• Title/Summary/Keyword: robust learning

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The Impact of Industry Architectures and Supply Chains on Successful Expansion in Emerging Markets (산업구조와 가치사슬이 신흥국 진출 성공에 미치는 영향)

  • Oh, Jae Ho;Park, Kwang Ho
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.43 no.3
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    • pp.29-40
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    • 2020
  • Korean firms have been vigorously searching and exploring overseas market opportunities through export and overseas investment. As of end of 2019, there were more than 80,000 Korean overseas subsidiaries all over the world. With Korean overseas direct investment increasing recently, it became one of the important issues for overseas investors to be successful in the global market. There are a lot of studies on factors influencing the performance of overseas subsidiaries such as 'firm' and 'country' factors. This study empirically examines subsidiary performance determinants with 'industry architectures' by using a sample of 292 overseas Korean firm subsidiaries. Industry architectures are the stable but evolving sets of rules and roles through which labor is divided within a sector. This article considers how industry architectures shape success in international expansion. Industry architectures differ between countries, are not necessarily technologically determined, shape firms' capabilities and their competitive environment, and constitute a distinct level of analysis. We extract antecedents of related theory and empirically test its impact with a survey of Korean firms expanding in emerging economies. We would say this is the first study which tries to focus on industry architectures with the performance of Korean overseas subsidiaries. We find that separability and similarity of industry architectures across countries and localization of subsidiaries are robust and important predictors of success in international expansion. Our results suggest that industry architectures should be added to firm and country as an intermediate level of analysis that helps explain success in international expansion. While we established a pattern, much more remains to be done. We focus on the success of foreign operations, but we do not consider the broader benefits of going abroad, such as the learning or network effects that accrue at the level of the entire firm. The next obvious question is whether the results would differ in the developed market context. These we leave for future research to consider.

A Study on Robust Moving Target Detection for Background Environment (배경환경에 강인한 이동표적 탐지기법 연구)

  • Kang, Suk-Jong;Kim, Do-Jong;Bae, Hyeon-Deok
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.5
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    • pp.55-63
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    • 2011
  • This paper describes new moving target detection technique combining two algorithms to detect targets and reject clutters in video frame images for surveillance system: One obtains the region of moving target using phase correlation method using $N{\times}M$ sub-block images in frequency domain. The other uses adaptive threshold using learning weight for extracting target candidates in subtracted image. The block region with moving target can be obtained using the characteristics that the highest value of phase correlation depends on the movement of largest image in block. This technique can be used in camera motion environment calculating and compensating camera movement using FFT phase correlation between input video frame images. The experimental results show that the proposed algorithm accurately detects target(s) with a low false alarm rate in variety environment using the receiver operating characteristics (ROC) curve.

Calculation of Detector Positions for a Source Localizing Radiation Portal Monitor System Using a Modified Iterative Genetic Algorithm

  • Jeon, Byoungil;Kim, Jongyul;Lim, Kiseo;Choi, Younghyun;Moon, Myungkook
    • Journal of Radiation Protection and Research
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    • v.42 no.4
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    • pp.212-221
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    • 2017
  • Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

Tracking Control for Robot Manipulators based on Radial Basis Function Networks

  • Lee, Min-Jung;Park, Jin-Hyun;Jun, Hyang-Sig;Gahng, Myoung-Ho;Choi, Young-Kiu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • v.9 no.1
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    • pp.285-288
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    • 2005
  • Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose a neuro-adaptive controller for robot manipulators using the radial basis function network(RBFN) that is a kind of a neural network. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between the actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that the parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed neuro-adaptive controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

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A Design of Du-CNN based on the Hybrid Machine Characters to Classify Target and Clutter in The IR Image (적외선 영상에서의 표적과 클러터 구분을 위한 Hybrid Machine Character 기반의 Du-CNN 설계)

  • Lee, Juyoung;Lim, Jaewan;Baek, Haeun;Kim, Chunho;Park, Jungsoo;Koh, Eunjin
    • Journal of the Korea Institute of Military Science and Technology
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    • v.20 no.6
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    • pp.758-766
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    • 2017
  • In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.

Recognition of Unconstrained Handwritten Numerals using Modified Chaotic Neural Networks (수정된 카오스 신경망을 이용한 무제약 서체 숫자 인식)

  • 최한고;김상희;이상재
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.1
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    • pp.44-52
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    • 2001
  • This paper describes an off-line method for recognizing totally unconstrained handwritten digits using modified chaotic neural networks(MCNN). The chaotic neural networks(CNN) is modified to be a useful network for solving complex pattern problems by enforcing dynamic characteristics and learning process. Since the MCNN has the characteristics of highly nonlinear dynamics in structure and neuron itself, it can be an appropriate network for the robust classification of complex handwritten digits. Digit identification starts with extraction of features from the raw digit images and then recognizes digits using the MCNN based classifier. The performance of the MCNN classifier is evaluated on the numeral database of Concordia University, Montreal, Canada. For the relative comparison of recognition performance, the MCNN classifier is compared with the recurrent neural networks(RNN) classifier. Experimental results show that the classification rate is 98.0%. It indicates that the MCNN classifier outperforms the RNN classifier as well as other classifiers that have been reported on the same database.

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Mobile Finger Signature Verification Robust to Skilled Forgery (모바일환경에서 위조서명에 강건한 딥러닝 기반의 핑거서명검증 연구)

  • Nam, Seng-soo;Seo, Chang-ho;Choi, Dae-seon
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.5
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    • pp.1161-1170
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    • 2016
  • In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.

A Korean speech recognition based on conformer (콘포머 기반 한국어 음성인식)

  • Koo, Myoung-Wan
    • The Journal of the Acoustical Society of Korea
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    • v.40 no.5
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    • pp.488-495
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    • 2021
  • We propose a speech recognition system based on conformer. Conformer is known to be convolution-augmented transformer, which combines transfer model for capturing global information with Convolution Neural Network (CNN) for exploiting local feature effectively. The baseline system is developed to be a transfer-based speech recognition using Long Short-Term Memory (LSTM)-based language model. The proposed system is a system which uses conformer instead of transformer with transformer-based language model. When Electronics and Telecommunications Research Institute (ETRI) speech corpus in AI-Hub is used for our evaluation, the proposed system yields 5.7 % of Character Error Rate (CER) while the baseline system results in 11.8 % of CER. Even though speech corpus is extended into other domain of AI-hub such as NHNdiguest speech corpus, the proposed system makes a robust performance for two domains. Throughout those experiments, we can prove a validation of the proposed system.

Recognition of Occluded Face (가려진 얼굴의 인식)

  • Kang, Hyunchul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.6
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    • pp.682-689
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    • 2019
  • In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

Small/Fast Moving Target Tracking base on Correlation Filter in Clutter Environment (클러터 환경에서 correlation filter기반 소형 고속 이동 표적 추적 시스템)

  • Jung, Young-Giu;Sun, Sun-Gu;Lee, Eui-Hyuk;Joo, Yong-Kwan;Kim, Taewan;Lee, Young-Cheol
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.4
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    • pp.93-98
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    • 2019
  • On today, optical system are the next generation weapon systems being studied in many countries, starting from USA. One of the most important technologies in optical system is a high-speed automatic target tracking system that can continuously track high-speed moving small targets. This paper designs an automatic target tracking system based on a correlated trekker that is robust against rapid shape changes for fast moving targets and small targets at a distance. The proposed system showed about 98% success rate in response to the targets that are under a complex background such as drone, ranger, etc.