Earticle

Readability is commonly defined as a statistical scale for measuring the ease of reading and understanding a text. In this paper, we propose that paragraph length, sentence length, and word length are applicable to readability metrics for Korean texts. A correlation between these readability metrics and text levels is statistically analyzed using correlation analysis and simple and multiple linear regression analyses based on data as found in Korean learning materials. Paragraph length and sentence length show a steady upward slope with increasingly higher learning levels, while word length has a downward sloping relationship with the elementary level only. This correlation can also be applied to materials evaluation focused on the arrangement of learning levels and chapters with respect to readability. This applicability is demonstrated by two statistical methods. First, cluster analysis is the assignment of a set of texts by level or by chapter into subsets sharing similar readability scales to evaluate materials arrangements in accordance with readability scales. Second, regression model with a breakpoint (Baayen 2008) divides a linear relation of readability scales into two models having a different slope so that we can explore a trend change of readability scales in materials.

Risks, uncertainties, and associated cost overruns are critical problems for construction projects. Cost contingency is an important funding source for these unforeseen events and is included in the base estimate to help perform financially successful projects. In order to predict more accurate contingency, many empirical models using regression analysis and artificial neural network method have been proposed and showed its viability to minimize prediction errors. However, categorical factors on contingency cannot have been treated and thus considered in these empirical models since those models are able to treat only numerical factors. This paper identified potential factors on contingency in transportation construction projects and evaluated categorical factors using the one-way ANOVA statistical method. Among factors including project work type, delivery method type, contract agreement type, bid award type, letting type, and geographical location, two factors of project work type and contract agreement type were found to be statistically important on allocating cost contingency.

A fast statistical method for Chinese unknown word detection is proposed. It is based on association measure, heuristic features and LocalMaxs algorithm. The unknown words discovered are not limited to certain word patterns. It is also effective for low frequency unknown words and sensitive to new coming texts. Experiment shows its effectiveness.

A quantified reasoning and description of the perplexity for evaluating language models by using the concept of information entropy is discussed in this article: The smaller the entropy of the language estimated by the language model is, the more precise the language model is; an interpolated model based on two (n‐1)‐gram models is better than the (n‐1)‐gram component models, but not a n‐gram model. We also explore the methods to estimating the entropy of Chinese using language models.

This paper simulated surface grinding process with statistically simulated grinding wheel topography, considering ridge formation phenomenon when grain scratch workpiece. Wheel grain is modeled as hybrid sphere and cone. Grinding wheel characteristic was evaluated with stylus by expanding the scanning region of the profilometer from a straight line to a plane. Each grain's diameter and semi-angle are assumed as normal distribution, each grain's protrusion height from wheel plane is assumed gamma distribution. So grinding wheel is simulated with grain's position randomly distributed without overlapping. Ground surface is 3-dimensionally simulated considering ridge formation of workpiece by each grain's cutting, and then surface profile and surface roughness parameters are compared with real ground workpiece.

This paper presents a method to predict the design allowable for composite laminates subjected to a tensile load with finite element analysis. The mechanical properties were obtained by conducting tensile and compressive lamina tests. The statistically-based properties were acquired using an in-house tool to calculate the design allowable. A tensile load was imposed on the FE model of the laminate, and each ply stress was compared to the design allowable of the lamina test. The load was considered as the failure load when the ply stress reached the allowable value. This load was applied to a laminate model to perform finite element analysis, and the laminate strength was calculated. The calculated strength and design allowable of the laminate were compared, and as a result, the difference between the results of analysis and test was found to be within 10%.

There are being a lot of studies for achievement of high speed Dehydration, high-strength and Lightweight of washing machine in the latest washing machine business. It is essential that strength of mechanical press-Joining (MPJ) for spin drum is improved to attain that target. MPJ of spin drum is composed of seaming and caulking process. Because Seaming process of MPJ has various design factors such as thickness, bending radius, seaming width, caulking press width and the dynamic factor such as multistage plastic working, elastic recovery, residual stress, the optimum conditions can't be easily determined. Using a design of experiment (DOE) based on the FEM (Finite Element Method), which has several advantages such as less computing, high accuracy performance and usefulness, this study was performed investigating the interaction effect between the various design factor as well as the main effect of the each design factor during drum MPJ and proposed optimum condition using center composition method among response surface derived from regression equation of simulation-based DOE.

