• Journal of IKEEE
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• v.21 no.3
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• pp.244-247
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• 2017

In this paper, we implemented a convolution neural network using GP-GPU. After defining the structure, CNN performed inferencing using the GP-GPU with 256 threads, which was the previous study, using the weight obtained from the training. Training used Intel i7-4470 CPU and Matlab. Dataset used Daimler Pedestrian Dataset. The GP-GPU is controlled by the PC using PCIe and operates as an FPGA. We assigned a thread according to the depth and size of each layer. In the case of the pooling layer, we used over warpping pooling to perform additional operations on the horizontal and vertical regions. One inferencing takes about 12 ms.

• Iberoamérica
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• v.12 no.1
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• pp.407-439
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• 2010

Growth of researches addressing cross-culture in international business is exponential. This article reviews the extant researches around the national culture and describes the various conceptualization of culture through discussion of some of popular models of national culture. This article presented some of the most important issues in international business surrounding globalization, especially convergence and divergence of cultures and cultural changes. Global rapid changes in international business environment request the reconsideration of the assumption of cultural stability and the simple view of culture, which tends to examine the static influence of a few cultural factors in isolation form other cultural factors and contextual elements. This paper identifies a valid cultural grouping and proposes the following typology of the possible methodologies in international business; Ethnological description, Use of proxies, Direct values inference, and Indirect values inference. Rather than selecting a single methodology, it appears to be more appropriate to use multi-method in the cross-cultural international business research. It has been shown that cultural change is intertwined with socioeconomic-institutional variables, and that these variables may also add to determine culture contemporarily. This paper also explained the dynamics of culture as multi-level, multi-layer constructs. According to this model, we may understand how the dynamic nature of culture conveys the top-down-bottom-up processes where one cultural level affects changes in other level of culture.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.842-849
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• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1869_1870
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• 2009

본 논문에서는 다항식 기반 Type-2 Fuzzy Neural Networks(T2FNN)를 설계하고 이를 패턴분류 문제에 적용하여 그 성능을 분석한다. T2FNN은 Fuzzy C-Means(FCM)을 Type-2 Fuzzy C-Means로 확장시킨 것이라 할 수 있으며, Input layer, Fuzzyification layer, Inference layer, Deffuzification layer의 4층 네트워크로 구성된다. interval Type-1 퍼지 집합인 후반부의 연결가중치는 Gradient Descent Method를 이용하여 학습한다. 제안된 RBF 신경회로망은 모의데이터와 패턴인식 성능 평가에 많이 사용되는 machine learning 데이터에 적용하여 패턴 분류기로서의 성능을 평가받는다.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.373-377
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• 2017

This paper presents a voice-activity detection (VAD) method for sound sequences with various SNRs. For real-time VAD applications, it is inadequate to employ a post-processing for the removal of burst clippings from the VAD output decision. To tackle this problem, building on the bi-level hidden Markov model, for which a state layer is inserted into a typical hidden Markov model (HMM), we formulated a robust method for VAD not requiring any additional post-processing. In the method, a forward-inference-ratio test was devised to detect the speech endpoints and Mel-frequency cepstral coefficients (MFCC) were used as the features. Our experiment results show that, regarding different SNRs, the performance of the proposed approach is more outstanding than those of the conventional methods.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.84-87
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• 1999

This paper presents a preform design method that combines the analytic method and inference of known knowledge with neural network. The analytic method is a finite element method that is used to simulate backward extrusion with pre-defined process parameters. The multi-layer network and back-propagation algorithm are utilized to learn the training examples from the simulation results. The design procedures are utilized to learn the training examples from the simulation results. The design procedures are two methods the first the neural network infer the deformed shape from the pre-defined processes parameters. The other the network infer the processes parameters from deformed shape. Especially the latest method is very useful to design the preform From the desired feature it is possible to determine the processes parameters such as friction stroke and tooling geometry. The proposed method is useful for shop floor to decide the processes parameters and preform shapes for producing sound product.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1046-1049
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• 1998

This paper deals with the new fault detection technique for transmission line using Neuro-fuzzy Scheme. Neuro-fuzzy Scheme is ANFIS(Adaptive-network Fuzzy Inference System) based on fusion of fuzzy logic and neural networks. The proposed scheme has five layers. Each layer is the component of fuzzy Inference system and performs different action. Using learning method of neural network, fuzzy premise and consequent parameters is tuned properly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.45-52
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• 2004

The sliding mode controller with a boundary layer implemented by simplified indirect inference method (SIIM) fuzzy logic was proposed. The components of the sliding line function are used for the inputs of the SIIM fuzzy logic. The proposed control system is simple because there is no need to derive the sigmoid function and there are only four rules. The overall stability of the proposed system and the boundness of the tracking error are proved easily using the Lyapunov theory. We apply the proposed controller to control a nonlinear time-varying system. The computer simulation showed the validity of the proposed control system.