste movement or discharge using positional sensors and geographic information systems, among others; and (6) selection and implementation of optimal treatment or recycling practices through Life Cycle Assessment (LCA) and clean technologies.

• The korean journal of orthodontics
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• v.27 no.6 s.65
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• pp.943-954
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• 1997

Forces needed for orthodontic tooth movement are obtained from various appliances such as orthodontic wires or elastic rubber. Orthodontic elastic rubber is widely used clinically, but permanent deformation and force decay may occur from the environmental changes, time of clinical use and the extent of the stretch, making the Prediction of force being applied difficult. The Present study examined and compared the changes in residual force between three brands of elastomeric chains (Ormco Generation II Power Chains ; brand A, RMO : Energy-Chain ; brand B, Unitek : AlastiK ; brand C) under various environmental conditions, amount of initial force, types of elastomer and the rate of extension. The characteristic physical properies of the elastomeric chains were as follows. 1. In all three brands, the residual force ratio was largest when the chains were stored in air, with no difference between water and saliva. 2. In all three brands, after 24 hours, there was no statistical difference in residual force ratio according to the initial force level. 3. In Brand A and B, the presence of filament had no correlation with the residual force ratio. In Brand C force decay was more severe when the chain contained filament. 4. In each brand, rate of extension had no effect on residual force ratio. 5. Brand B showed relatively higher residual force ratio compared to other brands.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.128-148
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• 2019

Massive environmental monitoring has been conducted continuously since the Fukushima Daiichi Nuclear Power accident in March of 2011 by different monitoring methods that have different features together with migration studies of radiocesium in diverse environments. These results have clarified the characteristics of radiological environments and their temporal change around the Fukushima site. At three months after the accident, multiple radionuclides including radiostrontium and plutonium were detected in many locations; and it was confirmed that radiocesium was most important from the viewpoint of long-term exposure. Radiation levels around the Fukushima site have decreased greatly over time. The decreasing trend was found to change variously according to local conditions. The air dose rates in environments related to human living have decreased faster than expected from radioactive decay by a factor of 2-3 on average; those in pure forest have decreased more closely to physical decay. The main causes of air dose rate reduction were judged to be radioactive decay, movement of radiocesium in vertical and horizontal directions, and decontamination. Land-use categories and human activities have significantly affected the reduction tendency. Difference in the air dose rate reduction trends can be explained qualitatively according to the knowledge obtained in radiocesium migration studies; whereas, the quantitative explanation for individual sites is an important future challenge. The ecological half-lives of air dose rates have been evaluated by several researchers, and a short-term half-life within 1 year was commonly observed in the studies. An empirical model for predicting air dose rate distribution was developed based on statistical analysis of an extensive car-borne survey dataset, which enabled the prediction with confidence intervals. Different types of contamination maps were integrated to better quantify the spatial data. The obtained data were used for extended studies such as for identifying the main reactor that caused the contamination of arbitrary regions and developing standard procedures for environmental measurement and sampling. Annual external exposure doses for residents who intended to return to their homes were estimated as within a few millisieverts. Different forms of environmental data and knowledge have been provided for wide spectrum of people. Diverse aspects of lessons learned from the Fukushima accident, including practical ones, must be passed on to future generations.

• Spatial Information Research
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• v.20 no.1
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• pp.9-18
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• 2012

South Korea has many landslides caused by heavy rains during summer time recently and the landslides continue to cause damages in many places. These landslides occur repeatedly each year, and the frequency of landslides is expected to increase in the coming future due to dramatic global climate change. In Korea, 81.5% of the population is living in urban areas and about 1,055 million people are living in Seoul. In 2011, the landslide that occurred in Seocho-dong killed 18 people and about 9% of Seoul's area is under the same land conditions as Seocho-dong. Even the size of landslide occurred in a city is small, but it is more likely to cause a big disaster because of a greater population density in the city. So far, the effort has been made to identify landslide vulnerability and causes, but now, the new dem and arises for the prediction study about the areal extent of disaster area in case of landslides. In this study, the diffusion model of the landslide disaster area was established based on Cellular Automata(CA) to analyze the physical diffusion forms of landslide. This study compared the accuracy with the Seocho-dong landslide case, which occurred in July 2011, applying the SCIDDICA model and the CAESAR model. The SCIDDICA model involves the following variables, such as the movement rules and the topographical obstacles, and the CAESAR model is also applied to this process to simulate the changes of deposition and erosion.