• Geomechanics and Engineering
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• 제29권3호
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Geomechanics and Engineering
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• 제30권2호
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Korean Journal of Mathematics
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• 제30권4호
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• pp.593-601
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• 2022

Let C(Q) denote Yeh-Wiener space, the space of all real-valued continuous functions x(s, t) on Q ≡ [0, S] × [0, T] with x(s, 0) = x(0, t) = 0 for every (s, t) ∈ Q. For each partition τ = τ_m,n = {(s_i, t_j)|i = 1, . . . , m, j = 1, . . . , n} of Q with 0 = s₀ < s₁ < . . . < s_m = S and 0 = t₀ < t₁ < . . . < t_n = T, define a random vector X_τ : C(Q) → ℝ^mn by X_τ (x) = (x(s₁, t₁), . . . , x(s_m, t_n)). In this paper we study the conditional integral transform and the conditional convolution product for a class of cylinder type functionals defined on K(Q) with a given conditioning function X_τ above, where K(Q)is the space of all complex valued continuous functions of two variables on Q which satify x(s, 0) = x(0, t) = 0 for every (s, t) ∈ Q. In particular we derive a useful equation which allows to calculate the conditional integral transform of the conditional convolution product without ever actually calculating convolution product or conditional convolution product.

• 로봇학회논문지
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• 제18권1호
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• pp.37-47
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• 2023

This paper introduced how to construct Denavit-Hartenberg (DH) parameters from the Unified Robot Description Format (URDF). URDF is convenient for describing a robot even though the robot is very complex. On the other hand, DH convention is not an easy notation for many novices who want to describe a robot. Therefore, most vendors provide URDF and users prefer to use URDF to describe a robot. However, some controllers or algorithms are based on DH parameters to perform kinematics, dynamics, control, etc. To connect URDF and DH parameters, we present a three-step approach to construct DH parameters from URDF. The first step is to define the joint axis for constructing DH parameters. The second step is constructing DH parameters to define joint character. The final step is constructing DH parameters to define the coordinate frame of the child link. This approach is based on intuitive vector calculation and guarantees the uniqueness of DH parameters. To verify our approach, we applied our approach to a simple one-link robot, a manipulator with 6 DOF, and a quadruped robot with 3 DOF per leg. We verified that our approach worked well based on forward kinematic results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Tuberculosis and Respiratory Diseases
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• 제44권4호
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• pp.796-805
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• 1997

연구배경 : 세포주기의 활성화, 그 중에서도 특히 $G_1$/S 이행에 관여하는 세포주기관련 단백질들은 암발생에 있어서 매우 중요한 역할을 하는 것으로 알려져 있다. $G_1$ 세포주기 관련 단백질 중의 하나인 cdk4 (cyclin dependent kinase 4)의 억제제로 알려져 있는 p16 유전자는 최근에 밝혀진 종양억제유전자중의 하나로서 MTS1 (multiple tumor suppressor 1)이라고도 불린다. p16 유전자는 지금까지 알려진 어느 종양관련 유전자보다도 유전자변이의 빈도가 높은 암억제유전자인데, 특히 비소세포폐암인 경우는 70% 이상의 세포주에서 p16 단백질의 발현이 없는 것으로 밝혀져 있어 p16 유전자는 비소세포폐암 발생에 매우 중요한 역할을 할 것이라고 알려져 있다. 본 연구에서는 비소세포폐암에서 p16을 이용한 유전자치료의 타당성을 입증하기 위하여 다음과 같은 연구를 시행하였다. 방 법 : p16이 결여된 비소세포폐암 세포주 (NCI-H441)에, 정상섬유아세포에서 총 RNA를 추출하여 역전사효소 및 DNA 중합효소반응으로 증폭된 p16 cDNA를 유핵세포 발현 vector인 pRC-CMV plasmid에 subcloning하여 구축된 pRC-CMV-p16 plasmid vector를 lipofectin을 이용하여 유전자 이입한 후, 단백질을 추출하여 Western blot 분석과 면역침전법으로 $G_1$ 세포주기관련 단백질의 변동을 관찰하고, colony 형성능을 비교함으로써 암억제효과를 확인하였다. 결 과 : p16이 유전자주입된 NCI-H441 세포주에서 p16과 cdk4가 복합체를 형성하고 있고 인산화 Rb가 대조 세포주에 비해 감소되어 있음을 확인할 수 있어, p16이 cdk4와 결합함으로써 cdk4에 의한 Rb의 인산화를 방해하고 이에 따른 $G_1$ 세포주기 정체에 의해 종양억제효과가 나타난다는 설명을 뒷받침할 수 있었다. Clonogenic assay 결과는 p16 유전자주입된 NCI-H441 세포주의 colony 형성능이 대조 세포주에 비하여 현격히 감소함을 관찰하였다. 결 론 : 이상의 결과로 p16(MTS1) 유전자를 p16 단백질을 발현하지 못하는 비소세포폐암 세포주에 주입할 경우, 주입한 유전자에서 생성되는 p16 단백질이 cdk와 결합하여 Rb 단백질의 인산화를 저하시켜 궁극적으로 암억제 효과를 일으킬 수 있음이 확인되었고, 이는 향후 비소세포폐암의 유전자치료에 있어서 p16 유전자의 이용 가능성을 확인한 기초자료가 된다고 생각된다.

