• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.1947-1955
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• 1989

With the increasing complexity of cabling at sensory level in process control and manufacturing automation, field buses were introduced to replace the traditional point to point links from each sensor or acruator to its controlling equipments by a single link on which all information is transmitted seriall and multiplexed in time. In this papr, we introduce the BITBUS network as a field bus. For the service discipline of the BITBUS network, two service strategies are proposed to obtain the performance of the network. They are the equal priority cyclic service strategy and the non-equal priority cyclic service strategy. The former assigns equal priority to each node for polling and the latter assumes non-equal priority. The BITBUS network was modeled as a cyclic queueing model and it is analyzed by two methods: the Kuehn's and the Boxma's. Computer simulation was also done for the cyclic queueing model and simulation results were compared with those. Under mathematically non-analyzable environment, only the computer simulation was done. From the simulation result, in order to meet the response time requirement of 5 msec imposed by International Electrotechnical Commission when each node has the average traffic of 5000 messages/sec in manufacturing automation the number of slave nodes should be smaller than 10 at the transmission rate of 2.5 Mbps.

• Journal of Communications and Networks
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• v.14 no.1
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• pp.51-62
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• 2012

The exponential growth in wireless services has resulted in an overly crowded spectrum. The current state of spectrum allocation indicates that most usable frequencies have already been occupied. This makes one pessimistic about the feasibility of integrating emerging wireless services such as large-scale sensor networks into the existing communication infrastructure. Cognitive radio is an emerging dynamic spectrum access technology that can be used for flexibly and efficiently achieving open spectrum sharing. Cognitive radio is an intelligent wireless communication system that is aware of its radio environment and that is capable of adapting its operation to statistical variations of the radio frequency. In ad hoc cognitive radio networks, a common control channel (CCC) is usually used for supporting transmission coordination and spectrum-related information exchange. Determining a CCC in distributed networks is a challenging research issue because the spectrum availability at each ad hoc node is quite different and dynamic due to the interference between and coexistence of primary users. In this paper, we propose a novel CCC selection protocol that is implemented in a distributed way according to the appearance patterns of primary systems and connectivity among nodes. The proposed protocol minimizes the possibility of CCC disruption by primary user activities and maximizes node connectivity when the control channel is set up. It also facilitates adaptive recovery of the control channel when the primary user is detected on that channel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.9
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• pp.570-577
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• 2014

Range-based real time localization systems require superior localization techniques as well as accurate ranging algorithms for better performance. To evaluate the ranging accuracy between two nodes in practical environments, this paper does not only present a qualitative analysis by computing a distance equation under SDS-TWR measurement model of no symmetry assumption, but also executes a quantitative evaluation by doing experiments after building up a test network employing the developed sensor node. Experimental results show that the ranging accuracy of the proposed implementation of IEEE 802.15.4a software stack is superior with smaller average error rate by 60% to one of the commercial Nanotron's reference development kit.

• Journal of Biosystems Engineering
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• v.25 no.3
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• pp.241-250
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• 2000

The feasibility of precision fertilizing for small size fields was studied by determining fertilizing amount of nitrogenous and calcareous to a cite specific region. A detailed soil survey at three experimental fields of $672m^2$, $300m^2$ and $140m^2$ revealed a considerable spatial variation of the pH and organic matter(OM) levels. Soil organic matter was measured using Walkley-Black method and soil pH was measured with a pH sensor. Soil sample was obtained by Grid Node Sampling Method. The soil sampling depth was 10∼20 cm from the soil surface. To display soil nutrient variation, a soil map was made using Geographic Information System (GIS) software. In soil mapping, soil data between nodes was interpolated using Inverse Distance Weighting (IDW) method. The variation was about 1∼1.8 in pH value and 1.4∼7% in OM content. Fertilizing Amount of nitrogenous and calcareous was determined by th fertilizing equation which was proposed by National Institute of Agricultural Science and Technology(NIAST). The variation of fertilizing amount was about 3∼11 kg/10a in nitrogenous and 70∼140 kg/10a in calcareous. The results showed a feasibility of precision fertilizing for small size fields.

• Annual Conference of KIPS
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• 2015.04a
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• pp.216-217
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• 2015

Duty-cycling could efficiently prolong the life time of Wireless Sensor Networks (WSNs) by let nodes be in dormant state most of the time, and only wake up (for sending or receiving) for a very short period. Flooding is one critical operation of WSNs. Many studies have been studied to improve the delay and/or energy efficiency of flooding. In this paper, we propose a novel time slot design, and the switching decision that reduce energy consumption for the schedule-based flooding tree. Each node, if failed to receive from its parent, will look for other candidate, among its siblings to overhear the flooding packet. By accurately collect information from other siblings, each node can make the best as possible switching decision; therefore the energy efficiency of the network is improved.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.52-57
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• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.505-524
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• 2010

Wireless structural monitoring systems consist of networks of wireless sensors installed to record the loading environment and corresponding response of large-scale civil structures. Wireless monitoring systems are desirable because they eliminate the need for costly and labor intensive installation of coaxial wiring in a structure. However, another advantageous characteristic of wireless sensors is their installation modularity. For example, wireless sensors can be easily and rapidly removed and reinstalled in new locations on a structure if the need arises. In this study, the reconfiguration of a rapid-to-deploy wireless structural monitoring system is proposed for monitoring short- and medium-span highway bridges. Narada wireless sensor nodes using power amplified radios are adopted to achieve long communication ranges. A network of twenty Narada wireless sensors is installed on the Yeondae Bridge (Korea) to measure the global response of the bridge to controlled truck loadings. To attain acceleration measurements in a large number of locations on the bridge, the wireless monitoring system is installed three times, with each installation concentrating sensors in one localized area of the bridge. Analysis of measurement data after installation of the three monitoring system configurations leads to reliable estimation of the bridge modal properties, including mode shapes.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1347-1360
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• 2018

Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.334-337
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• 2019

In this study, we built the data collection system to learn user's habit data by deep learning and to create an indoor environment according to the situation. The system consists of a data collection server and several sensor nodes, which creates the environment according to the data collected. We used Google Inception v3 network to analyze the photographs and hand-designed second DNN (Deep Neural Network) to infer behaviors. As a result of the DNN learning, we gained 98.4% of Testing Accuracy. Through this results, we were be able to prove that DNN is capable of extrapolating the situation.