• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.24-29
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• 2014

Digital mammography is the most common technique for the early detection of breast cancer. To diagnose the breast cancer in early stages and treat efficiently, many image enhancement methods have been developed. This paper presents a multi-scale contrast enhancement method in the Laplacian pyramid for the digital mammogram. The proposed method decomposes the image into the contrast measures by the Gaussian and Laplacian pyramid, and the pyramid coefficients of decomposed multi-resolution image are defined as the frequency limited local contrast measures by the ratio of high frequency components and low frequency components. The decomposed pyramid coefficients are modified by the contrast measure for enhancing the contrast, and the final enhanced image is obtained by the composition process of the pyramid using the modified coefficients. The proposed method is compared with other existing methods, and demonstrated to have quantitatively good performance in the contrast measure algorithm.

• Therapeutic Science for Rehabilitation
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• v.9 no.4
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• pp.21-31
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• 2020

Objective : Reminiscence program is a method to restore psychological stability for the elderly having dementia, and at the same time it makes the aged with dementia express themselves correctly by helping them to recollect their past life memories. The purpose of this study was to investigate the effects on communication ability in applying the reminiscence program to elderly patients with Alzheimer's dementia. Methods : The subject were 4 patients whose Alzheimer's dementia of moderate. This experiment was designed with pre-stage, treatment-stage, post-stage. The reminiscence therapy was compose of reminiscence activities of their live; in their childhood, adolescence, adulthood, and senescence. The therapy was delivered 30 times for 15 weeks. Results : The result of the study were as follows. First, after reminiscence therapy, recognition ability was improved. Second, after reminiscence therapy, emotional side was improved. Third, after reminiscence therapy, communication ability was improved. Conclusion : In this study, the reminiscence therapy had a positive effect on the improvement of communication skills among the elderly with Alzheimer's dementia. Based on the reminiscence therapy, it is thought to be very helpful in improving the communication ability of the elderly with dementia in the future.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.249-255
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• 2016

Since the elderly voices include a lot of noise caused by physiological changes in respiration, phonation, and resonance, the performance of the convergence health-care equipments such as speech recognition, synthesis, analysis program done by elderly voice is deteriorated. Therefore it is necessary to develop researches to operate health-care instruments with elderly voices. In this study, a voice activity detection using a symmetric higher-order differential energy function (SHODEO) was developed and was compared with auto-correlation function(ACF) and the average magnitude difference function(AMDF). It was confirmed to have a better performance than other methods in the voice interval detection. The voice activity detection will be applied to a voice interface for the elderly to improve the accessibility of the smart devices.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.146-154
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• 2010

Recently 'convergence' and 'communication' have been keywords in many areas. Artists and engineers have begun to communicate each other through collaboration based on new technologies. One of the exemplary technologies of this era of convergence is a technology of fusing five senses used by both Internet Art and industrial technologies such as car navigation systems and the iPhone. Sommerer and Mignonneau's Internet Art $\ll$Riding the Net$\gg$,$\ll$The Living Room$\gg$, and $\ll$The Living Web$\gg$ implement the Internet and the five-sense fusion technology to translate not only sound into visual images but also tactile senses into tempo-spatial representations. Likewise, industrial technologies such as car navigation systems and the iPhone employ the five-sense fusion technology of speech recognition, which leads to the expansion of the realm of senses in technology as seen in Internet Art. As examined in this study, the development of art and technology through their convergence will open up a new dimension of digital art and culture technology industry.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.91-97
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• 2020

Use of voice-based interfaces that can interact with users is increasing as AI technology develops. To date, however, most of the research on voice-based interfaces has been technical in nature, focused on areas such as improving the accuracy of speech recognition. Thus, the voice of most voice-based interfaces is uniform and does not provide users with differentiated sensibilities. The purpose of this study is to add a emotional factor suitable for the AI interface. To this end, we have derived emotional factors that should be considered in designing voice interface. In addition, we looked at voice trends that differed from country to country. For this study, we conducted interviews with voice industry experts from Finland and Norway, countries that use their own independent languages.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.654-668
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• 2002