• 한국정보전자통신기술학회논문지
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• 제10권5호
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• pp.409-416
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• 2017

본 논문은 유한체(finite fields)에서 압축센싱(compressed sensing) 프레임워크를 살펴본다. 하나의 측정 샘플은 센싱행렬의 행과 희소 신호 벡터와의 내적으로 연산되며, 본 논문에서 제안하는 확률적 희소 신호 복원 알고리즘을 이용하여 그 압축센싱의 해를 찾고자 한다. 지금까지 압축센싱은 실수(real-valued)나 복소수(complex-valued) 평면에서 주로 연구되어 왔지만, 이와 같은 원신호를 처리하는 경우 이산화 과정으로 정보의 손실이 뒤따르게 된다. 이에 대한 연구배경은 이산(discrete) 신호에 대한 희소 신호를 복원하고자 하는 노력으로 이어지고 있다. 본 연구에서 제안하는 프레임워크는 센싱행렬로써 코딩 이론에서 사용된 LDPC(Low-Density Parity-Check) 코드의 패러티체크 행렬을 이용한다. 그리고 본 연구에서 제안한 확률적 복원 알고리즘을 이용하여 유한체의 희소 신호를 복원한다. 기존의 코딩 이론에서 발표한 LDPC 복호화와는 달리 본 논문에서는 희소 신호의 확률분포를 이용한 반복적 알고리즘을 제안한다. 그리고 개발된 복원 알고리즘을 통하여 우리는 유한체의 크기가 커질수록 복원 성능이 우수한 결과를 얻었다. 압축센싱의 센싱행렬이 LDPC 패러티체크 행렬과 같은 저밀도 행렬에서도 좋은 성능을 보여줌에 따라 이산 신호를 고려한 응용 분야에서 적극적으로 활용될 것으로 기대된다.

• Journal of Microbiology and Biotechnology
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• 제28권9호
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• pp.1547-1553
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• 2018

Hyaluronidases are a family of enzymes that catalyse the breakdown of hyaluronic acid, which is abundant in the extracellular matrix and cumulus oocyte complex. To investigate the activity of recombinant bovine sperm hyaluronidase 1 (SPAM1) and determine the effect of the Asn-X-Ser/Thr motif on its activity, the bovine SPAM1 open reading frame was cloned into the mammalian expression vector pCXN2 and then transfected to the HEK293 cell line. Expression of recombinant bovine hyaluronidase was estimated using a hyaluronidase activity assay with gel electrophoresis. Recombinant hyaluronidase could resolve highly polymeric hyaluronic acid and also caused dispersal of the cumulus cell layer. Comparative analysis with respect to enzyme activity was carried out for the glycosylated and deglycosylated bovine sperm hyaluronidase by N-glycosidase F treatment. Finally, mutagenesis analysis revealed that among the five potential N-linked glycosylation sites, only three contributed to significant inhibition of hyaluronic activity. Recombinant bovine SPAM1 has hyaluronan degradation and cumulus oocyte complex dispersion ability, and the N-linked oligosaccharides are important for enzyme activity, providing a foundation for the commercialization of hyaluronidase.