복분자 와인의 제조를 위하여 복분자 과실 및 엑기스로부터 야생효모 Saccharomyces cerevisiae BA29를 분리 및 동정하였으며, 생화학적 특성 및 biogenic amine 생성 여부, 배양학적 특성 및 알코올 발효능과 알코올, 당, 아황산 저항성을 조사하였다. 또한 S. cerevisiae BA29의 산업적 공정 적용을 위한 균체량 증가를 위하여 통계학적 방법인 반응표면분석법을 사용하여 배양 배지 조성의 최적화를 수행하였다. 실험계획법은 중심합성계획을 사용하여 모델을 설정하였고, 산업적 공정 적용 시 비용 대비 효율성이 높은 molasses를 대체 탄소원으로 사용하여 실험을 수행하였다. 통계프로그램을 이용하여 분석한 결과 최대 균체 성장을 위한 배지 조성으로는 molasses 200 g/L, peptone 30 g/L, yeast extract 40 g/L로 예측되었으며, 이때의 최대 균체량은 20.6565 g/L로 예측되었다. 모델의 검증실험 결과 기본 배양 배지와 비교하였을 때 6.39 g/L에서 $20.9167{\pm}0.7925g/L$로 약 3.27배 증가하였다. 최종적으로 S. cerevisiae BA29를 사용하여 복분자 와인을 제조한 결과 20.33%의 알코올 생성능을 나타냈다. 이로써 복분자 과실로부터 분리한 효모 S. cerevisiae BA29를 이용한 우수한 복분자 와인 제조의 가능성을 확인하였다.

Rubus coreanus is known as Korean black raspberry, native to Korea, Japan, and China. Preliminary studies evaluating their potential for cancer treatment in mammalian test systems are ongoing. In recent years, interest has been renewed due to their high levels of anthocyanins. Anthocyanins in black raspberry are important due to their potential health benefits as dietary antioxidant, anti-inflammatory compound, and as a chemopreventive agent. In the present study, Saccharomyces cerevisiae BA29 was isolated from black raspberry fruit and fruit juice as a biogenic amine non-producing strain for manufacturing of black raspberry wine, after which we investigated its characteristics: biogenic amine-producing ability, cell growth ability, alcohol-fermentation ability, and resistance to alcohol, glucose, and sulfur dioxide. Based on preliminary experiments, we optimized culture medium compositions for improving dried cell weight of S. cerevisiae BA29 by response surface methodology (RSM) as a statistical method. Design for RSM used a central composite design, and molasses with the industrial applicability was used as a carbon source. Through statistical analysis, we obtained optimum values as follows: molasses 200 g/L, peptone 30 g/L, and yeast extract 40 g/L. For the model verification, we confirmed about 3-fold improvement of dried cell weight from 6.39 to 20.9167 g/L compared to basal yeast peptone dextrose medium. Finally, we manufactured black raspberry wine using S. cerevisiae BA29 and produced alcohol of 20.33%. In conclusion, S. cerevisiae isolated from black raspberry fruit and juices has a great potential in the fermentation of black raspberry wine.

It is difficult to have the existing structural optimization using coupled field analysis from CFD to structure analysis when the structure is influenced of fluid. Therefore in an initial model of this study after doing parameter design from the background of shape using topology optimization. and it is making a approximation formula using by the CFD-structure coupled-field analysis and design of experiment. By using this result, we conducted multi-objective optimization. We could confirm efficiency of stochastic method applicable in the scene of structure reliability design to be needed multi-objective optimization. And we presented a way of design that could overcome the time and space restriction in structural design such as the butterfly valve with the less experiment.

This study is on the estimation method of toot temperature for different tool nose radius and cutting conditions in turning. Experimental analysis has been performed in different cutting conditions such as cutting speed, feed rate, and depth of cut for the tool nose radius, 0.4R, 0.8R using SMC workpiece materials. Tool temperature is measured using a thermo-couple which is embedded in the insert tip. Using multiple linear regression method, the tool temperature can be determined as an exponential equation with cutting variables and tool nose diameters for the different tool materials. The equations determined in this study show a good correlation for the cutting conditions and can be used for a tool temperature estimation technique. The result indicates that the tool temperature decreases for increasing the tool nose radius in general. Also, nose radius hardly influences on the tool temperature compared with cutting speed, feed rate and depth of cut. This method will be useful for the estimation of tool life and temperature using limited experimental data for given cutting conditions.

