Earticle

Buildings are equipped with fire compartments to prevent the spread of fire. However, the fire compartment regulations in South Korea are simplistic and standardized, and the exception and mitigation also have ambiguous criteria, requiring adaptations to accommodate changes in architectures. Therefore, the purpose of this study is to examine the contents related to exception and mitigation for fire compartments in South Korea and propose to make up for the limitations. To identify limitations, this study investigates how fire compartments are implemented in South Korea and analyze international cases to compare fire compartment regulations. The comparative analysis reveals that domestic exception and mitigation have ambiguous spatial criteria, and international cases solve this issue by specifying regulations based on specific uses. Consequently, this study proposes the need to formulate and detail regulations of exception and mitigation considering both building use type and space characteristics, because of fire hazard levels changed by building use type.

Oriental medicine statistics are essential in research planning, research evaluation, and policy decision based on objective data. However, integrated administration of such statistics is not presently possible in the oriental medicine field, which has been slow in incorporating information communication technology. In an effort to address this problem, the Korea Institute of Oriental Medicine (KIOM) developed an oriental medicine statistical system in 2009, and the system has been offered in the traditional medicine information portal of OASIS. However, according to a 2010 survey targeting OASIS users, those surveys reported that needs for a system where various statistical data can be extracted via an interactive approach to multidimensional data. As a result of an analysis of the functions of the existing system, it was found that it is necessary to array and arithmetically analyze Stats Value, Drill Up & Drill Down, and Pivot. To this end, the existing DB schema should be redesigned. Based on our analysis result, we redesigned the database into a structure that is applicable to the reverse pivot algorithm. We used J2EE/JSP and a Flex framework to design and develop an oriental medicine statistical system that can provide multidimensional statistical data. Considering that the improved oriental medicine statistical system is planned to be offered by OASIS of KIOM, utilization and value of oriental medicine statistical data are expected to be enhanced.

Despite advancements in aviation technology, pilot-related human errors continue to contribute to accidents. This study examines flight crew safety behavior using both 5-point Likert-scale responses and subjective evaluations converted into fuzzy numbers. Seven predictors-management commitment, colleague commitment, organizational support, staff and equipment, collaboration and involvement, flight quality assurance, and just culture-were analyzed through correlation analysis, SEM, and path analysis with crisp data. While SEM showed excellent model fit, several predictors were not significant in the path analysis. To address these limitations, fuzzy statistical analysis was applied. Subjective single-item responses were transformed into triangular fuzzy numbers, and fuzzy regression using Lasso and least absolute deviation revealed significant relationships not detected in crisp data. Differences by airline type and flight experience were also identified, indicating lower fairness perception among low-cost carrier pilots and varying cooperative behavior depending on flight hours. Overall, the study demonstrates that combining crisp and fuzzy methods provides a more comprehensive understanding of pilots' safety behavior and offers useful implications for aviation safety management.

Training neural networks with softmax outputs requires assigning target values to output nodes. Due to its simplicity, we often use one-hot encoding, which adopts "one" or "zero" as the desired output values. However, when training neural networks to minimize the cross-entropy error function between the desired and actual output node values, overfitting of neural networks to training samples becomes a significant issue. A probabilistic target encoding has been proposed to mitigate the overfitting. In this paper, we derive the optimal solutions for output nodes that minimize the cross-entropy error function using the probabilistic target encoding. In the extreme case of the probabilistic target encoding, the analysis corresponds to the cross-entropy error function with one-hot encoding. The statistical analyses conducted to derive the optimal solutions provide considerable insights, including the interval of target values for the Bayes classifier.

The aim of this study is to develop the surface roughness prediction models, with the aid of statistical methods, for hastelloy C-22HS when machined by PVD and CVD coated carbide cutting tools under various cutting conditions. These prediction models were then compared with the results obtained experimentally. By using response surface method (RSM), first order models were developed with 95 % confidence level. The surface roughness models were developed in terms of cutting speed, feed rate and axial depth using RSM as a tool of design of experiment. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. It was found that the feed rate, cutting speed and axial depth played a major role in determining the surface roughness. On the other hand, the surface roughness increases with a reduction in cutting speed. PVD coated cutting tool performs better than CVD when machining hastelloy C-22HS. It was observed that most of the chips from the PVD cutting tool were in the form of discontinuous chip while CVD cutting tool produced continuous chips.

