• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.6-13
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• 2011

This paper deals with the interconnection problem in ad-hoc networks or sensor networks, where relay nodes are deployed additionally to form connections between given nodes. This problem can be reduced to a NP-hard problem. The nodes of the networks, by applications or geographic factors, can be deployed densely in some areas while sparsely in others. For such a case one can make an approximation scheme, which gives shorter execution time, for the additional node deployments by ignoring the interconnections inside the dense area of nodes. However, the case is still a NP-hard, so it is proper to establish a polynomial time approximation scheme (PTAS) by implementing a dynamic programming. The analysis can be made possible by an elaboration on making the definition of the objective function. The objective function should be defined to be able to deal with the requirement incurred by the substitution of the dense area with its abstraction.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.603-610
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• 2020

The sleep measurement system is important to detect sleep disturbances as early as possible to be diagnosed and treat the diseases quickly. This paper presents design of system to measure the quality of sleep by using nine accelerometer sensors embedded in the pillow to detect the acceleration of limb movement, e.g. head movements. The participants were asked to sleep using a smart pillow for five days. While sleeping, participants are recorded using a camera on a computer. After awakening, participants were asked to fill out a post-sleep questionnaire. Spearman's correlation was performed to test the correlation of gross body movement per minute rate with post-sleep questionnaire questions. Finally, the seven score of sleep quality were tested with gross body movement per minute rate. The result is the higher gross body movement per minute during sleep represented lower sleep quality.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.33-38
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• 2018

In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1635-1645
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• 1999

In this paper, an intelligent resynthesis technique for timing optimization at the architecture-level has been studied. The proposed technique can remedy the problem which may occur in combinational timing optimization techniques applied to circuits which have the hierarchical subblock structure at the architectural-level. The approach first tries to maintain the original hierarchical subblock while minimizing the longest delay of whole circuit. This paper tries to find a new approach to timing optimization for circuits which have hierarchical structure at architectural-level, and has verified its effectiveness experimentally. We claim its usefulness from the fact that most designers design the circuits hierarchically due to the increase of design complexity.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.501-509
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• 2016

We propose a method to improve the robustness of speaker verification on short test utterances. The accuracy of the state-of-the-art i-vector/probabilistic linear discriminant analysis systems can be degraded when testing utterance durations are short. The proposed method compensates for utterance variations of short test feature vectors using deep neural networks. We design three different types of DNN (Deep Neural Network) structures which are trained with different target output vectors. Each DNN is trained to minimize the discrepancy between the feed-forwarded output of a given short utterance feature and its original long utterance feature. We use short 2-10 s condition of the NIST (National Institute of Standards Technology, U.S.) 2008 SRE (Speaker Recognition Evaluation) corpus to evaluate the method. The experimental results show that the proposed method reduces the minimum detection cost relative to the baseline system.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.923-930
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• 2018

In the wireless communication system based on an array antenna, the angle of arrival (AOA) information of signal is very important element and various AOA estimation algorithms have been studied. Although most AOA estimation algorithms employ the uniform linear array (ULA), some algorithms apply the planar array (PA) antenna. In this paper, we present an algorithm for efficiently estimating AOAs of adjacent multiple signals, based on the uniform rectangular array antenna. This approach has two steps; after approximately estimating AOA groups consisting of the closely located signal sources using CAPON, accurately estimating the individual AOA of each signal in the estimated AOA group using Beamsapce MUSIC. The estimation performance of the presented cascade AOA algorithm is illustrated through the computer simulation example.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.607-613
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• 2019

This paper proposes LF-MMI (Lattice Free Maximum Mutual Information)-based acoustic modeling using external knowledge for speech recognition. Note that an external knowledge refers to text data other than training data used in acoustic model. LF-MMI, objective function for optimization of training DNN (Deep Neural Network), has high performances in discriminative training. In LF-MMI, a phoneme probability as prior probability is used for predicting posterior probability of the DNN-based acoustic model. We propose using external knowledges for training the prior probability model to improve acoustic model based on DNN. It is measured to relative improvement 14 % as compared with the conventional LF-MMI-based model.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1081-1088
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• 2021

The modern wireless communication system employes the beamforming technique based on a massive array antenna with a number of elements, for supporting the smooth communication services. A reliable beamforming technology requires the Angle-of-Arrival(: AOA) information for the signal incident to the receiving antenna, which is generally estimated by the high-resolution AOA estimation algorithm such as Multiple Signal Classification(: MUSIC). Although the MUSIC algorithm has the excellent estimation performance, it is difficult to estimate AOA in real time for the massive array antenna due to the extremely high computational complexity. In order to enhance this problem, in this paper, we propose the cascade AOA estimation algorithm based on a Flexible Massive Concentric Circular Array(: FMCCA) antenna with the On/Off function for antenna elements. The proposed cascade algorithm consists of the CAPON algorithm using some elements among entire antenna elements and the Beamspace MUSIC algorithm using entire elements. We provide computer simulation results for various scenarios to demonstrate the AOA estimation performance of the proposed approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.609-616
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• 2023

In a wireless network environment, since sensors are not always connected to power, the life of a battery, which is an energy source supplied to sensors, is limited. Therefore, various studies have been conducted to extend the network life, and a layer-based routing protocol, LEACH(: Low-energy Adaptive Clustering Hierarchy), has emerged for efficient energy use. However, the LEACH protocol, which transmits fused data directly to the sink node, has a limitation in that it consumes as much energy as the square of the transmission distance when transmitting data. To improve these limitations, this paper proposes an algorithm that can minimize the transmission distance with multi-hop transmission where cluster heads are chained between cluster heads through relative distance calculation from sink nodes in every round.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.