Purpose: The purpose of this study was to investigate the relationship and the most influencing sub-variable of emotional labor significantly predicts to burnout and job satisfaction among clinical nurses. Methods: This descriptive study was conducted with a convenient sample 271 of clinical nurses in K providence, Korea. Surveyed data was collected from April 17, 2010 to May 14, 2010. To determine the most effective variable of emotional labor to burnout and job satisfaction, the dada were analyzed by Canonical correlation using SAS 9.1. Results: Canonical correlation analysis revealed that emotional intensity among 5 sub-domains of emotional labor made the most outstanding contribution in predicting to burnout. Emotional deep acting was the most outstanding contribution in predicting to job satisfaction of clinical nurses. Conclusion: The results suggest that reducing emotional intensity should be included in structured burnout management. Especially the positive reinforcement of emotional deep acting for clinical nurses is important for increasing job satisfaction.

무선 센서 네트워크(Wireless Sensor Network: WSN)에서 센서들을 클러스터(Cluster) 단위로 그룹화하고 각 클러스터에서 통신 중계 역할을 하는 클러스터 헤드(Cluster Head: CH)를 선출하는 클러스터링 알고리즘이 에너지 보존과 중계 효율을 위해 제안되어 왔다. 한편, 오염된 노드(Compromised Node), 즉 내부공격자를 통해 CH 선출과정에 개입하여 네트워크 운영에 치명적인 영향을 미치는 공격기법들이 등장하였으나, 암호키 기반 대응방식과 같은 기존 대응방법은 내부공격자 방어에 한계가 있었다. 따라서, 본 연구에서는 클러스터링 알고리즘의 CH 선출 통계를 바탕으로 비정상적인 CH 선출 공격을 탐지하는 통계적 탐지기법을 제안한다. 대표적인 클러스터링 알고리즘인 LEACH와 HEED가 운영되는 환경에서 오염노드에 의한 비정상적인 CH 선출 공격을 설계하고, 제안기법의 공격탐지여부에 대한 실험을 통해 제안기법의 효과성을 확인하였다.

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

본 연구는 공개된 수질측정 자료를 이용하여 담수호의 수질변화추이를 분석하고 수질항목의 이상여부의 판단기준을 마련하며, 자료로부터 부영양화의 지표인 Chlorophyll-a를 예측할 수 있는 회귀모형을 구성하여 담수호 관리에 이용할 수 있는 방안을 검토하고자 하였다. 이에 따라 서해안 담수호 3개소를 선정하여 약 20년간의 수질항목자료를 회귀분석 방법으로 분석하고, 각 수질항목의 연중 주기적인 변화를 나타내는 회귀식과 신뢰도 95%에서의 표준편차를 산정함으로서 이상 여부의 판단방법을 제시하였다. 또한 불규칙한 관측일로부터 Chlorophyll-a의 시간적 변화율을 산정하고, 다른 수질항목간의 상관관계 분석 및 회귀모형을 구성하여 분석함으로서 수질측정 자료만을 이용하여 Chlorophyll-a의 변화를 예상할 수 있는 방법을 제시하였다. 본 연구결과는 통계학적 모형에 의한 근사적인 수질예측방법으로서 향후 수질측정 자료의 양적·질적 개선이 이루어진다면 담수호 수질관리에 기여할 것으로 기대된다.

As using public monitoring data, analysing a trends of water quality change, establishing a criteria to determine abnormal status and constructing a regression model that can predict Chlorophyll-a, an indicator of eutrophication, was studied. Accordingly, the three freshwater lakes were selected, approximately 20 years of water quality monitoring data were analyzed for periodic changes in water quality each year using regression analysis, and a method for determining abnormalities was presented by the standard deviation at confidence level 95%. By calculating the temporal change rate of Chlorophyll-a from irregular observed data, analyzing correlations between the rate and other water quality items, and constructing regression models, a method to predict changes in Chlorophyll-a was presented. The results of this study are expected to contribute to freshwater lake water quality management as an approximate water quality prediction method using the statistical model.

