• Physical Therapy Korea
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• v.10 no.1
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• pp.97-108
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• 2003

Slipping during various kinds of movement often leads to potentially dangerous incidents of falling. The purpose of this paper was to review some of the research performed in the field including such topics as rating scales for balance, kinematics and kinetics of slipping, adaptation to slippery conditions, postural and balance control, and protective movement during falling. Controlling slipping and fall injuries requires a multifaceted approach. Environmental conditions (state of floor surface, tidiness, lighting, etc), work task (walking, carrying, pushing, lifting, etc), and human behavior (anticipation of hazards, adaptation to risks, risk taking, etc) must be accounted for in the assessment of slip and fall-related risks. Future directions of research must deal with modeling of basic tribophysical, biomechanical, and postural control process involved in slipping and falling.

• The Journal of Engineering Geology
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• v.28 no.3
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• pp.379-395
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• 2018

A walking trail environment can be divided into the upper part of the trail, the trail itself, and the lower part of the trail. In this study, based on field investigations, we developed a risk index for trails by considering human/societal factors that affect each of these three trail environments. A checklist was developed for field investigations, and checklist items were scored through relative importance analysis. The relative weights of items were analyzed using the AHP (Analytic Hierarchy Process) technique, revealing that the upper environment of a trail is twice as important as the rest of the environment. The importance and score of items belonging to each environment were determined. We define the risk index as the sum of the item scores. Weights were added using data from existing investigations including landslides risk rating and designated risk steep slopes. The risk index has a maximum value of 200, and the maximum and minimum calculated scores of 335 risk sections were 159 and 64.2, respectively. As a result of comparative analysis between field observations and risk index calculations, most sections at relatively low risk had risk values less than 100, and sections with high risks or that had been the site of accident yielded scores that exceeded 140.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.23-33
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• 2015

Recently, the Ministry of Employment and Labor Management is a trend to strengthen all men death rate than the accident rate. Points reduction in the accident rate change orders related to credit rating score to +2 points in his plans as part of +1 point. In addition, according to the fancy linger RISK treatment in the event of a disaster site and fiction treatment to achieve accident-free during processing the scene interspersed with equity issues have been raised. In general disaster for the problem in the first two cases occurs when abnormal process according to the disaster site manager positions dismissal policy, each division headquarters itself, interspersed disasters performance compared to processing in accordance with the refrain, processing expenses in accordance with the composition of untreated industrial accident, costs and burdens partners FTC, there is a possibility that the issues raised, such as the Ministry of Employment and Labor. In response to domestic social practices focused on the construction site practices and prevention measures should be evaluated with respect to what.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.3
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• pp.27-40
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• 2013

Rotorcraft operating in the domestic aviation safety techniques are applied CRM training is conducted but, aircraft accidents caused by human factors has not shown a declining trend. Thus, knowledge of aviation safety and human factors for the spread of awareness of improved rotorcraft flight operations department managers to understand the complexity of nature and culture, and to perform high-risk mission helicopter pilot study of local activation and enhance safety awareness research was conducted in order to. In this study, the development direction of CRM training studies in order to identify the leading NOTECHS (Non-Technical Skills; non-technical pilot skills) of the four categories as the independent variable and the dependent variable corresponding to the resulting effect on the key variables awareness of the differences were studied. In addition, the direction and strength of the relationship were analyzed to determine the relationship of each independent variable to assess the impact on the dependent variable regression analysis was performed. Pilot training and evaluation of non-technical skills related to the teaching reflected in the CRM training and assessment must be carried out with 5 star rating scale was preferred. Therefore, to meet our country rotorcraft operating environment 'NOTECHS' aviation safety by developing training programs reflected in the educational process, implementation, and periodic training and assessment is done in future research on this analysis and feedback is done to reflect the specific performance expect.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.99-123
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• 2004