In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.197-199
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• 2000

연속음성인식을 수행하기 위해서는 발음사전과 언어모델이 필요하다. 이 둘 사이에는 디코딩 단위가 일치하여야 하므로 발음사전 구축시 디코딩 단위로 표제어 단위를 선정하며 표제어 사이의 음운변화 현상을 반영한 발음사전을 구축하여야 한다. 한국어에 부합하는 음운변화현상을 분석하여 학습용 자동 발음열을 생성하고, 이를 통하여 발음사전을 구축한다. 전처리 단계로 기호, 단위, 숫자 등 전처리 과정 및 형태소 분석 과정을 수행하며, 디코딩 단위인 의사 형태소 단위를 생성하기 위해 규칙을 이용한 태깅 과정을 거친다. 이를 통해 나온 결과를 발음열 생성기 입력으로 하며, 결과는 학습용 발음열 또는 발음사전 구성을 위한 형태로 출력한다. 표제어간 음운변화 현상이 반영된 상태의 표제어 단위이므로 실제 음운변화가 반영되지 않은 상태의 표제어와는 그 형태가 상이하다. 이는 연속 발음시 생기는 현상으로 실제 인식에는 이 음운변화 현상이 반영된 사전이 필요하게 된다. 생성된 발음사전의 효용성을 확인하기 위해 다음과 같은 실험을 통해 성능을 평가하였다. 음향학습을 위하여 PBS(Phonetically Balanced Sentence) 낭독체 17200문장을 녹음하고 그 전사파일을 사용하여 학습을 수행하였고, 발음사전의 평가를 위하여 이 중 각각 3100문장을 사용하여 다음과 같은 실험을 수행하였다. 형태소 태그정보를 이용하여 표제어간 음운변화 현상을 반영한 최적의 발음사전과 다중 발음사전, 언어학적 기준에 의한 수작업으로 생성한 표준 발음사전, 그리고 표제어간의 음운변화 현상을 고려하지 않고 독립된 단어로 생성한 발음사전과의 비교 실험을 수행하였다. 실험결과 표제어간 음운변화 현상을 반영하지 않은 경우 단어 인식률이 43.21%인 반면 표제어간 음운변화 현상을 반영한 1-Best 사전의 경우 48.99%, Multi 사전의 경우 50.19%로 인식률이 5~6%정도 향상되었음을 볼 수 있었고, 수작업에 의한 표준발음사전의 단어 인식률 45.90% 보다도 약 3~4% 좋은 성능을 보였다.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.437-448
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• 2018

Objective: Acute ischemic stroke (AIS) requires time-dependent reperfusion therapy, and early recognition of AIS is important to patient outcomes. This study was conducted to identify the clinical features and risk factors of AIS patients that are missed during the early stages of diagnosis. Methods: We retrospectively reviewed AIS patients admitted to a hospital through the emergency department. AIS patients were defined as ischemic stroke patients who visited the emergency department within 6 hours of symptom onset. Patients were classified into two groups: an activation group (A group), in which patients were identified as AIS and the stroke team was activated, and a non-activation group (NA group), for whom the stroke team was not activated. Results: The stroke team was activated for 213 of a total of 262 AIS patients (81.3%), while it was not activated for the remaining 49 (18.7%). The NA group was found to be younger, have lower initial National Institutes of Health Stroke Scale scores, lower incidence of previous hypertension, and a greater incidence of cerebellum and cardio-embolic infarcts than the A group. The chief complaints in the A group were traditional stroke symptoms, side weakness (61.0%), and speech disturbance (17.8%), whereas the NA group had non-traditional symptoms, dizziness (32.7%), and decreased levels of consciousness (22.4%). Independent factors associated with missed stroke team activation were nystagmus, nausea/vomiting, dizziness, gait disturbance, and general weakness. Conclusion: A high index of AIS suspicion is required to identify such patients with these findings. Education on focused neurological examinations and the development of clinical decision tools that could differentiate non-stroke and stroke are needed.