• 한국정보과학회논문지:정보통신
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• 제31권6호
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• pp.633-642
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• 2004

최근 유비쿼터스 컴퓨팅 환경의 등장에 따라, 사용자의 자주 변화하는 제약조건에 맞는 서비스를 제공하기 위해서 망 내에서의 문맥인식성을 고려한 자치적응성(self-adaptation)이 요구되고 있다 그러나 기존 망에 자치적응성 기능을 추가하고자 할 때 느린 망 표준화 문제와 느린 서비스 재배치 문제가 발생한다. 액티브네트워크는 이러한 문제점을 해결하기 위한 적합한 환경으로써 망에 새로운 자치 적응성을 추가하고 빠른 재배치를 할 수 있다. 그러므로 본 논문은 문맥인식성을 고려한 자치적응성 지원을 위해서 에이전트 기반 액티브네트워크와 제약조건 기반 SCE(Service Creation Environment)클 이용한 SAS(Self Adaptation Service)를 제안하였다. 본 SAS의 이점은 망 내에서 문맥인식(context-aware)을 고려한 서비스 지원과 빠른 서비스 재배치 지원이다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 1996년도 10월 학술대회지
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• pp.464-467
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• 1996

This paper aims to exploit inter/intra-speaker phoneme sub-class variations as criteria for adaptation in a phoneme recognition system based on a novel neural network architecture. Using a subcluster neural network design based on the One-Class-in-One-Network (OCON) feed forward subnets, similar to those proposed by Kung (2) and Jou (1), joined by a common front-end layer. the idea is to adapt only the neurons within the common front-end layer of the network. Consequently resulting in an adaptation which can be concentrated primarily on the speakers vocal characteristics. Since the adaptation occurs in an area common to all classes, convergence on a single class will improve the recognition of the remaining classes in the network. Results show that adaptation towards a phoneme, in the vowel sub-class, for speakers MDABO and MWBTO Improve the recognition of remaining vowel sub-class phonemes from the same speaker

• 한국컴퓨터정보학회논문지
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• 제9권3호
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• pp.149-155
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• 2004

본 논문은 응용수준 액티브네트워크(ALAN)상에서 자치 적응성(Self-Adaptation) 컨텐츠 서비스 네트워크(CSN)에 관하여 언급하였다. 최근에는 컨텐츠 전송에 초점을 둔 CDN(Content Delivery Network)기술과 더불어 서비스 전송에 초점을 둔 CSN(Content Service Network)이 등장하였다. 그리고 CSN과 관련하여 IETF(Internet Export Task Force)에서는 OPES(Open Pluggable Edge Service)표준의 표준화가 진행 중에 있다. 그러나 최근에 유비쿼터스 컴퓨팅 환경의 도래와 함께 망에서는 사용자의 자주 변화하는 제약조건에 맞는 서비스를 제공하기 위한 자치적응성(self-adaptation)이 요구되고 있다. 기존 CDN/CSN는 자치 적응성의 고려가 부족하다. 왜냐하면 기존 망이 이러한 기능을 현재 지원할 수 없기 때문이다. 그러므로 본 논문에서는 이러한 문제를 해결하기 위해서 망에서 기능성 제공이 가능한 액티브네트워크 상의 자치적응성 CSN의 구조를 제안하였다.

• 센서학회지
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• 제30권5호
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• ETRI Journal
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• 제39권5호
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• pp.695-706
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• 2017

This paper presents a caching algorithm that offers better reconstructed data quality to the requesters than a probabilistic caching scheme while maintaining comparable network performance. It decides whether an incoming data packet must be cached based on the dynamic caching probability, which is adjusted according to the priorities of content carried by the data packet, the uncertainty of content popularities, and the records of cache events in the router. The adaptation of caching probability depends on the priorities of content, the multiplication factor adaptation, and the addition factor adaptation. The multiplication factor adaptation is computed from an instantaneous cache-hit ratio, whereas the addition factor adaptation relies on a multiplication factor, popularities of requested contents, a cache-hit ratio, and a cache-miss ratio. We evaluate the performance of the caching algorithm by comparing it with previous caching schemes in network simulation. The simulation results indicate that our proposed caching algorithm surpasses previous schemes in terms of data quality and is comparable in terms of network performance.