We focus in this study on incremental financing decisions and estimate a logit model for the probability a firm will choose a private placement over a public bond issue. We hypothesize that information asymmetry, financial risk, agent cost, and proprietary information may affect a firm's choice between public debt and private placements. We find that as the size of firm increases, the probability of choosing a private placement declines significantly. The age of the firm, however, is not a significant factor affecting the firm's choice between public and privately-placed bond. The coefficients on the firm's leverage and non-investment grade dummy are significantly positive, meaning firms with high financial risk and credit risk select private placements. The findings regarding agency-related variables, PER and Tobin's Q, are somewhat complex. We find significant evidence that firms with high PER prefer private placements to public bonds, suggesting that borrowers with options to engage in asset substitution or underinvestment are more likely to choose private placements. The coefficient of Tobin's Q is negative, but not significant, which weakly support the hold-up hypothesis. When we construct an interaction term on the Tobin's Q with a non-investment rating dummy, however, the Tobin's Q interaction term becomes positive and significant. Thus, high Tobin's Q firms with a speculative rating are significantly more likely to choose a private placement, regardless of the potential hold-up problems. The ratio of R&D to sales, proxy for proprietary information, is positively significant. This result can be interpreted as evidence in favor of a role for proprietary information in the debt sourcing decision process for these firms.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Korean Biological Nursing Science
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• v.20 no.3
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• pp.177-186
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• 2018

Purpose: Increasing number of older adults are receiving cancer surgeries especially for gastrointestinal cancers, which brings forth attention to age-related postoperative complication prevention. Postoperative delirium (POD) is a common complication that rises after surgical procedures involving general anesthesia, largely in the elderly population. Due to its sudden onset and fluctuating symptoms, POD often goes underdiagnosed and undertreated even though it may lead to various adverse outcomes. POD in GI cancer surgical elderly patients is poorly understood in terms of prevalence, pathophysiology, assessment, treatment and nursing management. We aimed to identify available literature and investigate study results to broaden our understanding of geriatric GI cancer POD. Methods: The search process involved six databases to identify relevant studies abided by inclusion criteria. Results: Eleven studies were selected for this review. Geriatric POD is closely related to frailty and surgical complications. Frailty increases vulnerability to surgical stress and causes cerebral changes that affect stress-regulating neurotransmitter proportions, brain blood flow, vascular density, neuron cell life and intracellular signal transductions. These conditions of frailty result in increased risks of surgical complications such as blood loss, cardiovascular events and inflammation, which all may lead to the POD. Mini Metal State Examination (MMSE), Confusion Assessment Method (CAM) and Delirium Rating Scale-revised-98 (DRS-R-98) are recommended for POD assessment to identify high-risk patients. Conclusion: The POD prevalence ranged from 8.2% to 51.0%. The multifactorial causative mechanism suggests nurses to identify highrisk elderly GI-cancer surgical patients by reviewing patient-specific factors and surgery-specific factors.

• The Journal of Information Systems
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• v.18 no.4
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.

• The Journal of Internal Korean Medicine
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• v.44 no.4
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• pp.603-620
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• 2023

Objectives: This study reviewed randomized controlled trials (RCTs) investigating the efficacy of herbal medicine on sleep disorders associated with Parkinson's disease and suggests a better research process. Methods: We searched for RCTs for herbal medicine treatments for sleep disorders related to Parkinson's disease on July 31, 2023 using eight databases (PubMed, Embase, the Cochrane library, China National Knowledge Infrastructure [CNKI], the Research Information Service System [RISS], Science ON, the Oriental Medicine Advanced Searching Integrated System [OASIS], and the Korea Citation Index [KCI]). Cochrane's risk of bias tool was used to assess the quality of the RCTs. Results: A total of 16 RCTs met all the inclusion criteria, and in most reports, the treatment group showed a significant improvement in sleep disorders compared to the control group. Total effective rate (TER), Pittsburgh Sleep Quality Index (PSQI), Unified Parkinson's Disease Rating Scale (UPDRS), TCM Symptom Score (TSS), Parkinson's Disease Sleep Scale (PDSS), etc., were used as evaluation indicators. Conclusion: Herbal medicine is a potential treatment for sleep disorders associated with Parkinson's disease. However, the selected RCTs were of poor quality, and it is necessary to perform more systematic studies.