• 전기학회논문지
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• 제64권1호
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• pp.113-120
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• 2015

In this paper, we propose a feature extraction method using the stacked autoencoders which consist of restricted Boltzmann machines. The stacked autoencoders is a sort of deep networks. Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. In terms of pattern classification problem, the feature extraction is a key issue. We use the stacked autoencoders networks to extract new features which have a good influence on the improvement of the classification performance. After feature extraction, fuzzy k-nearest neighbors algorithm is used for a classifier which classifies the new extracted data set. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1020-1025
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• 2021

Facial alignment is very important task for human life. And facial landmark detection is one of the instrumental methods in face alignment. We introduce the stacked hourglass networks with transposed convolutional layers for facial landmark detection. our method substitutes nearest neighbor upsampling for transposed convolutional layer. Our method returns better accuracy in facial landmark detection compared to stacked hourglass networks with nearest neighbor upsampling.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.151-160
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• 2024

Artificial Neural Networks that enabled Artificial Intelligence are being used in many fields. However, the application to mechanical structures has several problems and research is incomplete. One of the problems is that it is difficult to secure a large amount of data necessary for learning Artificial Neural Networks. In particular, it is important to detect and recognize external forces and forces for safety working and accident prevention of mechanical structures. This study examined the possibility by applying the Current Neural Network of Artificial Neural Networks to detect and recognize the load on the machine. Tens of thousands of data are required for general learning of Recurrent Neural Networks, and to secure large amounts of data, this paper derives load data from ANSYS structural analysis results and applies a stacked auto-encoder technique to secure the amount of data that can be learned. The usefulness of Stacked Auto-Encoder data was examined by comparing Stacked Auto-Encoder data and ANSYS data. In addition, in order to improve the accuracy of detection and recognition of load data with a Recurrent Neural Network, the optimal conditions are proposed by investigating the effects of related functions.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제24권3호
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• pp.113-121
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• 2018

포지션 인코딩은 문장 내 등장하는 단어의 위치에 따라 가중치를 적용하는 방법이다. 포인터 네트워크는 입력열에 대응되는 위치를 출력하는 딥 러닝 모델이며, 상호참조해결에 적용될 수 있다. 그러나 포인터 네트워크는 입력열의 길이가 긴 경우에 성능이 저하되는 문제가 있다. 이러한 문제를 해결하기 위하여 본 논문에서는 포지션 인코딩과 동적 포지션 인코딩을 포인터 네트워크에 적용할 것을 제안하고, Encoder RNN의 레이어를 더 깊게 쌓아 높은 수준으로 추상화할 것을 제안하며, 이를 이용한 상호참조해결 모델을 제안한다. 실험 결과, 본 논문에서 제안한 포지션 인코딩 기반 스택 포인터 네트워크 모델이 기존의 포인터 네트워크 모델보다 6.01% 향상된 CoNLL F1 71.78%의 성능을 보였다.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• JSTS:Journal of Semiconductor Technology and Science
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• 제12권2호
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• pp.139-149
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• 2012

A 3D stacked IC is made by multiple dies (possibly) with heterogeneous process technologies. Therefore, die-to-die variation in 2D chips renders on-package variation (OPV) in a 3D chip. In spite of the different variation effect in 3D chips, generally, 3D die stacking can produce high yield due to the smaller individual die area and the averaging effect of variation on data path. However, 3D clock network can experience unintended huge clock skew due to the different clock propagation routes on multiple stacked dies. In this paper, we analyze the on-package variation effect on 3D clock networks and show the necessity of a post silicon management method such as body biasing technique for the OPV induced 3D clock skew control in 3D stacked IC designs. Then, we present a parametric yield improvement method to mitigate the OPV induced 3D clock skew.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• 한국지능시스템학회논문지
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• 제11권4호
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• pp.293-301
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• 2001

본 논문은 신경회로망과 같은 경험적 모델에서 출력별로 신뢰 구간을 추정하는 세 가지 대표적인 방법을 검토하고, 검토한 방법을 뉴로-퍼지 모델에 적용하여 장단점을 비교 분석한다. 본 논문에서 고려한 출력별 신뢰 구간 계산 방법은 cross-validation을 이용한 stacked generalization, 회귀 모델에서 유도된 predictive error bar, 지역 표현하는 신경회로망의 특성에 기반한 local reliability measure이다. 간단한 함수 근사화 문제와 혼돈 시계열 예측 문제를 이용하여 모의 실험을 수행하고, 세 가지 신뢰도 추정 방법의 성능을 정량적, 정성적으로 비교 분석한다. 분석 결과를 기초로 각 방법의 장단점 및 특성을 고찰하고, 모델링 문제에서 모델의 출력별 신뢰 구간 계산 방법의 실제 적용 가능성을 탐색한다.