• 한국정보시스템학회지:정보시스템연구
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• 제27권2호
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• pp.135-155
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• 2018

Purpose Recently, with the enhancement of mobile technologies, people have formed various relationships and spreaded networks on social network service(SNS). In addition, although people make a decision based on the thoughts and emotions about self, there is little empirical research on social relations and self-construal of users in social network game (SNG). Design/methodology/approach This study was designed to examine the structural relationships among SNG users' social capital, self-construal, behavioral adaptation, SNG reuse intention and charged item purchasing intention. Findings The results from this study are as follow. First of all, the bonding social capital did not have a significant impact on behavioral adaptation to SNG, but bridging social capital had a positive impact on behavioral adaptation. Second, independent self-construal did not have a significant impact on behavioral adaptation to SNG, but interdependent self-construal had a positive impact on behavioral adaptation. Lastly, the behavioral adaptation to SNG had a positive impact reuse intention and charged item purchasing intention. Also, SNG reuse intention had a positive impact on charged item purchasing intention.

• 전자공학회논문지B
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• 제32B권5호
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• pp.765-776
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• 1995

This paper describes a speaker adaptation method to improve the recognition performance of MLP(multiLayer Perceptron) based HMM(Hidden Markov Model) speech recognizer. In this method, we use lst-order linear transformation network to fit data of a new speaker to the MLP. Transformation parameters are adjusted by back-propagating classification error to the transformation network while leaving the MLP classifier fixed. The recognition system is based on semicontinuous HMM's which use the MLP as a fuzzy vector quantizer. The experimental results show that rapid speaker adaptation resulting in high recognition performance can be accomplished by this method. Namely, for supervised adaptation, the error rate is signifecantly reduced from 9.2% for the baseline system to 5.6% after speaker adaptation. And for unsupervised adaptation, the error rate is reduced to 5.1%, without any information from new speakers.

• 대한산업공학회지
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• 제26권4호
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• 대한원격탐사학회지
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• 제38권2호
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• pp.199-213
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• 2022

비지도 도메인 적응은 연단위 작물 분류를 위해 매년 반복적으로 양질의 훈련자료를 수집해야 하는 비실용적인 문제를 해결할 수 있다. 이 연구에서는 작물 분류를 위한 딥러닝 기반 비지도 도메인 적응 모델의 적용성을 평가하였다. 우리나라 마늘, 양파 주산지인 합천군과 창녕군을 대상으로 무인기 영상을 이용한 작물 분류 실험을 통해 deep adaptation network (DAN), deep reconstruction-classification network, domain adversarial neural network (DANN)의 3개의 비지도 도메인 적응 모델을 정량적으로 비교하였다. 비지도 도메인 적응 모델의 분류 성능을 평가하기 위해 소스 베이스라인 및 대상 베이스라인 모델로 convolutional neural networks (CNNs)을 추가로 적용하였다. 3개의 비지도 도메인 적응 모델은 소스 베이스라인 CNN보다 우수한 성능을 보였으나, 소스 도메인 영상과 대상 도메인 영상의 자료 분포 간 불일치 정도에 따라 서로 다른 분류 성능을 보였다. DAN의 분류 성능은 두 도메인 영상 간 불일치가 작을 때 다른 두 모델에 비해 분류 성능이 높은 반면에 DANN은 두 도메인 영상 간 불일치가 클 때 가장 우수한 분류 성능을 보였다. 따라서 신뢰할 수 있는 분류 결과를 생성하기 위해 두 도메인 영상의 분포가 일치하는 정도를 고려해서 최상의 비지도 도메인 적응 모델을 선택해야 한다.