The conventional SEA model considers only the resonant part of the structural response to an acoustic excitation. Therefore, this study investigates non-resonant responses of isotropic and orthotropic plates to acoustically induced vibrations in a reverberation chamber. A modified SEA model is introduced to predict the non-resonant plate response. The estimated non-resonant and resonant responses are then compared with those obtained experimentally, and good agreement is observed for isotropic and orthotropic plates. For an isotropic plate with a small dissipation loss factor, when the non-resonant part is ignored, the estimated response can lead to significant errors at frequencies near and above the critical frequency, while large errors may occur at frequencies below the critical frequency for an orthotropic plate with a high dissipation loss factor. The experimental study indicates that the non-resonant response component should be included in the estimated responses to enhance predictive accuracy.

Malfunctions or critical fatigue problems often occur in mistuned periodic structural systems since their vibration responses may become much larger than those of perfectly tuned periodic systems. These are called vibration localization phenomena and it is of great importance to accurately predict the localization phenomena for safe and reliable designs of the periodic structural systems. In this study, a simple discrete system which represents periodic structural systems is employed to analyze the vibration localization phenomena. The statistical effects of mistuning, stiffness coupling, and damping on the vibration localization phenomena are investigated through Monte Carlo simulation. It is found that the probability of vibration localization was significantly influenced by the statistical properties except the standard deviation of coupling stiffness.

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. Prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

Objective To investigate trends of the research designs and statistical methods in the Annals of Rehabilitation Medicine (ARM) published from 2005 to 2015 through a comparison of articles with the Archives of Physical Medicine and Rehabilitation (APMR).Methods The authors reviewed all articles published in ARM and APMR for the years 2005 and 2015 in order to determine their research designs as well as their statistical methods used in each article.Results In ARM, randomized controlled trials increased from 4.5% in 2005 to 6.5% in 2015. In APMR, randomized controlled trials increased from 8.1% in 2005 to 14.0% in 2015, meta-analyses increased to 5.3%, and systematic reviews increased to 6%. The number of studies using statistical methods increased in ARM from 1.9 to 2.6 per article and in APMR, from 2.7 to 3.1. Use of advanced methods in ARM also showed an increase from 2005 to 2015.Conclusion This study concludes that there is a trend of increased awareness and attempts to use varied research approaches in ARM articles. There should also be more in-depth discussions and opportunities for researchers to share their experiences regarding statistical methods in the clinical field.

Regularities in the learning environment allow us to make predictions and guide behavior. Growing evidence of location probability learning (LPL) demonstrates that the statistical regularity of target locations affects spatial attention allocation. However, existing studies on LPL mostly focus on learning in adults. To achieve a comprehensive understanding of the mechanism of LPL, we investigated the effect of target location probability on visual search in children aged 5 to 9 years compared to adults. Both children and adults responded faster when the target appeared in the high probability “rich” quadrant than in the low probability “sparse” quadrants of the search space. Importantly, the magnitude of the bias was constant across participants of various ages and not dependent on individual differences in executive functions. These results provide novel evidence that implicit statistical learning of target locations occurs early in development and remains stable until early adulthood and this is a distinct developmental pattern from learning of explicit goal-driven spatial attention.

To eliminate ambiguities in the existing methods to simplify Chinese pronunciation learning, we propose a model that can predict the pronunciation of Chinese characters automatically. The proposed model relies on a statistical machine translation (SMT) framework. In particular, we consider the components of Chinese characters as the basic unit and consider the pronunciation prediction as a machine translation procedure (the component sequence as a source sentence, the pronunciation, pinyin, as a target sentence). In addition to traditional features such as the bidirectional word translation and the n-gram language model, we also implement a component similarity feature to overcome some typos during practical use. We incorporate these features into a log-linear model. The experimental results show that our approach significantly outperforms other baseline models.

