Earticle

Due to the earthquake in Türkiye, the importance of preventing earthquake damage has increased worldwide. Korea is not safe from earthquakes, either. In 2016 and 2017, a Gyeongju earthquake with a record-high magnitude (ML 5.8) and the Pohang earthquake (ML 5.4) occurred. In addition, in Korea, earthquakes with a magnitude of 3.0 or higher have occurred about 12 times a year on average in the last 10 years (2013-2022), so it is necessary to prepare for damage prevention. The objective of this study is to explain the measures to prevent landslides caused by earthquakes in Korea. Since there are no cases of earthquake-induced landslides in Korea, the evaluation factors were derived by analyzing overseas studies. In addition, the applicability of the landslide hazard map on the national scale of Korea was examined. The earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Finally, an experimental study was conducted for critical rainfall adjustment for early warning after the earthquake. Three soil samples were collected from the cut-slope area in mountains and a direct shear test was performed to calculate the strength parameters according to the change in the water content ratio. In addition, the ring shear test according to the change in the water content ratio and the change in shear strength due to water supply during the shear was analyzed. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. As a result of the direct shear test, the cohesion decreased linearly as it approached the saturation state. When water was supplied during the ring shear test, the maximum shear strength decreased by 65.7-74.8% and the residual shear strength decreased by 53.5-60%. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area. Also, it is more likely that landslides will occur due to rainfall after the earthquake.

본 연구는 ‘고속도로 톨게이트 상단 광고(이하 T/G광고)’의 효과를 예측할 수 있는 방법을 제시할 목적으로 진행되었다. 이를 위해 우선 (1) 교통량, (2) 유효가시거리, (3) viewpoint 도달점, (4) 유효가시도, (5) 상대적 유효가시도, (6) 광고 노출시간 등 T/G광고의 효과에 영향을 미칠 것으로 예상되는 변인들을 제시하였다. 그리고 이 변인들을 바탕으로 T/G광고의 정량적 효과를 나타내는 ‘impact 지수’를 계산하는 방법을 제시하였다. 마지막으로 impact 지수에 T/G광고의 정성적 가치를 결합하여 T/G광고 효과의 index값을 산출하는 방법을 제시한 뒤, 이를 전국 348개 톨게이트 중 교통량 상위 30위에 해당하는 톨게이트 및 그 외 기타 요소들을 고려하여 추가한 3개의 톨게이트 등 총 33개의 톨게이트에 적용하여 각 톨게이트의 효과등급을 제시하였다.

This study aimed to develop a method that can predict the impact of tollgate advertising. To this end, this study first identified the several key factors that are expected to affect the impact of tollgate advertising, such as (1) traffic, (2) valid distance of visibility, (3) viewpoint reach point, (4) valid degree of visibility, (5) relative degree of visibility, and (6) time of advertising exposure. Based on the factors, this study developed a method that can estimate the degree of impact indicating the quantitative impact of tollgate advertising. Further, the degree of impact was combined with the qualitative value of tollgate advertising to index the impact of tollgate advertising. Finally, the index was applied to the top 30 tollgates out of 348 tollgates across the country in terms of traffic and the other 3 tollgates added to grade the impact of the 33 tollgates.

In the cloud computing environment, according to the predicting average response time of service, it can adjust to the follow-up system, so that the response time of the system is acceptable. The traditional methods of predicting average response time of serve mainly include the method of gray predicting and neural network model, but the two methods face several problems, such as longer processing time and unsuitable to larger volatility data. According to the above problems, the paper proposes the method of predicting average response time of cloud service based on the MGM (1, N) - BP neural network, the combination of two methods of predicting can use less sample information, it can get a high precision of predicting result and it can also predict the volatile system. Experimental results show the feasibility and effectiveness of the method.

A modeling method which can predict the shelf life of various types of spare parts in a relatively short time is put forward in this article. At present, it is difficult to solve the problem of mass modeling because the shelf life prediction models for different kinds of spare parts are of great diversification. In this paper, the best fitting nonlinear variables are selected by Gram-Schmidt regression method, and the detailed steps of automatic modeling process are given, which have advantages of strong robustness and are easy in programming. Especially, it can eliminate the influence of multicollinearity among alternative models effectively. By using natural rubber heating elongation data, an example is taken to demonstrate the process of automatic modeling. The nonlinear regression models selected by automatic modeling process are consistent in Dakin equation, and the predict values of natural rubber shelf life are included in the storage period given by manufacturing plant.

