• Journal of Information Processing Systems
• /
• v.5 no.2
• /
• pp.77-86
• /
• 2009

In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling and feature extraction from bio-potentials. Such a system could increase the quality of life and significantly lower healthcare costs for everyone in general, and for the elderly and those with disabilities in particular.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.497-498
• /
• 2007

The most of important thing when we design a Wireless Sensor Network is resources. You have to consider energy efficient operation When you design Wireless Sensor Network. Because Sensor devices have a limited resources. In this paper, we proposed energy efficiency localization technique in Wireless Sensor Network. We used Cell ID technique for location search. This method can reduce power consumption and the network life time will be extension.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.780-791
• /
• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.12
• /
• pp.4269-4292
• /
• 2014

Currently, increasing numbers of access points (AP) are being deployed in enterprise offices, campuses and municipal downtowns for flexible Internet connectivity, but most of these access points are idle or redundant most of the time, which causes significant energy waste. Therefore, with respect to power conservation, applying energy efficient strategies in WIFI networks is strongly advocated. One feasible method is dynamically managing network resources, particularly APs, by powering devices on or off. However, when an AP is powered on, the device is initialized through a long boot time, during which period clients cannot be associated with it; therefore, the network performance would be greatly impacted. In this paper, based on a global view of an entire WLAN, we propose an AP selection technology, known as Temporary Access Selection (TAS). The criterion of TAS is a fusion metric consisting of two evaluation indexes which are based on throughput and battery life, respectively. TAS is both service and clients' preference specific through balancing the data rate, battery life and packet size. TAS also works well independently in traditional WLANs in which no energy efficient strategy is deployed. Moreover, this paper demonstrates the feasibility and performance of TAS through experiments and simulations with Network Simulator version 3 (NS3).

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.3
• /
• pp.221-229
• /
• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• International conference on construction engineering and project management
• /
• 2009.05a
• /
• pp.1324-1327
• /
• 2009

Social infrastructure is the basis of public welfare and should be recognized and managed as important assets. Bridge is one of the most important infrastructures to be managed systematically because the impact of the failure is critical. It is essential to monitor the performance of bridges in order to manage them as an asset. But current analytical methods such as predictive modeling and structural analysis are very complicated and difficult to use in practice. To apply these methods, structural and material condition data collection should be performed in each element of bridge. But it is difficult to collect these detailed data in large numbers and various kinds of bridges. Therefore, it is necessary to collect data of major measurement items and predict the life of bridges roughly with advanced information technologies. When certain measurement items reach predefined limits in the monitoring bridges, precise performance measurement will be done by detailed site measurement. This paper describes the selection of major measurement items that can represent the tendency of bridge life and introduces automated bridge data collection test-bed using wireless sensor network technology. The following will be major parts of this paper: 1) Examining the features of conventional bridge management system and data collection method 2) Mileage concept as a bridge life indicator and measuring method of the indicator 3) Test-bed of automated and real-time based bridge life indicator monitoring system using wireless sensor network

• ETRI Journal
• /
• v.40 no.5
• /
• pp.604-612
• /
• 2018

To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node-selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy-adjustment factor are introduced to optimize the node-selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node-selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data-receiving rate in sufficient time.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.154-156
• /
• 2006

These days wireless sensor networks receive much attention from industry and researchers on various fields. The challenge is that wireless sensor networks have limited resources. Nevertheless, the route discovery in ZigBee sensor networks is performed by simple flooding when the original racket is rebroadcasted at least once by every node in a network. Such uncontrolled flooding generates an excessive number of packets competing for the media and causes a high collision rate. In this article, we propose a suitable packet Time-To-Live value to solve problems of uncontrolled flooding in ZigBee networks. It is shown that more sufficient route discovery in a ZigBee network can save network resources and lengthen the life of a sensor network.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.1
• /
• pp.48-55
• /
• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.9 no.4
• /
• pp.119-133
• /
• 2014

This study examines the effects of social network support types on the subjective satisfaction of life for elderly people focused on the impact of voluntary and present oriented social network type represented by hobby club vs. non voluntary and past oriented social network type for example alumni association in Korean society respectively. According to the advanced studies figured out by preceding researchers, most of social networks had a good and positive effects on the elderly people's subjective satisfaction of life. However, this study tried to classify the social networks by type such as starting time to make relationship among members and voluntary or non voluntary for participation to the networks. As a result of this study, voluntary and present oriented social network represented by hobby club has a very positive relationship with Korean elderly people's subjective satisfaction of life while non voluntary and past oriented social network as alumni association has no relationship with elderly people's subjective satisfaction of life. Therefore, government policies and budget should be concentrated on encouraging or supporting voluntary and present oriented social networks such as hobby club, voluntary agency etc. rather than non voluntary and past oriented social networks so as to maximize the effect for increasing Korean elderly people's subjective satisfaction of life.