• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.207-208
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• 1998

Classification of extracellularly recorded action potential into each unit is an important procedure for further analysis of spike trains as point process. We utilize feedforward neural network structures, multilayer perceptron and radial basis function network to implement spike classifier. For the efficient training of classifiers, nonlinear energy operator that can trace the instantaneous frequency as well as the amplitude of the input signal is used. Trained classifiers shows successful operation, up to 90% correct classification was possible under 1.2 of signal-to-noise ratio.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.213-216
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• 2021

본 논문에서는 Text Classification에 사용된 딥러닝 모델을 적용하여 행동 인식, 손동작 인식 및 감정 인식 방법을 제안한다. 먼저 라이브러리를 사용하여 영상에서 특징 추출 후 식을 적용하여 특징의 벡터를 저장한다. 이를 Conv1D, Transformer, GRU를 결합한 모델에 학습시킨다. 이 방법을 통해 하나의 딥러닝 모델을 사용하여 다양한 분야에 적용할 수 있다. 제안한 방법을 사용해 SYSU 3D HOI 데이터셋에서 99.66%, eNTERFACE' 05 데이터셋에 대해 99.0%, DHG-14 데이터셋에 대해 95.48%의 클래스 분류 정확도를 얻을 수 있었다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1731-1737
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• 2016

In this paper, a method is proposed to recognize human actions as nonverbal expression; the proposed method is composed of two steps which are action representation and action recognition. First, MHI(Motion History Image) is used in the action representation step. This method includes segmentation based on depth information and generates spatio-temporal templates to describe actions. Second, CNN(Convolution Neural Network) which includes feature extraction and classification is employed in the action recognition step. It extracts convolution feature vectors and then uses a classifier to recognize actions. The recognition performance of the proposed method is demonstrated by comparing other action recognition methods in experimental results.

• Korean Security Journal
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• no.3
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• pp.145-174
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• 2000

Protection activity is divided by the security measures of preventive action and immediately protective reaction in case of emergency situation. The purpose of this study is to emphasis the importance of prevention when providing security for protectees. What I suggested in this thesis is summarized below. Chapter I which sets out purpose, concept, general remarks are followed 3 steps for conducting security action by Chapter II. Chapter III concerns the classification, security technic of preventive action. Classified involving security information, security action, security measures. It is followed site survey, security plan, detailed procedures, coordinative meeting, previous security action, protective action, review meeting by protective technic. Chapter IV consider effective counter plan method of preventive action. Chater V, conclusion.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.155-162
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• 2020

Recently, with the development of computer vision and deep learning technology, research on human action recognition has been actively conducted for video analysis, video surveillance, interactive multimedia, and human machine interaction applications. Diverse techniques have been introduced for human action understanding and classification by many researchers using RGB image, depth image, skeleton and inertial data. However, skeleton-based action discrimination is still a challenging research topic for human machine-interaction. In this paper, we propose an end-to-end skeleton joints mapping of action for generating spatio-temporal image so-called dynamic image. Then, an efficient deep convolution neural network is devised to perform the classification among the action classes. We use publicly accessible UTD-MHAD skeleton dataset for evaluating the performance of the proposed method. As a result of the experiment, the proposed system shows better performance than the existing methods with high accuracy of 97.45%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1278-1281
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• 2022

현재 Action classification model은 computational resources의 제약으로 인해 video전체의 frame으로 학습하지 못한다. Model에 따라 다르지만, 대부분의 경우 하나의 action을 학습시키기 위해 보통 많게는 32frame, 적게는 8frame으로 model을 학습시킨다. 본 논문에서는 이 한계를 극복하기 위해 하나의 video의 많은 frame들을 mix-up과정을 거쳐 한장의 frame에 여러장의 frame 정보를 담고자 한다. 이 과정에서 video의 시간에 따른 변화(temporal- dynamics)를 손상시키지 않기 위해 linear mix-up이라는 방법을 제안하고 그 성능을 증명하며, 여러장의 frame을 mix-up시켜 모델의 성능을 향상시키는 가능성에 대해 논하고자 한다.

• Journal of Korean Elementary Science Education
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• v.27 no.1
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• pp.9-22
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• 2008

The purpose of this study was to investigate the type of classification criterion and the characteristic of classification strategy that appear in pre-service elementary teachers' classification activity. The 4 tasks were developed for classification activity; button as a real things that attribute is prominent, shell as a real things that attribute is less prominent, snow flake as a picture cards that attribute is prominent, and galaxy as a picture cards that attribute is less prominent. The 5 college students who major in elementary education were selected. Data were collected by interview with participants, participants' classification recording paper, investigator's observation of participants' action observation, and videotaped that record participants' subject classification process. Result proved in this study is as following. First, pre-service elementary teachers used 4 qualitative classification criterion of feature, random field, image and secondary property, and used 2 dimension classification criterion of space and quantity. They used single quality classification criterion or combining dimension classification criterion in classification activity. Second, pre-service elementary teachers have classification strategy that apply each various classification criterion, and also classification strategy are different according to subject, but discussed that "anchor" and "priming effect" are important for effective classification. Result of this study is expected to contribute classification research and classification teaching program development.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1851-1863
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• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.