• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.188-195
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• 2002

The M-algorithm for decoding convolutional codes can significantly reduce the complexity of the Viterbi algorithm by tracking a fixed number of survivor paths in each level of the decoding trellis. It is an easily-implementable algorithm suited for real-time processing of high-speed data. The algorithm, however, generates a sequence of catastrophic errors when the correct path is not included in the set of survivor paths. In this paper, the performance of the M-algorithm obtained from using various decoding complexity levels, frame lengths, and code constraint lengths is presented. The performance gain is quantified when the algorithm is used in conjunction with codes of increased constraint length. In particular, it is demonstrated the gain from the increased code free distance overcompensates the loss from the correct path being excluded from the survivors, when the frame length is short to moderate. Using 64 survivor paths, the signal-to-noise ratio gain obtained by increasing the constraint length from K=7 to K=9, 11, 15 is respectively 0.6, 0.75, and 08dB, when the frame of length L=100 has the frame error rate of 0.01%.

• The KIPS Transactions:PartC
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• v.15C no.4
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• pp.303-310
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• 2008

To increase the lifetime of ad-hoc networks, a ratio of energy consumption for each node should be kept constant by equally distributing network traffic loads into all of the nodes. In this paper, we propose a modified AODV routing protocol to determine a possible route by considering a remaining battery capacity of a node and the degree of its usage. In addition, to reduce the amount of energy consumption during the path rediscovery process due to the huge amount of the AODV control messages the limited number of possible routes are stored into a routing table of a source node. When some links of a route fail, another possible path can be looked up in the table before the route discovery process should be initiated. We have tested our proposed method with a conventional AODV and a MMBCR method which is one of the power-efficient energy routing protocols based on the three performance metrics, i.e., the total remaining battery capacity, network lifetime and the ratio of data packets received by the destination node to compare their performance.

• Korean Journal of Family Social Work
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• no.54
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• pp.225-262
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• 2016

This article reports on the analysis of the patterns and frequency of recurrences of substantiated instances of maltreatment in the Child Protective System (CPS). The data was collected from 2012 to 2014 by the CPS. Five-thousand-five-hundred-forty-two cases were substantiated in 2012 and then 323 cases were exposed recurrence during that time. Most recurrence families experienced only one recurrence. Results from survival analyses instances indicated that risk of recurrence was greatest during the first one month following a report. The prior CPS report, multiple type of abuse, physical abuse, and services type was linked the pattern of recurrence of maltreatment. Also, victim's age and the number of problematic behavior, perpetrator's stress and social isolation, intra family member as perpetrator, prior CPS report, and the number of reports was linked recurrence.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.371-378
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• 2012

Through sensor nodes it can obtain behavior, condition, location of objects. Generally speaking, sensor nodes are very limited because they have a battery power supply. Therefore, for collecting sensor data, efficient energy management is necessary in order to prolong the entire network survival. In this paper, we propose a method that increases energy efficiency to be self-configuring by distributed sensor nodes per cluster. The proposed method is based on the ANTCLUST. After measuring the similarity between two objects it is method that determine own cluster. It applies a colonial closure model of ant. The result of an experiment, it showed that the number of alive nodes increased 27% than existing clustering methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6A
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• pp.864-870
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• 1999

A key difference between a tactical military and commercial satcom system is that the military system is expected to provide a service in the presence of ECM threats such as jamming. In this paper, we analyzed the three different type of link system applicable to the military satellite and directly compared to each system in order to find system performance according to the maximum jammer power. For the analysis, we modeled and simulated the SHF satellite system based on uplink jamming. From the results, it is found for each different type of link the maximum jammer power that can be tolerated with link operating at its suggested nominal rate. Also, we studied to the survivability level against the jamming condition through the performance comparison in shipborne and portable terminal consistent with frequency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.714-724
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• 2022

The spread of mobile devices comes to increase explosive data traffic and then results in various types of service demands. In order to satisfy the increment of traffic and the requirements for various services, optical internet technologies that transmit several Tbps through only a single optical fiber have been actively studied. Because of the large bandwidth, an optical link can accommodate a large number of service types. Therefore, new technologies are required to guarantee the survivability of optical internet and handle the requirements of each service. So, in this article, a new scheme based on GMPLS technologies is proposed to deal with the various service demands and survivability issues in IP over WDM networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2187-2192
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• 2016

A data-centric routing protocol considering a data aggregation technique at relay nodes is required to increase the lifetime of wireless sensor networks. An energy-efficient data-centric routing algorithm is proposed by considering a tradeoff between acquisition time and energy consumption in the wireless sensor network. First, the proposed routing scheme decides the sink node among all sensor nodes in order to minimize the maximum distance between them. Then, the proposed routing extends its tree structure in a way to minimize the link cost between the connected nodes for reducing energy consumption while minimizing the maximum distance between sensor nodes and a sink node for rapid information gathering. Simulation results show that the proposed data-centric routing algorithm has short information acquisition time and low energy consumption; thus, it achieves high energy efficiency in the wireless sensor network compared to conventional routing algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.79-86
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• 2009

In this paper, we propose a new clustering scheme that provides optimal data aggregation effect and reduces energy consumption of nodes by considering the density of nodes when forming clusters. Since the size of the cluster is determined to ensure optimal data aggregation rate, our scheme reduces transmission range and minimizes interference between clusters. Moreover, by clustering using locally adjacent nodes and aggregating data received from cluster members, we reduce energy consumption of nodes. Through simulation, we confirmed that energy consumption of the whole network is minimized and the sensor network life-time is extended. Moreover, we show that the proposed clustering scheme improves the performance of network compared to previous LEACH clustering scheme.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.