항공기, 선박 등의 내부 및 핵심 설비 장치들에는 안전 관리를 위하여 다양한 종류의 환경 센서들을 설치되며 이는 이상 상황들을 감지하는 데 활용된다. 실시간적으로 수집되는 센서 데이터 중 이상 상황이 발생했을 시에 센서 데이터들은 중요한 데이터로 상황 감지 및 저장 분석에 활용되어야 하지만 이상치 (outlier) 데이터는 이상 상황 데이터의 정확도를 떨어뜨리는 불필요한 데이터이기 때문에 판단이 필요하다. 본 논문에서는 다중 센서의 데이터를 기반으로 통계적 분석 방법 중에 하나인 IQR 방식과 함께 센서 간의 상관관계를 활용하여 실시간적으로 수집되는 데이터가 이상 상황에서 발생한 것인지 센서 고장이나 오류에 의한 이상치인지를 판단할 수 있는 알고리즘을 제안하였다. 제안된 알고리즘을 실시간 센서 데이터 수집 및 처리 장치에 구현하고 실제 화재 테스트 환경에서 실험한 결과, 기존 IQR 방식의 이상치 검출 방식에서는 누락되던 이상 상황 데이터를 정확하게 검출할 수 있음을 확인하였다.

To ensure safety management, various types of environmental sensors are installed in the interior and critical equipment of aircraft and ships, and these sensors are used to detect abnormal situations. Among the sensor data collected in real time, data generated during abnormal situations must be utilized as essential information for situation detection, storage, and analysis. However, outlier data can reduce the accuracy of abnormal situation data and should therefore be carefully evaluated. This paper proposes an algorithm that combines the Interquartile Range (IQR) statistical analysis method with inter-sensor correlations to determine whether real-time sensor data are generated from an actual abnormal situation or are outliers caused by sensor faults or errors. The proposed algorithm was implemented in a real-time sensor data collection and processing system and tested in a fire test environment. Experimental results demonstrated that the algorithm effectively detects abnormal situation data that were missed by conventional IQR-based outlier detection methods.

본 연구에서는 급수전개를 이용한 추계론적 유한요소해석법의 개선을 위한 등가몬테카를로 추계장함수를 제안하고 1차 Taylor전개를 이용한 추계론적 유한요소해석법인 가중적분법에 적용하였다. 일반적으로 1차 Taylor전개를 이용하는 수치해석법에서의 응답변화도는 고려하고 있는 추계장의 분산계수에 대하여 선형거동을 보인다. 그러나 몬테카를로 해석의 경우 추계장 분산계수에 대하여 비선형 거동을 나타낸다. 이는 급수전개법의 1차 Taylor전개에 따른 선형특성에 기인한다. 따라서, 가중적분법에서 사용되는 Taylor전개된 변위벡터와 몬테카를로 해석에서의 변위벡터를 비교하고 이들 두 변위벡터 사이에 상호 불일치 하는 점을 고찰하여 몬테카를로 해석에서의 변위벡터와 등가의 변위벡터를 구성하고 이를 가중적분법에 적용하였다. 제안한 등가몬테카를로 추계장은 본래의 추계장 함수에 대한 고차함수로 주어진다. 평면구조에 대한 수치해석을 통하여 제안한 등가몬테카를로 추계장을 이용한 정식화의 타당성을 고찰하였다 새로운 정식화는 기존의 l차 가중적분법을 위한 정식화 과정과 유사하게 수행할 수 있었다.

In this paper, a stochastic field that is compatible with Monte Carlo simulation is suggested for an expansion-based stochastic analysis scheme of weighted integral method. Through investigation on the way of affection of stochastic field function on the displacement vector in the series expansion scheme, it is noticed that the stochastic field adopted in the weighted integral method is not compatible with that appears in the Monte Carlo simulation. As generally recognized in the field of stochastic mechanics, the response variability is not a linear function of the coefficient of variation of stochastic field but a nonlinear function with increasing variability as the intensity of uncertainty is increased. Employing the stochastic field suggested in this study, the response variability evaluated by means of the weighted integral scheme is reproduced with high precision even for uncertain fields with moderately large coefficient of variation. Besides, despite the fact that only the first-order expansion is employed, an outstanding agreement between the results of expansion-based weighted integral method and Monte Carlo simulation is achieved.