• International journal of advanced smart convergence
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• 제8권4호
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• pp.75-81
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• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.

• 디지털산업정보학회논문지
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• 제10권2호
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• pp.203-211
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• 2014

In recent highly competitive business environment, collaboration has become one of the important business strategies for companies to survive and/or prosper. There are many different types of collaboration strategies, and it is crucial for companies to select the right ones according to the types of collaboration they require. To select the right type of collaboration options for business, in the past research, there have been two important criteria to classify collaboration types, namely governance (who makes key decisions - one kingpin participant or all players?) and membership (can anyone participate, or just select players?). In this research, we add a new classification criterion, recognition of collaboration purpose, which means whether collaborators know or do not know the purpose of collaboration in advance. Recently, we see many cases in which social media data are used in many unknown purposes a priori. In this research, we add such cases to develop new classification model.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.769-782
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• 2022

A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1524-1534
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• 2022

This study proposes the classification model of Minhwa genre based on object detection of deep learning. To detect unique Korean traditional objects in Minhwa, we construct custom datasets by labeling images using object keywords in Minhwa DB. We train YOLOv5 models with custom datasets, and classify images using predicted object labels result, the output of model training. The algorithm consists of two classification steps: 1) according to the painting technique and 2) genre of Minhwa. Through classifying paintings using this algorithm on the Internet, it is expected that the correct information of Minhwa can be built and provided to users forward.

• 한국음향학회지
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• 제39권6호
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• pp.615-621
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• 2020

본 논문은 다중 관측소에서 측정된 지진 신호를 이용한 그래프 합성곱 신경망 기반 지진 이벤트 분류 방법을 제안한다. 기존의 딥러닝 기반 지진 이벤트 분류 방법은 대부분 단일 관측소에서 측정된 신호로부터 지진 이벤트를 분류한다. 지진 관측망에는 수많은 지진 관측소가 존재하며 하나의 관측소만 사용하는 방법보다 여러 관측소의 정보를 동시에 활용하는 방법이 지진 이벤트 분류 성능 향상을 이끌 수 있다. 본 논문에서는 단일 관측소에서 측정된 지진 신호들에 합성곱 신경망을 적용해 임베딩 특징을 추출한 후 그래프 합성곱 신경망을 이용해 단일 관측소들 사이의 정보를 융합하는 다중 관측소 기반 지진 이벤트 분류 구조를 제안한다. 관측소의 개수 변화 등 다양한 실험을 통해 제안한 모델의 성능 검증을 수행하였으며 실험 결과 제안하는 모델이 단일 관측소 기반 분류 모델보다 약 10 % 이상의 정확도와 이벤트 재현율 성능 향상을 보여주었다.

• 대한안전경영과학회지
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• 제20권1호
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• pp.41-55
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• 2018

For several decades, attribute classification methods using the asymmetrical relationship between an attribute performance and the satisfaction of that attribute have been explored by numerous researchers. In particular, the Kano model, which classifies quality attributes into 5 elements using simple questionnaire and two-dimensional evaluation table, has gained popularity: Attractive, One-dimensional, Must-be, Indifferent, and Reverse quality. As Kano's model is well accepted, many literatures have introduced categorization methods using the Kano's evaluation table at attribute level. However, they applied different terminologies and classification criteria and this causes confusion and misunderstanding. Therefore, a criterion for quality classification at attribute level is necessary. This study is aimed to suggest a new attribute classification method that sub-categorizes quality attributes using 5-point ordinal point and Kano's two-dimensional evaluation table through an extensive literature review. For this, the current study examines the intrinsic and extrinsic problems of the well-recognized Kano model that have been used for measuring customer satisfaction of products and services. For empirical study, the author conducted a comparative study between the results of Kano's model and the proposed method for an e-learning case (33 attributes). Results show that the proposed method is better in terms of ease of use and understanding of kano's results and this result will contribute to the further development of the attractive quality theory that enables to understand both the customers explicit and implicit needs.