• 인지과학
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• 제21권4호
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• pp.603-625
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• 2010

물체의 신경 표상은 시각 피질 전반에 걸쳐 분산되어 있고 다른 물체의 신경 표상과 중첩된 형태로 유지된다. 따라서 특정 물체를 반복적으로 접하면 그 물체의 재인은 촉진되지만 다른 물체의 재인은 상대적으로 손상될 수 있다. 이러한 현상을 각각 반복 점화(priming)와 반점화(antipriming)라고 한다. 본 연구는 반복 반점화 즉, 반복된 물체 자체가 반점화 될 가능성을 검증하였다. 학습 단계에서 참가자들은 다양한 수준으로 화질이 손상된 물체 사진들의 재인 수준을 평정하였고 검사 단계에서는 정상적인 화질의 물체 사진을 보고 범주 판단 과제를 실시하였다. 그 결과, 실험 1과 2에서 모두 학습 단계에서 쉽게 재인되었던 물체는 검사 단계에서 더 효율적으로 처리되었지만(반복 점화), 반대로 학습 단계에서 지각적으로 모호했던 물체는 검사 단계에서 비효율적으로 처리되었다(반복 반점화). 이러한 결과는 지각적으로 모호한 물체를 경험할 때 세부특징에 관한 감각 표상과 다수의 물체 표상들 간의 연결이 강화되어 후속 재인 과정을 방해하기 때문인 것으로 추정된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• 대한음성학회지:말소리
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• 제33_34호
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• pp.91-98
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• 1997

Auditory discrimination which represents a very basic and important perceptual skill in children is a necessary condition for effective learning. It is necessary, therefore, to devise a standardized test tool for a reliable assessment of the auditory discrimination ability of children. The Korean Auditory Discrimination Test(KADT) is a tentative test tool that the authors have devised to meet such demand, i.e., to test the auditory discrimination ability of Korean children, both normal and hearing- and speech-impaired, between the ages of 4 and 8. The KADT consists of 40 pairs of words arranged in a systematic manner, of which thirty are 'minimal pairs' of words and the rest homophonous synonyms. The 30 minimal pairs are composed in such a way that major phonological contrasts involving consonants and vowels at initial, medial and final positions are duly represented. The test score will be determined by the number of right responses made by the children. Further attempts will be made to refine and improve KADT in future.

• 음성과학
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• 제6권
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• pp.7-24
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• 1999

Eleven Korean learners of English took part in an experiment where the production and perception of English /r/ and /l/ in four different word positions was investigated. Overall the subjects made more errors on /l/ in both production and identification tests. The frequency of the subjects' errors was also sensitive to word positions in which the two English liquids occur. Especially the subjects made noticeably fewer errors in intervocalic medial position. It is suggested that the Korean subjects' acquisitional pattern in this particular case of foreign phone learning can be explained more by language particular 'interference' effects rather than 'universal' acoustic arguments such as those given in Dissosway et a1. (1982) and Sheldon and Strange (1982). The results of the experiment also support the minority position among second language educators that in some cases of non-native phone acquisition, learners' production abilities can be developed earlier than their perceptual abilities.

• 영어어문교육
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• 제13권2호
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• pp.129-153
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• 2007

When English language teachers are deciding on their priorities for teaching pronunciation, it is imperative to know what kind of differences and errors are most likely to interfere with communication, and what special problems particular first-language speakers will have with English pronunciation. In other words, phoneme discrimination skill is an integral part of speech processing for the EFL learners' learning to converse in English. Training using sound visualization technique can be effective in improving second language learners' perceptions and productions of segmental and suprasegmental speech contrasts. This study assessed the efficacy of a pronunciation training that provided visual feedback for EFL learners acquiring pitch and durational contrasts to produce and perceive English phonemic distinctions. The subjects' ability to produce and to perceive novel English words was tested in two contexts before and after training; words in isolation and words in sentences. In comparison with an untrained control group, trainees showed improved perceptual and productive performance, transferred their knowledge to new contexts, and maintained their improvement three months after training. These findings support the feasibility of learner-centered programs using sound visualization technique for English language pronunciation instruction.

