• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.524-531
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• 2010

The automobile networks consist of the communication bus systems which become independent and heterogeneous each other. Most often, the CAN buses are implemented in a car in order to connect all the electronic control units in various ways. Thus, some gateways are necessary for exchanging the useful information between electronic control units on the buses. The automobile body networks group is divided into two kinds on a large scale, namely the low-speed bus and the high-speed bus. To interchange messages between the two, a CAN signal gateway is designed and implemented in a miniature scale. A network analyzer (called "Vehicle spy") and an oscilloscope monitor network situation to confirm the due operation of CAN signal gateway. The efficiency of the designed gateway is evaluated. The more message thread increased, the more efficiency decreased.

• Journal of Integrative Natural Science
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• v.9 no.3
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• pp.199-205
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• 2016

In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.7-14
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• 2006

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• Journal of Preventive Medicine and Public Health
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• v.46 no.6
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• pp.336-345
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• 2013

Objectives: Research has shown that obesity appears to spread through social ties. However, the association between other characteristics of social networks and obesity is unclear. This study aimed to identify the association between social network characteristics and body mass index (BMI, $kg/m^2$) in an elderly Korean population. Methods: This cross-sectional study analyzed data from 657 Koreans (273 men, 384 women) aged 60 years or older who participated in the Korean Social Life, Health, and Aging Project. Network size is a count of the number of friends. Density of communication network is the number of connections in the social network reported as a fraction of the total links possible in the personal (ego-centric) network. Average frequency of communication (or meeting) measures how often network members communicate (or meet) each other. The association of each social network measure with BMI was investigated by multiple linear regression analysis. Results: After adjusting for potential confounders, the men with lower density (<0.71) and higher network size (4-6) had the higher BMI (${\beta}$=1.089, p=0.037) compared to the men with higher density (>0.83) and lower size (1-2), but not in the women (p=0.393). The lowest tertile of communication frequency was associated with higher BMI in the women (${\beta}$=0.885, p=0.049), but not in the men (p=0.140). Conclusions: Our study suggests that social network structure (network size and density) and activation (communication frequency and meeting frequency) are associated with obesity among the elderly. There may also be gender differences in this association.

• Korean Institute of Interior Design Journal
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• v.16 no.2 s.61
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• pp.131-138
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• 2007

Relationship between body and space is fundamental in space design. The perception and concept of human body in each age directly affected the space makings of that time. Thoughts on human body are related to various periodical backgrounds such as culture, art, technology and etc. Body-Space relationship has been changed through different epochs and is being changed in the present time too. In contemporary cities, architectural programs has been fragmented and activities of individuals become more articulated. The rigidity of each architectural program has been forced to be more flexible amalgamation of diverse behaviors by dynamic urban time-space formations and patterns. Based on this current situations, new experimental designs that question the existing preconceived relationship between body and space in different views. These design experiments attempt to overcome the solid physical fixation of architectural buildings and to directly relate human body to intelligent devices, technologies, machines as well as spaces. This research focus on the innovative design projects in which body, machine, space are smartly compound as one organization. The purpose of this study is to examine the new Body-Space relationship as well as some relevant case projects in contemporary fashion, furniture, interior design and architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1854-1868
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• 2018

A wireless body-sensor network (WBSN) refers to a network-configured environment in which sensors are placed on both the inside and outside of the human body. The sensors are much smaller and the energy is more constrained when compared to traditional wireless sensor network (WSN) environments. The critical nature of the energy-constraint issue in WBSN environments has led to numerous studies on the reduction of energy consumption of WBSN sensors. The transmission-power-control (TPC) technique adjusts the transmission-power level (TPL) of sensors in the WBSN and reduces the energy consumption that occurs during communications. To elaborate, when transmission sensors and reception sensors are placed in various parts of the human body, the transmission sensors regularly send sensor data to the reception sensors. As the reception sensors receive data from the transmission sensors, real-time measurements of the received signal-strength indication (RSSI), which is the value that indicates the channel status, are taken to determine the TPL that suits the current-channel status. This TPL information is then sent back to the transmission sensors. The transmission sensors adjust their current TPL based on the TPL that they receive from the reception sensors. The initial TPC algorithm made linear or binary adjustments using only the information of the current-channel status. However, because various data in the WBSN environment can be utilized to create a more efficient TPC algorithm, many different types of TPC algorithms that combine human movements or fuse TPC with other algorithms have emerged. This paper defines and discusses the design and development process of an efficient TPC algorithm for WBSNs. We will describe the WBSN characteristics, model, and closed-loop mechanism, followed by an examination of recent TPC studies.