• 로봇학회논문지
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• 제2권4호
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• pp.297-303
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• 2007

In this paper, we discuss a service to give alarm in the case of emergency for the old and the weak by human behavior recognition in Intelligent Space. Our Intelligent Space consists of mobile robots, sensors and agents. And these components are connected to network framework. Agent analyzes data acquired from networked sensors and determines task of robots and a space to provide a service for humans. In our emergency alarm service, human behavior recognition service module analyzes accelerometer data obtained from body-attached human behavior sensing platform, and classifies into four basic human behavior such as walking, running, sitting and falling-down. For the old and the weak, falling-down behavior may bring about dangerous situations. On such an occasion, agent executes emergency alarm service immediately. And then a selected mobile robot approaches fallen person and sends images of the person to guardians. In this paper, we set up a scenario to verify the emergency alarm service in Intelligent Space, and show feasibility of the service from our simulation experiments.

• Genomics & Informatics
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• 제17권2호
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• pp.17.1-17.3
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• 2019

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

• 한국음향학회지
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• 제38권6호
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• pp.670-677
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• 2019

음성 또는 음향 이벤트 신호에서 발생하는 배경 잡음은 인식기의 성능을 저하시키는 원인이 되며, 잡음에 강인한 특징을 찾는데 많은 노력을 필요로 한다. 본 논문에서는 딥러닝을 기반으로 다중작업 오토인코더(Multi-Task AutoEncoder, MTAE) 와 와설스타인식 생성적 적대 신경망(Wasserstein GAN, WGAN)의 장점을 결합하여, 잡음이 섞인 음향신호에서 잡음과 음성신호를 추정하는 네트워크를 제안한다. 본 논문에서 제안하는 MTAE-WGAN는 구조는 구배 페널티(Gradient Penalty) 및 누설 Leaky Rectified Linear Unit (LReLU) 모수 Parametric ReLU (PReLU)를 활용한 변수 초기화 작업을 통해 음성과 잡음 성분을 추정한다. 직교 구배 페널티와 파라미터 초기화 방법이 적용된 MTAE-WGAN 구조를 통해 잡음에 강인한 음성특징 생성 및 기존 방법 대비 음소 오인식률(Phoneme Error Rate, PER)이 크게 감소하는 성능을 보여준다.

• 말소리와 음성과학
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• 제10권1호
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• pp.33-38
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• 2018

Although deep neural network-based acoustic models have greatly improved the performance of automatic speech recognition (ASR), reverberation still degrades the performance of distant speech recognition in indoor environments. In this paper, we adopt the DenseNet, which has shown great performance results in image classification tasks, to improve the performance of reverberant speech recognition. The DenseNet enables the deep convolutional neural network (CNN) to be effectively trained by concatenating feature maps in each convolutional layer. In addition, we extend the concept of multi-resolution CNN to multi-resolution DenseNet for robust speech recognition in reverberant environments. We evaluate the performance of reverberant speech recognition on the single-channel ASR task in reverberant voice enhancement and recognition benchmark (REVERB) challenge 2014. According to the experimental results, the DenseNet-based acoustic models show better performance than do the conventional CNN-based ones, and the multi-resolution DenseNet provides additional performance improvement.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2002년도 11월 학술대회지
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• pp.137-140
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• 2002

This study was conducted to examine the korean word length effects on auditory word recognition. Linguistically, word length can be defined by several sublexical units such as letters, phonemes, syllables, and so on. In order to investigate which units are used in auditory word recognition, lexical decision task was used. Experiment 1 and 2 showed that syllable length affected response time, and syllable length interacted with word frequency. As a result, in recognizing auditory word syllable length was important variable.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제27권3호
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• pp.207-215
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• 2016

Objectives: This study aimed to investigate the differences in the facial emotion recognition and discrimination ability between children with attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Methods: Fifty-three children aged 7 to 11 years participated in this study. Among them, 43 were diagnosed with ADHD and 10 with ASD. The parents of the participants completed the Korean version of the Child Behavior Checklist, ADHD Rating Scale and Conner's scale. The participants completed the Korean Wechsler Intelligence Scale for Children-fourth edition and Advanced Test of Attention (ATA), Penn Emotion Recognition Task and Penn Emotion Discrimination Task. The group differences in the facial emotion recognition and discrimination ability were analyzed by using analysis of covariance for the purpose of controlling the visual omission error index of ATA. Results: The children with ADHD showed better recognition of happy and sad faces and less false positive neutral responses than those with ASD. Also, the children with ADHD recognized emotions better than those with ASD on female faces and in extreme facial expressions, but not on male faces or in mild facial expressions. We found no differences in the facial emotion discrimination between the children with ADHD and ASD. Conclusion: Our results suggest that children with ADHD recognize facial emotions better than children with ASD, but they still have deficits. Interventions which consider their different emotion recognition and discrimination abilities are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• 인지과학
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• 제19권2호
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• pp.125-142
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• 2008

본 연구는 공간-기반 주의(space-based attention)와 대상-기반 주의(object-based attention)가 공간 및 시각 작업기억에 미치는 영향을 살펴보고자 두 가지 주의 자원 양상이 모두 관여하는 공간 스트룹 과제를 이용하여 작업기억의 재인율을 측정하였다. 작업기억과 공간 스트룹 과제의 자극 배열의 유사성 조건은 작업기억 과제 수행 시 공간 시연에 미치는 공간-기반 주의의 영향을 살펴보고자 구성되었으며, 스트룹 조건은 작업기억 과제 수행시 대상에 대한 시연에 미치는 대상-기반 주의의 영향을 살펴보고자 하였다. 그 결과, 공간 작업기억과 공간 스트룹 과제의 자극 배열의 유사성이 높은 조건에서 공간 작업기억의 재인율이 높은 것으로 나타났으며, 스트룹 조건에 따라서는 차이가 없는 것으로 나타났다. 반면, 시각 작업기억의 재인율은 스트룹 일치조건보다 불일치조건에서 더 저조한 것으로 나타났으며, 유사성 조건에 따라서는 차이가 없는 것으로 나타났다. 이러한 결과는 작업기억에서 요구되는 자원의 양상과 선택적 주의 자원의 양상이 동일한 경우에만 선택적 주의가 작업기억에 영향을 준다는 것을 시사하는 것이다.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.33-40
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• 2020

Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.

• Journal of Biosystems Engineering
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• 제19권2호
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.