• 대한의용생체공학회:의공학회지
• /
• 제29권1호
• /
• pp.25-31
• /
• 2008

It is well known that temporal information, i.e speech onset, about input speech can be represented to the response nerve signal of auditory nerve better depending on the adaptation effect occurred in the auditory nerve synapse. In addition, the performance of a speech processor of cochlear implant can be improved by the adaptation effect. In this paper, we observed the emphasis characteristic of speech onset in the recently proposed adaptation algorithm, analyzed the characteristic of performance change according to the variation of parameters and compared with transient emphasis spectral maxima (TESM) is the previous typical strategy. When observing false peaks which are generated everywhere except speech onset, in the case of the proposed model, the false peak were generated much less than in the case of the TESM and it is more distinguishable under noise.

• ETRI Journal
• /
• 제28권2호
• /
• pp.155-161
• /
• 2006

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.

• Journal of Information Science Theory and Practice
• /
• 제1권1호
• /
• pp.10-26
• /
• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

• 제어로봇시스템학회논문지
• /
• 제6권1호
• /
• pp.8-14
• /
• 2000

In this paper, three adaptive sliding mode control algorithms, which self-tune both the sliding mode gain and the boundary layer thickness, are proposed. The first algorithm uses a gain adaptation rule is combined with the boundary layer thickness adaptatioin rule to satisfy the sliding condition. In the third algorithm, the computation burden of the second algorithm is reduced further, and therefore no extra cost is required for real-time implementation. Due to the mixed sliding mode gain and the boundary layer thickness adaptation scheme, the tracking error and the chattering of the control input can be reduced greatly.

• 한국컴퓨터정보학회논문지
• /
• 제24권4호
• /
• pp.45-50
• /
• 2019

Recently, character motion have been used extensively in the entertainment business, and researchers have investigated algorithms of reproducing, editing, and simulating mimic human movements. Also, many recent researches have suggested how a character interacts with its surrounding environment in terms of motion. Specially, spatial relationships of the environment have been introduced for adapting and preserving character motion. In this paper, we propose a motion adaptation technique preserving a spatial property between a virtual character and the configuration of its surrounding space. Additionally, we report on experimental results of smoothly adapted motions in various environmental structures with original motions such as walk, jump, and tumbling.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권4호
• /
• pp.626-648
• /
• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• 한국정밀공학회지
• /
• 제12권9호
• /
• pp.95-103
• /
• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• 대한전자공학회논문지
• /
• 제20권6호
• /
• pp.11-17
• /
• 1983

본 연구는 explicit M.K.A.C. 알고리즘으로 마이크로프로세서를 사용하여 직류전동기 속도 제어를 하였다. 실험에 사용된 적응 제어 알고리즘으로는 먼저 지수함수적 가중 최소사승법 (exponentially wighted least square method: E.W.L.S) 알고리즘의 계산상의 불안정과 수행시간을 최소로 하기 위하여 시간 지연이 있는 E.W.L.S.알고리즘에 EDUt-_인수화법을 도입한 UDUt-인수화법 알고리즘을 사용하였고, 또한 gradient-type의 알고리즘으로도 초기에 dtatl-zollr을 갖는 직류전동기를 효과적으로 제어하기 위하여 gradient-type의 알고리즘에 smoothing polynomal과 상수ℓ을 사용한 SM gradient-쇼pe의 알고리즘을 제안하였다. UDTt인수화법 알고리즘을 적용한 결과 수행시간이 단축되었으며, SM gradient-type의 알고리즘의 경우는 dead-zone을 위한 기동전압을 사용하지 않고도 효과적인 속도제어를 할 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권7호
• /
• pp.2785-2799
• /
• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

• 정보과학회 논문지
• /
• 제44권1호
• /
• pp.95-106
• /
• 2017

최근 높은 품질의 멀티미디어 콘텐츠를 제공하고 급증하는 트래픽을 처리하기 위해 HTTP 적응적 스트리밍이 등장하였다. HTTP 적응적 스트리밍 서비스의 QoE (Quality of Experience; 사용자 체감 품질)를 향상시키기 위해서는 평균적인 비디오 품질을 최대화해야 하며 품질 변경 주기, 품질 감소 폭, 그리고 끊김 현상을 최소화해야 한다. 최근 제안되었던 HTTP 적응적 스트리밍의 품질 조절 알고리즘들은 QoE 관련 특성을 세밀하게 고려하지 않아 높은 QoE를 제공하기 어렵다. 본 논문에서는 HTTP 적응적 스트리밍의 QoE 향상을 위해 SQA (Smooth Quality Adaptation) 알고리즘을 제안한다. 제안하는 알고리즘은 일정 기간 품질을 유지하고 비디오 품질의 급격한 감소를 방지함으로써 부드러운 사용자 체감 품질을 제공한다. 시뮬레이션을 통해 제안하는 알고리즘이 대역폭 감소 구간에서 품질 변경 폭을 감소시키고 품질 변경을 최소화하는 것을 확인하였다.