• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.942-943
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• 2021

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• ETRI Journal
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• 제36권5호
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.

• 농촌계획
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• 제5권1호
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• pp.56-65
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• 1999

The purpose of this study was to identify remained real state of the village groves in human settlement circle. That was practiced in case of Chinan-Gun region which traditional elements had well been conserved. 33 village groves were found by site survey, reference and interview in Chinan-Gun region. 31 of 51 village groves were clarified as complementing village grove by classification of grove character. It was identified through survey that many were partially destructed by development and human overuse. The results of this study showed general, socio-behavioral characteristics, characteristics of forest state and vegetation structure of village groves in Chinan-Gun region. Length, area, form, type, motive, location, relationship of those were analyzed to identify general characteristics. Facilities, human behavior and ownership of those were analyzed to identify socio-behavioral characteristics. Principal dominant species and appearing rate, height, width, density of those, species diversity of groves were analyzed to identify forest state and vegetation structure. Interrelation between each factor were analyzed and comparative review with previous studies was achieved.

• Fashion, Industry and Education
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• 제14권2호
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• pp.24-34
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• 2016

This research examined two decades of the U.S. used clothing exports to the world. All countries (209) were classified into four groups based on the level of economic development. Between 1996 and 2012, U.S. used clothing exports shifted away from low-income economies to high-income economies. For the first time, our research demonstrated that the majority of used clothing discarded by American consumers is exported to high-income economies instead of poorest nations of the world. Next, used clothing exports and imports by volume and value in seven high-income countries were analyzed. The high-income countries not only exported but also imported significant amount of used clothing, which indicates a growing demand for worn apparel in developed nations. The demand might be at least partially attributed to the popular vintage clothing trend and increasing consumer environmentalism. Implications regarding development and implementation of a new classification system of worn clothing and recommendations for future research are presented.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 가을 학술발표회논문집
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• pp.159-165
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• 2000

A new state evaluation approach for structural safety is presented in this study. To reduce the subjectivity of the view and judgement of each expert founded on a limited body of knowledge in cognitive and inferential process of safety assessment, we introduced inductive learning method in AI. Inductive learning derives generalization from experiences. Decision tree induction algorithm analyzes the domain knowledge, produce rules via decision trees and then allow us to determine the classification of an object from case examples. The training set of state evaluation is constructed according to the selected attributes from working reports of RC bridge girders.

• 전자공학회논문지C
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• 제34C권6호
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• pp.79-88
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• 1997

New technologies, systems, and services in telecommunication have increased the need for an efficient and robust control mechanism to protect switching systems from overload. To achieve proper control, it is necessary to find a set of parameters that can describe the system. However, it is difficult to find types of data that can form a suitable basis for control. In this paper, we categorize the load status of a switching system into three classes (i.e., normal state, pre-overload state, and overload state) and formulate the overload detection as a classification problem. We find the relationships between the load classes and a set of monitored switching system parameters by applying a fuzzy rule-generation method. The rules are automatically generated from training data. Simulation results involving a switching system is given.

• 한국조경학회지
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• 제26권3호
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• pp.152-161
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• 1998

The purpose of this study was to identify remained real state of groves of enclosed village in human settlement circle. That was practiced in case of Chinan-Gun region which traditional elements had well been conservated. 48 village groves were found by site survey, reference and interview in Chinan-Gun region. 27 groves of 48 village groves were clarified as complementing village grove by classification of grove character. It was identified through survey that many were partially destructed by development and human use. The results of this study showed general, socio-behavioral characteristics, characteristics of forest state and vegetation structure of complementing village groves. Length, area, form, type, motive, location, relationship of those were analyzed to identify general characteristics. Facilities, human behavior and ownership of those were analyzed to identify socio-behavoral characteristics. Dominent species, appearing rate, height, width, density and biodiversity of upper trees were analyzed to identify forest state and vegetation structure. Interrelation of each factor were analiged and comparative review with previous studies was achieved.

• 한국정보기술학회 영문논문지
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• 제8권2호
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• pp.27-39
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• 2018

In this paper, we describe the algorithms, EEG classification methods, and position data analysis methods using EEG and ADS1299 sensors. In addition, it is necessary to manage the amount of real-time data of location data and EEG data and to extract data efficiently. To do this, we explain the process of extracting important information from a vast amount of data through a cloud server. The electrical signals extracted from the brain are measured to determine the psychological state and health status, and the measured positions can be collected using the position sensor and triangulation method.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.173-178
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• 2021

The article reveals the essence of the concept of "pedagogical innovation" and identifies trends in the development of a modern educational institution. A qualitative analysis of the scientific literature in the field of innovation science has been carried out. The essence of the concept of "pedagogical innovation" is revealed. The modern classification of pedagogical innovations is given. The factors of the success of the introduction of pedagogical innovations are determined. The main trends in the development of modern educational institutions are outlined.