Frictional resistance comprises more than 60% of the total resistance for most merchant ships. Active and passive devices have been used to reduce frictional resistance, but the most effective and practical device is an air lubrication system. Such systems have been applied in several ships, and their effects have been verified in sea trials. On the other hand, there are some differences between the results predicted in model tests and those measured in sea trials. In this study, numerical analyses were carried out for a model and a full-scale ship. A new extrapolation method was proposed to improve the estimation of the full-scale resistance of a ship with an air lubrication system. The volume of fluid (VOF) method was considered for the numerical models of the air layer. The numerical method was validated by comparing the experimental data on the air layer pattern and the total resistance.

The statistical two-order and two-scale method is developed for predicting the mechanics parameters, such as stiffness and strength of core-shell particle-filled polymer composites. The representation and simulation on meso-configuration of random particle-filled polymers are stated. And the major statistical two-order and two-scale analysis formulation is briefly given. The two-order and two-scale expressions for the strains and stresses of conventionally strength experimental components, including the tensional or compressive column, the twist bar and the bending beam, are developed by means of their classical solutions with orthogonal-anisotropic coefficients. Then a new effective mesh generation algorithm is presented. The mechanics parameters of core-shell particle-filled polymer composites, including the expected stiffness parameters, minimum stiffness parameters, and the expected elasticity limit strength and the minimum elasticity limit strength, are defined by means of the stiffness coefficients and elasticity strength criterions for core, shell and matrix. Finally, the numerical results for predicting both stiffness and elasticity limit strength parameters are compared with the experimental data.

Predicting the sloshing motion of a coolant during a seismic assessment of a rectangular spent fuel pool is of critical concern. Linear theory, which provides a simple analytical method, has been used to predict the sloshing motion in rectangular pools and tanks. However, this theory is not suitable for the high-frequency excitation problem. In this study, the authors developed a simple analytical method for predicting the sloshing motion in a rectangular pool for a wide range of excitation frequencies. The correlation among the linear theory parameters, influencing on excitation and convective waves, and the excitation frequency is investigated. Sloshing waves in a rectangular pool with several liquid heights are predicted using the original linear theory, a modified linear theory and computational fluid dynamics analysis. The results demonstrate that the developed method can predict sloshing motion over a wide range of excitation frequencies. However, the developed method has the limitations of linear solutions since it neglects the nonlinear features of sloshing motion. Despite these limitations, the authors believe that the developed method can be useful as a simple analytical method for predicting the sloshing motion in a rectangular pool under various external excitations.

Large numbers of submarine pipelines are laid as the world now is attaching great importance to offshore oil exploitation. Free spanning of submarine pipelines may be caused by seabed unevenness, change of topology, artificial supports, etc. By combining Iwan's wake oscillator model with the differential equation which describes the vibration behavior of free-span submarine pipelines, the pipe-fluid coupling equation is developed and solved in order to study the effect of both internal and external fluid on the vibration behavior of free-span submarine pipelines. Through generalized integral transform technique (GITT), the governing equation describing the transverse displacement is transformed into a system of second-order ordinary differential equations (ODEs) in temporal variable, eliminating the spatial variable. The MATHEMATICA built-in function NDSolve is then used to numerically solve the transformed ODE system. The good convergence of the eigenfunction expansions proved that this method is applicable for predicting the dynamic response of free-span pipelines subjected to both internal flow and external current.

In this paper, we propose a heuristic method to select features using a Two-Phase Markov Blanket-based (TPMB) algorithm. The first phase, filtering phase, of TPMB algorithm works by filtering the obviously redundant features. A non-linear correlation method based on Information theory is used as a metric to measure the redundancy of a feature [1]. In second phase, approximating phase, the Markov Blanket (MB) of a system is estimated by employing the concept of cross entropy to identify the MB. We perform experiments on microarray data and report two popular dataset, AML-ALL [3] and colon tumor [4], in this paper. The experimental results show that the TPMB algorithm can significantly reduce the number of features while maintaining the accuracy of the classifiers.

