• 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.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.899-903
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• 2017

In this paper, it is shown the in-vehicle network architecture and implementation for multimedia service which supports Human machine interface of multi-passengers. For multi-passengers' vehicle, it has to be considered the factor of network traffic, simultaneously data transferring to multi users and accessibility to use variety of media contents for passengers compared to conventional in-vehicle network architecture system Therefore, it is proposed the change of network architecture compared with general MOST network, implementation of designed software module which can be interoperable between ethernet and MOST network and accessible interface that passenger can be plugged into MOST network platform using their device based on ethernet network system.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.31-37
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• 2017

This study was initiated with the aim of suggesting a further step in the program of literary therapy by revealing the mechanism by which the body heals through the discharge of neural network codes. Sentence is encoded as neural signals in our body as it is being read. If the neural networks in the human body are activated and created, the code in which the neural networks are encoded is a code composed of sentences. That is, Sentence is a code. And if the Sentence connects to the human body again and activates the human neural networks, it can be said that Sentence is encoded. At this time, the relation of "neural network codes = Sentence codes" is established. In other words, human narrative and literary narratives are the mediums that convey the same kinds of neural network codes. Cho Ji-Hoon's Poem "White Night" draws sadness through the path of loneliness in 1strophe. Through the Sentence of Loneliness, it activates neural network codes of sadness. 2strophe for the 'pure white snow' is the encoding of the Sentence. In 3strophe, the sentence for 'sadness' is encoded. This flow causes a healing mechanism in this Poem, because the neural network codes about the loneliness, sadness, and eyes of the human body are passed to the other. Here, the other is "White Night". In the future, it is expected that more effective healing results will be obtained if a literary therapy program on the encoding of the sentence of Cho Ji-Hoon's Poem is performed in the future.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.499-501
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• 2004

This paper deals with the implementation of a quadruped working robot using wireless sensor network with TinyOS. It is often required to install real time OS and wireless network in the mobile robot field since robots work alone without human intervention and also exchanging their information between robot systems. The suggested controller utilizes a built-in wireless network OS and makes the variance action related with human-kindly motions for a quadruped walking robot. In addition, a kinematics analysis of its structure and control architecture of robot system is suggested and verified the usefulness through the real experiment.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.454-458
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• 2008

It can improve human being's life quality that all people can have more convenient medical service under pervasive computing environment. For a pervasive health care application for diabetes patient, we've implemented a health care system, which is composed of three parts. Various sensors monitor both outer and inner environment of human such as temperature, blood pressure, pulse, and glycemic index, etc. These sensors form zigbee based sensor network. And medical information server accumulates sensing values and performs back-end processing. To simply transfer these sensing values to a medical team is a low level's medical service. So, we've designed a new service model based on back propagation neural network for more improved medical service. Our experiments show that a proposed healthcare system can give high level's medical service because it can recognize human's context more concretely.

• BMB Reports
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• v.50 no.11
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• pp.535-536
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• 2017

A novel approach has been used to identify functional interactions relevant to human disease. Using high-throughput human-yeast genetic interaction screens, a first draft of disease interactome was obtained. This was achieved by first searching for candidate human disease genes that confer toxicity in yeast, and second, identifying modulators of toxicity. This study found potentially disease-relevant interactions by analyzing the network of functional interactions and focusing on genes implicated in amyotrophic lateral sclerosis (ALS), for example. In the subsequent proof-of-concept study focused on ALS, similar functional relationships between a specific kinase and ALS-associated genes were observed in mammalian cells and zebrafish, supporting findings in human-yeast genetic interaction screens. Results of combined analyses highlighted MAP2K5 kinase as a potential therapeutic target in ALS.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.133-139
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• 2023

As the old saying goes "a picture is worth a thousand words" data visualization is essential in almost every industry. Companies make Data-driven decisions and gain insights from visual data. However, there is a need to investigate the role of data visualization in human resource management. This review aims to highlight the power of data visualization in the field of human resources. In addition, visualize the latest trends in the research area of human resource and data visualization by conducting a quantitative method for analysis. The study adopted a literature review on recent publications from 2017 to 2022 to address research questions.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.162-168
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• 2023

As the old saying goes "a picture is worth a thousand words" data visualization is essential in almost every industry. Companies make Data-driven decisions and gain insights from visual data. However, there is a need to investigate the role of data visualization in human resource management. This review aims to highlight the power of data visualization in the field of human resources. In addition, visualize the latest trends in the research area of human resource and data visualization by conducting a bibliometric analysis. The study adopted a literature review on recent publications from 2017 to 2022 to address research questions.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.