• 한국기계가공학회지
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• 제21권7호
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• pp.91-97
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• 2022

Pipes transport and supply fuel in various categories; however, corrosion occurs because of the external environment, impurities are mixed in the fuel, and substances leak to the outside, which can lead to serious accidents. Therefore, in this study, inspection equipment using a laser scanner was manufactured to classify conditions according to the degree of corrosion of the outer wall of the pipe, and the corrosion height and maximum value of the pipe were obtained from the surface information. Using the k-means method, it was classified into four states, and the standard of the average height and maximum height of corrosion for each state was derived.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2670-2685
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• 2020

Flying ad-hoc networks (FANETs) is a vibrant research area nowadays. This type of network ranges from various military and civilian applications. FANET is formed by micro and macro UAVs. Among many other problems, there are two main issues in FANET. Limited energy and high mobility of FANET nodes effect the flight time and routing directly. Clustering is a remedy to handle these types of problems. In this paper, an efficient clustering technique is proposed to handle routing and energy problems. Transmission range of FANET nodes is dynamically tuned accordingly as per their operational requirement. By optimizing the transmission range packet loss ratio (PLR) is minimized and link quality is improved which leads towards reduced energy consumption. To elect optimal cluster heads (CHs) based on their fitness we use k-means. Selection of optimal CHs reduce the routing overhead and improves energy consumption. Our proposed scheme outclasses the existing state-of-the-art techniques, ACO based CACONET and PSO based CLPSO, in terms of energy consumption and cluster building time.

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1259-1268
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• 2017

The alpha eigenvalue problem in multigroup neutron diffusion is studied with particular attention to the theoretical analysis of the model. Contrary to previous literature results, the existence of eigenvalue and eigenflux clustering is investigated here without the simplification of a unique fissile isotope or a single emission spectrum. A discussion about the negative decay constants of the neutron precursors concentrations as potential eigenvalues is provided. An in-hour equation is derived by a perturbation approach recurring to the steady state adjoint and direct eigenvalue problems of the effective multiplication factor and is used to suggest proper detection criteria of flux clustering. In spite of the prior work, the in-hour equation results give a necessary and sufficient condition for the existence of the eigenvalue-eigenvector pair. A simplified asymptotic analysis is used to predict bands of accumulation of eigenvalues close to the negative decay constants of the precursors concentrations. The resolution of the problem in one-dimensional heterogeneous problems shows numerical evidence of the predicted clustering occurrences and also confirms previous theoretical analysis and numerical results.

• 대한음성학회지:말소리
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• 제45호
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

• 융합신호처리학회논문지
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• 제2권4호
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• pp.9-15
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• 2001

본 연구에서는 한국어 음성인식 성능향상을 위한 문맥의존 음향 모델을 개선하기 위하여 한국어 음성학적 지식과 결정트리를 접목한 음소결정트리 기반 상태분할 알고리즘으로 한국어에 적합한 문맥의존 음향 모델에 관해 고찰한다. HMM (Hidden Markov Model)의 각 상태를 네트워크로 연결하여 문맥의존 음향모델로 표현하는 HM-Net(Hidden Markov Network)이 있는데 이는 SSS(Successive State Splitting) 알고리즘으로 작성한다. 이 방법은 음향 모델의 상태공유관계와 모델의구조를 결정하는데 효율적이지만 모델을 학습할때 문맥환경에 따라 출현하지 않는 문맥이 존재하는 문제점이 있다 본 연구에서는 이러한 문제점을 해결하기 위해 2진 결정트리와 SSS 알고리즘의 장점을 결합하여 문맥방향 상태분할을 수행할 때 각 노드에서 한국어 음성학적 지식으로 구성된 음소 질의어에 따라 상태분할 하는 방법으로서 PDT-SSS(Phonetic Decision Tree-based SSS) 알고리즘을 적용한다. 적용한 방법으로 작성한 문맥의존 음향 모델의 유효성을 확인하기 위해 국어공학센터 (KLE)m이 452 단어와 항공편 예약관련 200문장(YNU 200)에 대해 화자독립 음소, 단어 및 연속음성인식 실험을 수행하였다. 인식실험결과, 문맥 의존 음향모델에 대한 화자독립 음소, 단어 및 연속음성 인식실험에서 기존의 단일 HMM 모델보다 향상된 인식률을 보여, 한국어에 적합한 문맥의존 음향 모델을 작성하는데 한국어 음성학적 지식과 음소결정트리 기반 상태분할 알고리즘이 유효함을 확인하였다.

• 한국정보과학회논문지:시스템및이론
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• 제26권12호
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• pp.1493-1503
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• 1999

ATM Clustering System과 같이 SAN(System Area Network) 환경에서 동작하는 시스템은 낮은 지연시간과 넓은 대역폭의 네트워크가 필수적이나 기존의 에러 복구 프로토콜들은 이러한 요구를 충족시키기에는 큰 오버헤드를 가지고 있다. 제안된 새로운 에러 복구 프로토콜은 ATM Clustering System 환경에서 최적의 성능을 나타내는 light-weight 프로토콜로 에러가 없는 상황과 에러 복구가 진행중인 상황에 따라 acknowledgement 주기를 적응적으로 변화시키는 adaptive acknowledgement scheme를 제안하여 적용하였다. 제안된 프로토콜은 상용 툴인 SDT를 이용한 논리 검증 받았고, DEVSim++ 환경에서의 성능 분석을 통해 프로토콜이 최상의 성능을 보이기 위한 파라메터 값을 찾았고, 이 값을 적용하였을 때의 성능을 기존의 프로토콜과 비교하여 제안된 프로토콜이 더 우수함을 확인하였다.Abstract While a system working with SAN, such as ATM Clustering System, requires a network with low latency and wide bandwidth, the previous error recovery protocols have a serious network overhead to satisfy this requirement. The suggested error recovery protocol is a light-weight protocol which can shows its best performance at ATM Clustering System and uses a newly suggested adaptive acknowledgement scheme. In the adaptive acknowledgement scheme, the period of acknowledgement is dynamically changed depending on the state of the network. We proved the logical correctness of our protocol with SDT and did performance analysis with DEVSim++. From the analysis, we found the optimal parameter values for best performance and showed that our protocol works better than the previous error recovery protocols.

• 전기학회논문지
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• 제66권7호
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• Computers and Concrete
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• 제21권6호
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• 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.