Background: The Jra antigen is a high-prevalence red blood cell (RBC) antigen. Reports on cases of fatal hemolytic disease of the fetus and newborn and acute hemolytic transfusion reactions suggest that antibodies against Jra (anti-Jra) have potential clinical significance. Identifying anti-Jra is challenging owing to a lack of commercially available antisera. We developed an alternative approach to rapidly predict the presence of anti-Jra using the Taq- Man single-nucleotide polymorphism (SNP)-genotyping method. Methods: Residual peripheral blood samples from 10 patients suspected of having the anti-Jra were collected. Two samples with confirmed Jr(a?) RBCs and anti-Jra were used to validate the TaqMan genotyping assay by comparing the genotyping results with direct sequencing. The accuracy of the assay in predicting the presence of anti-Jra was verified through crossmatching with in-house Jr(a?) O+ RBCs. Results: The TaqMan-genotyping method was validated with two Jr(a?) RBC- and anti-Jraconfirmed samples that showed concordant Jra genotyping and direct sequencing results. Jra genotyping for the remaining samples and crossmatching the serum samples with inhouse Jr(a?) O+ RBCs showed consistent results. Conclusions: We validated a rapid, simple, accurate, and cost-effective method for predicting the presence of anti-Jra using a TaqMan-based SNP-genotyping assay. Implementing this method in routine practice in clinical laboratories will assist in solving difficult problems regarding alloantibodies to high-prevalence RBC antigens and ultimately aid in providing safe and timely transfusions and proper patient care.

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<<p). Several methods for variable selection have been proposed in the Cox model. Among those, we consider least absolute shrinkage and selection operator (LASSO), threshold gradient descent regularization (TGDR), and two different 치ustering threshold gradient descent regularization (CTGDR}-the K-means CTGDR and the hierarchical CTGDR-and compare these four methods in an application of lung cancer data. Comparison of the four methods shows that the two CTGDR methods yield more compact gene selection than TGDR, while LASSO selects the smallest number of genes. When these methods are evaluated by the approach of Ma and Huang (2007), none of the methods shows satisfactory performance in separating the two risk groups using the log-rank statistic based on the risk scores calculated from the selected genes. However, when the risk scores are calculated from the genes that are significant in the Cox model, the performance of the log-rank statistics shows that the two risk groups are well separated. Especially, the TGDR method has the largest log-rank statistic, and the K-means CTGDR method and the LASSO method show similar performance, but the hierarchical CTGDR method has the smallest log-rank statistic.

Because forest fire changes the direction according to the environmental elements, it is difficult to predict the direction of it. Currently, though some researchers have been studied to which predict the forest fire occurrence and the direction of it, using the remote detection technique, it is not enough and efficient. And recently because of the development of the sensor technique, a lot of In-Situ sensors are being developed. These kinds of In-Situ sensor data are used to collect the environmental elements such as temperature, humidity, and the velocity of the wind. Accordingly we need the prediction technique about the environmental elements analysis and the direction of the forest fire, using the In-Situ sensor data. In this paper, as a technique for predicting the direction of the forest fire, we propose the correlation analysis technique about In-Situ sensor data such as temperature, humidity, the velocity of the wind. The proposed technique is based on the clustering method and clusters the In-Situ sensor data. And then it analyzes the correlation of the multivariate correlations among clusters. These kinds of prediction information not only helps to predict the direction of the forest fire, but also finds the solution after predicting the environmental elements of the forest fire. Accordingly, this technique is expected to reduce the damage by the forest fire which occurs frequently these days.