Objectives: Ozone ($O_3$) advisories are issued by provincial/prefectural and city governments in Korea and Japan when oxidant concentrations exceed the criteria of the related country. Advisories issued only after exposure to high $O_3$ concentrations cannot be considered ideal measures. Forecasts of $O_3$ would be more beneficial to citizens' health and daily life than real-time advisories. The present study was undertaken to present a simplified forecasting model that can predict surface $O_3$ concentrations for the afternoon of the day of the forecast. Methods: For the construction of a simple and practical model, a multivariate regression model was applied. The monitored data on gases and climate variables from Japan's air quality networks that were recorded over nearly one year starting from April 2016 were applied as the subject for our model. Results: A well-known inverse correlation between $NO_2$ and $O_3$ was confirmed by the monitored data for Iksan, Korea and Fukuoka, Japan. Typical time fluctuations for $O_3$ and $NO_x$ were also found. Our model suggests that insolation is the most influential factor in determining the concentration of $O_3$. $CH_4$ also plays a major role in our model. It was possible to visually check for the fit of a theoretical distribution to the observed data by examining the probability-probability (P-P) scatter plot. The goodness of fit of the model in this study was also successfully validated through a comparison (r=0.8, p<0.05) of the measured and predicted $O_3$ concentrations. Conclusions: The advantage of our model is that it is capable of immediate forecasting of surface $O_3$ for the afternoon of the day from the routinely measured values of the precursor and meteorological parameters. Although a comparison to other approaches for $O_3$ forecasting was not carried out, the model suggested in this study would be very helpful for the citizens of Korea and Japan, especially during the $O_3$ season from May to June.

근래에 불어 닥친 전 세계적 글로벌 금융위기와 유럽의 재정위기는 국가가 책임질 가능성이 있는 채무 (부채) 수준을 보다 정확하게 파악하는 것이 중요함을 일깨워준 계기가 되었으며, 이 러한 영향으로 국가 재정통계 작성의 국제 기준이 빠르게 변화되어 가고 있다. GFS 2012에서 보듯이 변화의 기본 방향은 재정활동의 영역을 보다 포괄적으로 정의함으로써 잠재적인 공공부문의 채무(부채) 부담까지 파악하려는 데 있다. GFS 2012는 UN이 발간하는 SNA 2008과 IMF가 발간하는 BPM 6th edition (BPM6)에서 사용되어지는 공공부문 분류에 동일한 개념을 사용하기에 GFS 2012는 이러한 국제적인 추세를 반영하여 GFS 2001 보다 제도단위와 그 하위부문 구분에 대한 상세한 분류를 추가함은 물론 부문분류의 실제적 적용을 통하여 급변하고 복잡한 환경 하에서 우후죽순처럼 발생하는 준기업, 구조조정기관, 특수목적기관, 합작벤처, 감채기금, 연금제도, 강제적립기금, 국부펀드, 시장규제기관, 개발 및 인프라 기업 또는 기관 등에 대한 분류 원칙을 설명하여 향후 이러한 실체들의 분류를 더욱 간편하게 하였다는 점에서 그 특징이 있다 할 것이다. 우리나라의 경우에도 통계의 국제적인 비교를 위해서나 여태까지 명확하지 않았던 제도단위의 명확한 분류구분을 위하여 GFS 2012 체제로 넘어가야 하는 것이 필연적이며 이를 위하여는 GFS 2012에서 제시하는 분류구분 정의, 원칙, 절차 등에 대한 상세한 정보와 분석이 필요할 것이다. 본 연구에서는 무엇보다도 GFS 2001에 없고 GFS 2012에는 존재하는 공공부문 분류에 관한 의사결정도(decision tree)를 설명하여, 향후 민간부문과 공공부문의 명확한 분류는 물론 공공부문의 산하부문 (일반정부와 공기업)에 대한 더 상세한 분류를 가능케 하는 지침을 제시해 준다. 또한 부문의 실제적 적용을 위한 부문분류원칙 (practical application of sector classification principles)도 복잡다기하게 변하는 공공부문의 정확한 분류에 큰 도움이 될 것으로 기대된다.

The primary purpose of this article is to introduce definitions and classification of public sector and institutional units appearing in the newly published 2012 Government Finance Statistical Manual (GFSM) by IMF. Compared to 2001 GFSM, several important changes have been made with the 2012 edition. Among which GFSM 2012 clearly defined public sector and its subsectors. To facilitate such classification, it introduced decision tree through which it classifies subsectors of public sectors and institutional units quite clearly. On top of that, the GFSM 2012 newly introduced practical application of sector and classification principles in classifying public and quasi-public sectors. With the introduction of definitions and classification of public sector and institutional units. this article also aims to provide policy implications on how these changes would affect government size including public debt.

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.

Copy?Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out? only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).