• Smart Structures and Systems
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• 제29권5호
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• pp.729-746
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• 2022

In this paper, a nonlinear structural model updating methodology based on the Deep Belief Network (DBN) is proposed. Firstly, the instantaneous parameters of the vibration responses are obtained by the discrete analytical mode decomposition (DAMD) method and the Hilbert transform (HT). The instantaneous parameters are regarded as the independent variables, and the nonlinear model parameters are considered as the dependent variables. Then the DBN is utilized for approximating the nonlinear mapping relationship between them. At last, the instantaneous parameters of the measured vibration responses are fed into the well-trained DBN. Owing to the strong learning and generalization abilities of the DBN, the updated nonlinear model parameters can be directly estimated. Two nonlinear shear-type structure models under two types of excitation and various noise levels are adopted as numerical simulations to validate the effectiveness of the proposed approach. The nonlinear properties of the structure model are simulated via the hysteretic parameters of a Bouc-Wen model and a Giuffré-Menegotto-Pinto model, respectively. Besides, the proposed approach is verified by a three-story shear-type frame with a piezoelectric friction damper (PFD). Simulated and experimental results suggest that the nonlinear model updating approach has high computational efficiency and precision.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• Bulletin of the Korean Chemical Society
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• 제16권7호
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• pp.630-634
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• 1995

The cluster distributions for different concentrations of 1,4-dibromonaphthalene (DBN) in 4,4'-dibromobenzophenone (DBBP)/1,4-dibromonaphthalene (DBN) choleic acid were determined by a computer simulation in order to model the energy transfer dynamics. The results of the simulation indicate that long range interaction between molecules further apart than nearest does not occur and energy transfer efficiency is restricted by single range interaction. The results also demonstrate that the trapping is diffusion limited. The energy transfer rate is reduced by a factor of 15 in DBBP/DBN choleic acid realtive to that in DBBP/DBN doped into polystyrene due to the larger distance between molecules.

• 한국정보통신학회논문지
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• 제22권3호
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• pp.418-424
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• 2018

수중 표적 탐지 및 식별은 군사 및 비군사적으로 중요한 문제이다. 최근 패턴인식 분야에서 딥러닝 기술이 발전되면서 많은 성능개선 결과가 발표되고 있다. 그중 DBN(Deep Belief Network)기법은 DNN(Deep Neural Network)을 사전 훈련하는데 사용되어 좋은 성능을 보여주고 있다. 본 논문에서는 능동 소나를 이용한 수중 표적의 식별 문제에 DBN을 사용하여 실험을 진행하고, 그 결과를 비교하였다. 표적신호는 3차원 하이라이트 모델을 사용하여 합성된 능동 소나 신호를 사용하였고, 특징추출 방법으로는 FrFT(Fractional Fourier Transform) 기반의 특징추출을 사용하였다. 단일 센서, 즉, 단일 방위 데이터 기반의 실험에서 DBN을 이용한 식별 결과는 기존의 BPNN(Back Propagation Neural Network)에 비해 약 3.83 % 향상되었다. 또한, 다중 방위 기반의 식별 실험에서는 관측열의 개수가 3을 초과하면 95% 이상의 성능을 얻을 수 있었다.

• International journal of advanced smart convergence
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• 제5권3호
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.543-548
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• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• 전기학회논문지P
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• 제58권2호
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• pp.164-171
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• 2009

This paper presents new learning algorithm of dynamic Bayesian networks (DBN) by means of constrained least square (LS) estimation algorithm and gradient descent method. First, we propose constrained LS based parameter estimation for a Markov chain (MC) model given observation data sets. Next, a gradient descent optimization is utilized for online estimation of a hidden Markov model (HMM), which is bi-linearly constructed by adding an observation variable to a MC model. We achieve numerical simulations to prove its reliability and superiority in which a series of non stationary random signal is applied for the DBN models respectively.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.286-295
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• 2007

This paper presents an intelligent PID control for stochastic systems with nonstationary nature. We optimally determine parameters of a PID controller through learning algorithm and propose an online PID control to compensate system errors possibly occurred in realtime implementations. A dynamic Bayesian network (DBN) model for system errors is additionally explored for making decision about whether an online control is carried out or not in practice. We apply our control approach to traffic control of Transmission Control Protocol (TCP) networks and demonstrate its superior performance comparing to a fixed PID from computer simulations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.681-682
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• 2006

일반적으로 축구 비디오 데이터는 멀티모달과 멀티레이어 속성을 지닌다. 이러한 데이터를 다루기 적합한 모델은 동적 베이지안 네트워크(Dynamic Bayesian Network: DBN) 형태의 위계적 은닉 마르코프 모델(Hierarchical Hidden Markov Model: HHMM)이다. 이러한 HHMM 중 다중속성의 특징들이 서로 상호작용하는 PHHMM(Product Hierarchical Hidden Markov Model)이 있다. 본 논문에서는 PHHMM 을 축구 경기의 Play/Break 이벤트 검색 및 분석에 적용하였고 바람직한 결과를 얻었다.