인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

In identifying flood vulnerable areas, three methods are generally deployed: the geomorphology method which is based on topographic features; the past evidence method based on observed data of past actual floods; and, prediction of flood areas through hydrologic models. This study aims to improve the prediction model of the geomorphology method through the application of fuzzy method in GIS modeling. The generally used GIS method of superimposing thematic map layers assumes crisp boundaries of the layers, which results in either risk-averse solutions or risk-taking solutions. The introduction of fuzzy concepts to processing of evaluation criteria (DEM, slope, aspect) solves this problem. As the result of applying the fuzzy method to a test site in the west Nak-Dong river, similar flood vulnerable areas were predicted as when using the conventional Boolean criteria. The resulting map, however, showed varying degree of uncertainty of flooding in these areas. This extra information is deemed to be valuable in taking phased actions during flood response, leading to a more effective and timely decision-making.

The equilibrium lattice constants for various rock salt structure type compounds were predicted by an ab-initio pseudopotential method adapted in the Vienna Ab-initio Simulation Program (VASP), and the values were compared with the experimental lattice constants found in the literature to check the transferability of the pseudopotential of each atom. The results clearly indicate that a well-transferable pseudopotential is essential to predict the material properties of complex ionic systems consisting of many different kinds of atoms.

The goal of this study is to analyze the effects of geometries of mortise and tenon on moment carrying capacity of the "sagae" connection. Effects of different tenon widths, mortise depths of connection from the top and bottom beams on stress distribution were investigated using the finite element method (FEM). Critical normal and shear stresses occurred at the reentrant corner from the mortise of the bottom beam. The maximum moment carrying capacity of the sagae connection from the FEM was validated from the results of experimental test. Maximizing moment carrying capacity of the sagae connection was found when the tenon width and mortise depth from the two beams were 40 mm and 60 mm, respectively.

To investigate the practical assessing method of pork quality, 302 carcasses were selected randomly to represent commercial conditions and were probed at 24 hr postmortem (PM) by Danish Meat Quality Marbling (MQM), Hennessy Grading Probe (HGP), Sensoptic Resistance Probe (SRP) and NWK pH-K21 meter (NpH). Also, filter paper wetness (FPW), lightness (L*), ultimate pH (pHu), subjective color (SC), firmness/wetness (SF) and marbling scores (SM) were recorded. Each carcass was categorized as either PSE (pale, soft and exudative), RSE (Reddish-pink, soft and exudative), RFN (reddish-pink, firm and non-exudative) or DFD (dark, firm and dry). When discriminant analysis was used to sort carcasses into four quality groups the highest proportion of correct classes was 65% by HGP, 60% by MQM, 52% by NpH and 32% by SRP. When independent variables were combined to sort carcasses into groups the success was only 67%. When RSE and RFN groups were merged so that there were only three groups (PSE, RSE+RFN, DFD) differentiating by color MQM was able to sort the same set of data into the new set of three groups with 80% accuracy. The proportions of correct classifications for HGP, NpH and SRP were 75%, 61% and 35% respectively. There was a decline in predication accuracy when only two groups, exudative (PSE and RES) and non exudative (RFN and DFD) were sorted. However, when two groups designated PSE and non-PSE (RSE, RFN and DFD) were sorted then the proportion of correct classification by MQM, HGP, SRP and NpH were 87%, 81%, 71% and 66% respectively. Combinations of variables only increased the prediction accuracy by 1 or 2% over prediction by MQM alone. When the data was sorted into three marbling groups based on SM this was not well predicted by any of the probe measurements. The best prediction accuracy was 72% by a combination of MQM and NpH.

가스터빈 사이클의 성능 및 NOx 배출물 생성량 예측을 위한 모사 프로그램을 개발하였다. 압축기 및 터빈은 등엔트로피 과정으로, 연소기는 Thermal NOx 생성을 수반하는 연소모형으로서 가정하였다. 또한 터빈 냉각을 위한 추출공기량과 냉각방식이 성능에 미치는 적절한 상관 관계식을 도입하여 평가하였다. 본 성능평가 모델을 이용하여 예측된 결과와 실험결과간의 비교를 통하여 모델의 타당성을 검증하였고, 증기 분사량, 터빈 냉각변수 및 압축비 변화에 따른 예측결과를 통하여 가스터빈 시스템 최적 운전 및 설계기준을 제시하였다.