• 한국CDE학회논문집
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• 제18권3호
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• pp.155-166
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• 2013

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot's perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

• 대한전기학회논문지
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• 제40권2호
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• pp.217-223
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• 1991

Most common applications of neural networks to control problems are the automatic motor controls using the artificial perceptual function. These control mechanisms are similar to those of the intelligent and pattern recognition control of an adaptive method frequently performed by the animate nature. In this paper, the pole-balancing problem is selected as the control object and an actual cart-pole controller is implemented by a computer interfacing and demonstrated as motor control using the reinforcement learning rule. In the experiment, given a change of the main parameters of cart-pole dynamics, a comparison is made between the LQR scheme and neural network method. The neural network method exhibits a more effecftive control action in a real situation having a large uncertainty than the LQR scheme.

• 말소리와 음성과학
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• 제13권4호
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• pp.9-21
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• 2021

본 연구는 한국어 평음과 기음 대조에 초점을 맞춘 지각 훈련을 통해 한국어 학습자들이 L2 폐쇄음을 대조하는 음향 신호의 지각 가중치를 모국어 값의 방향으로 개선하는지 고찰하였다. 중국어가 모국어인 한국어 학습자 19명과 지각 훈련을 진행한 한국어가 모국어인 교사 2명이 실험에 참가하였다. 사전 테스트 결과에 따라 훈련 집단과 비훈련 집단으로 나누었고, 훈련 집단만 5일 동안 지각 훈련을 진행하였다. 폐쇄음 신호의 지각 가중치를 추정하기 위해 음향 신호를 체계적으로 조작한 자극으로 사전 및 사후 테스트를 시행했다. 개별 학습자들의 테스트 결과에 대해 이분형 로지스틱 회귀 분석을 시행해, 말소리 대조를 지각하는 데에 해당 음향 신호를 사용한 가중치를 추정하는 지각 β 계수 값을 계산하였다. 두 폐쇄음을 구별하는 주요 신호인 F0의 지각 계수에 대해, 훈련 집단은 사전 테스트 대비 사후 테스트에서 평균 0.451의 통계적으로 유의미한 증가를 보인 반면 비훈련 집단은 유의미하지 않은 0.246의 증가를 나타냈다. 그러나 지각 훈련 후 F0 사용의 변화 패턴이 개별 학습자들 간에 다양하게 나타났다.

• 말소리와 음성과학
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• 제13권4호
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• pp.23-34
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• 2021

이 연구는 이집트인 한국어 학습자를 대상으로 하여 이들이 한국어 모음을 어떻게 지각하고 범주화하며, 이들이 발음한 한국어 모음을 한국인들이 어떻게 지각하는지 밝히고, 이를 토대로 이집트인 학습자들의 한국어 모음 범주화가 그들의 한국어 모음 지각과 산출에 어떤 영향을 미치는지 밝히는 것을 목적으로 한다. 실험 1에서는 이집트인 학습자가 한국어 모음을 어떻게 지각하는지 알아보기 위해 이집트인 학습자 53명을 대상으로 하여 한국인이 발음한 한국어 자극 단어를 듣고 어느 단어를 들었는지 객관식으로 고르는 과제를 수행하게 하였고, 실험 2에서는 이집트인 학습자들이 발음한 한국어 모음을 한국인들이 어떻게 지각하는지 밝히기 위해 이집트인 학습자 9명이 산출한 자극 단어 117(13개×9명)개를 한국인들에게 들려주고, 어느 단어를 들었는지 객관식으로 고르게 한 다음 모음의 발음이 원어민 수준에 얼마나 근접하는지 5점 척도로 평가하도록 하였다. 실험 결과 이집트어에 존재하지 않는 "새로운" 한국어 모음은 별도의 범주를 쉽게 형성하여 잘 지각된 반면 산출이 잘 되는 새로운 모음도 있었고, 산출에서 어려움을 겪는 모음도 있었다. 반면에 이집트어 음소와 "비슷한" 한국어 음소는 비교적 잘 산출되지만 지각하는 데는 큰 어려움이 있다는 사실도 확인할 수 있었다. 이 연구 결과를 토대로 기존의 음성학습모델(speech learning model)과 지각동화모델(perceptual assimilation model)이 제2언어 학습자들의 제2언어 음성 지각을 잘 설명해 주지만 음성 산출을 설명하는 데 미흡함이 있어 이에 대한 보완이 필요함을 논의했다.