• International journal of advanced smart convergence
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• v.5 no.1
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• pp.8-15
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• 2016

Studies have been made for the wireless sensor network protocols by a number of researchers to date. In particular, the studies as to the hierarchical protocol LEACH algorithm was concentrated. Various studies have been derived for the performance of the protocol is based on the LEACH protocol have been made. Improved algorithms have been proposed continuously. On the other hand, The performance comparison and evaluation of the improved algorithm is insufficient. Therefore, we compared the performance for the ML-LEACH (Multi Hop-Layered) and DL-LEACH (Dual Hop-Layered) been derived mainly LEACH. scalability, energy consumption, CH elected, network lifetime were selected as a Performance evaluation items.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.2
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• pp.136-140
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• 2011

Monitoring of water surfaces through permanent measurement of hydrodynamic and meteorological data is one of the main requirements in safe and sustainable water management. The Caspian Sea, the major surface water body in Iran, significantly affects more than 600 km of urban and industrial coastline. In the present work, an integrated marine monitoring network for the entire southern coastline of the Caspian Sea was developed. The main design concerns centered on the network measuring components and data recording, checking, filtering, gap recognition, and transferring systems. Four coastal monitoring stations were assigned, along with two regional collecting stations and one central data station for gathering, checking and delivering recorded data at different access levels. Applicable guidelines on selection of measuring devices for both shallow and deep water zones are presented herein.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.821-822
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• 2006

Ubiquitous healthcare monitoring and measuring system based on wireless sensor network was implemented and tested. The system can measure the ECG and body temperature of patients or elderly persons and transfer the data wirelessly in ad-hoc network to remote base-station connected to doctor's PDA/PC or hospital server, using wireless sensor motes. The data obtained can be analyzed by doctors and care providers to monitor a health status of patient in real time environment. To prove the capabilities of the wireless sensor network platform for ubiquitous healthcare applications, the performance of our monitoring and measuring system was tested with positive results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.139-146
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• 2008

Wireless sensor network-related application system can perform functions such as environmental pollution monitoring, building control, home automation in future. In this paper, we present wireless sensor network based system for subway station in order to reduce the damage of the people and the subway station due to fire. Sensor nodes in this system can sense temperature, illumination, smoke, and human body in real time and detect the accident in the subway station. These real-time sensing and wireless networking minimize casualties and damage to property.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.113-127
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• 2015

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.

• Korean Journal of Health Education and Promotion
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• v.19 no.2
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• pp.73-98
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• 2002

This study was carried out to investigate the relationship between social support, social network and health behaviors as surveyed by cross-sectional study in 744 rural people aged above 30 of a community dwelling sample of one county for 6 days of July in 2000. Objectives of this study was in order to establish an effective health promotion. The sample was accrued by face to face interview of direct visiting from clustered sampling method. Interview was conducted by trained medical students with the questionnaire consisted of socio-demographic data, health behavior, social support and social network based on previous literature. The summarized results were as follows: 1. There were significant difference in the level of social support and social network by general characteristic variables except occupation and residency type(p〈0.05). 2. There were significant difference in knowledge about hypertension, smoking status, status of physical exercise, diet patterns by social support and social network in spite of variation of social support and social network subconcept(p〈0.05). And there were significant difference in alcohol drinking status, body weight control and diet pattern according to level of social network(p〈0.05). But smoking status by social support and network results opposite direction(p〈0.05). 3. There were no regular or consistent result in the relationship between social support, social network and health behavior. 4. Major predictors for health behavior on the multiple logistic regression that included general characteristic, social support and social network were age, instrumental social support and worry about health. Significant variables of multiple logistic regression for health behavior that included social support(instrumental and emotional) and social network were instrumental social support and social network. These results suggest that only a instrumental element and social network may be associated with health behavior. Inconsistent with prior research in these some item, a positive consistent relationship was not found between social support, social network and health behavior. So the study should be replicated to determined the reliability of our findings.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.9-18
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• 2014

The sensors attached on/in a person are moved since human body frequency changes their activity, therefore in wireless body area networks, nodal mobility and non-line-of-sight condition will impact on performance of networks such as energy efficiency and reliable communication. We then proposed schemes which study on forwarding decisions against frequent change of topology and channel conditions to increase reliable connections and improve energy efficiency. In this work, we control the size of packets, forwarding rate based on ratio of input links and output links at each node. We also robust the network topology by extending the peer to peer IEEE 802.15.4-based. The adaptive topology from chain-based to grid-based can optimal our schemes. The simulation shows that these approaches are not only extending network lifetime to 48.2 percent but also increase around 6.08 percent the packet delivery ratio. The "hot spots" problem is also resolved with this approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.763-770
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• 2021

In this paper, in order to exclude the influence of BAN(Body Area Network) signals operating in the ISM band, the design and optimization process of an on-body microstrip patch antenna operating at 4.567 GHz is presented. The antenna for the monitoring of the lower legs with cancellous osteoporosis is designed to be lightweight and compact with improved return loss and bandwidth. The structure around the applied lower leg consisted of a five-layer dielectric plane. Taking into account losses, the complex dielectric constant of each layer is calculated using multi Cole-Cole model parameters, whereas a unipolar model is used for normal or osteoporotic cancellous bones. The return loss of the coaxial feed antenna on the phantom is -67.26 dB at 4.567 GHz, and in the case of osteoporosis, at the same frequency the return loss difference is 35.88 dB, and the resonance frequency difference is about 7 MHz.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.433-441
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• 2007

The bio-Sensors, which are sensing the vital signs of human bodies, are largely used by the medical equipment. Recently, the sensor network technology, which composes of the sensor interface for small-seize hardware, processor, the wireless communication module and battery in small sized hardware, has been extended to the area of bio-senor network systems due to the advances of the MEMS technology. In this paper we have suggested a design and implementation of a health care information system(called u-EMS) using a bio-sensor network technology that is a combination of the bio-sensor and the sensor network technology. In proposed system, we have used the following vital body sensors such as EKG sensor, the blood pressure sensor, the heart rate sensor, the pulse oximeter sensor and the glucose sensor. We have collected various vital sign data through the sensor network module and processed the data to implement a health care measurement system. Such measured data can be displayed by the wireless terminal(PDA, Cell phone) and the digital-frame display device. Finally, we have conducted a series of tests which considered both patient's vital sign and context-awared information in order to improve the effectiveness of the u-EMS.