자율주행 차량에는 라이다, 레이더, 카메라 등 다양한 센서들이 융합되어 활용되고 있다. 특히 라이더 및 레이더는 고가의 장비로 자율주행 자동차의 대중화를 위해 해결해야 하는 부분으로 고가의 장비를 대체할 수 있는 연구가 지속적으로 이루어지고 있다. 본 논문에서는 비용면에서 저가이면서 손쉽게 장착할 수 있는 단일 카메라를 이용하여 주행 차량의 전방 측면 차량 바퀴와 인접 차선을 감지하고 거리를 추정하는 방법을 제안하였다. 제안된 방법은 입력 영상을 통해 프레임 추출 후 프레임 이미지에서 차선과 바퀴를 검출하고 거리를 측정하여 실제 도로 환경에서 실측 된 거리와 비교하였고, 오차범위 ±3cm 안에서 비교적 정확히 거리를 산출할 수 있었다. 이를 통해 자율주행 자동차의 비용 절감 또는 라이다, 레이더 센서의 고장으로 대체 가능한 수단으로 활용할 수 있을 것으로 판단된다.

Various sensors such as lidar, radar, and camera are fused and used in autonomous vehicles. Rider and radar sensors are difficult to popularize because they are expensive equipment. In order to popularize autonomous vehicles, research that can replace expensive equipment is continuously being conducted. In this paper, we use a single camera that is inexpensive and can be easily mounted. We propose a method for detecting the wheels and adjacent lanes of a front-side vehicle of a driving vehicle and estimating distances. Our proposed method detects lanes and wheels from frame images after frame extraction via input images. In addition, the distance is measured and compared with the actual distance measured in the actual road environment. The distance could be calculated relatively accurately within the error range of ± 3 cm. Through this, it is expected that the camera can be used as an alternative means when the cost of autonomous vehicles is reduced or when the lidar or radar sensor fails.

강우에 의한 사면파괴의 초기단계 및 과정을 규명하기 위하여 실내 사면모델 시험결과를 분석하였다. 이 파괴 초기단계와 관련된 수리학적 상호관계를 파악하기 위해 인공강우 살포동안 사면모델 내 체적함수비 변화를 측정하는데 목적을 두었다. 사면파괴의 과정은 인공강우동안 사면 하단부의 유출면 발달에 의해 파괴를 발생시키는 요인으로 작용하였다. 따라서 국부적인 사면파괴를 예측하기 위해서는 강우에 의해 발생된 침투수의 침투특성에 대한 장기적인 계측이 매우 중요하다. 사면 내에서 강우에 의한 침윤선의 침투 및 지하수위 상승과 밀접한 연관성을 갖는 체적함수비 변화를 3가지의 침투변화(phase I, phase II, phase III로 나누어 사면파괴를 계측할 수 있는 수리학적 측면으로 접근하였다. 따라서 체적함수비가 급격히 증가하는 phase III 단계부터는 사면파괴 발생 가능성이 매우 증가함을 파악하였다. 그러므로 사면 내 체적함수비 변화를 연속적으로 계측함으로써 강우에 의한 사면파괴의 발생을 효율적이고 실질적으로 예측할 수 있을 것으로 판단된다.

This study presents the results of a series of laboratory scale slope failure experiments aimed at clarifying the process and the condition leading to the initiation of rainfall-induced slope failures. For the evaluation of hydrologic response of the model slopes in relation the process of failure initiation, measurements were focused on the changes in volumetric water content during the initiation process. The process leading to failure initiation commences by the development of a seepage face. It appears reasonable to conclude that slope failures are a consequence of the instability of seepage area formed at the slope surface during rainfall period. Therefore, this demonstrates the importance of monitoring the development seepage area for useful prediction about the timing of a particular failure event. The hydrologic response of soil slopes leading to failure initiation is characterized by three phases (phase I, II and III) of significant increase in volumetric water content in association with the ingress of wetting front and the rise of groundwater level within the slope. The period of phase III increase in volumetric water content can be used to initiate advance warning towards a failure initiation event. Therefore, for the concept outlined above, direct and continuous monitoring of the change in volumetric water content is likely to provide the possibility for the development of a reliable and effective means of predicting the occurrence of rainfall-induced slope failures.