• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.114-123
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• 2016

The effective feature extraction method for unmanned aerial vehicle (UAV) detection is proposed and verified in this paper. The UAV engine sound is harmonic complex tone whose frequency ratio is integer and its variation is continuous in time. Using these characteristic, we propose the feature vector composed of a mean and standard deviation of difference value between fundamental frequency with 1st overtone as well as mean variation of their frequency. It was revealed by simulation that the suggested feature vector has excellent discrimination in target signal identification from various interfering signals including frequency variation with time. By comparing Fisher scores, three features based on frequency show outstanding discrimination of measured UAV signals with low signal to noise ratio (SNR). Detection performance with simulated interference signal is compared by MFCC by using ELM classifier and the suggested feature vector shows 37.6% of performance improvement As the SNR increases with time, the proposed feature can detect the target signal ahead of MFCC that needs 4.5 dB higher signal power to detect the target.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2293-2300
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• 2008

In ad-hoc sensor network, AODV routing information is disclosed to other nodes because AODV protocol doesn't have any security mechanisms. The problem of AODV is that an attacker can falsify the routing information in RREQ packet. If an attacker broadcasts the falsified packet, other nodes will update routing table based on the falsified one so that the path passing through the attacker itself can be considered as a shortest path. In this paper, we design the routing-information-spoofing attack such as falsifying source sequence number and hop count fields in RREQ packet. And we suggest an efficient scheme for detecting the attackers and isolating those nodes from the network without extra security modules. The proposed scheme doesn't employ cryptographic algorithm and authentication to reduce network overhead. We used NS-2 simulation to evaluate the network performance. And we analyzed the simulation results on three cases such as an existing normal AODV, AODV under the attack and proposed AODV. Simulation results using NS2 show that the AODV using proposed scheme can protect the routing-information-spoofing attack and the total n umber of received packets for destination node is almost same as the existing norm at AODV.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.535-549
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• 2008

Vicarious calibration for the satellite sensor relies on simulated TOA (Top-of-Atmosphere) radiances over various targets. In this study, TOA visible radiance was calculated over ocean targets which are located in five different regions over the Indian and Pacific ocean, and its possible use for the satellite sensor calibration was examined. TOA radiances are simulated with the 6S radiative transfer model for the comparison with MODIS/Terra and SeaWiFS measurements. Geometric angles and sensor characteristics of the reference satellites were taken into account for the simulation. AOT (Aerosol Optical Thickness) from MODIS/Terra, pigment concentrations from Sea WiFS, and ozone amount from OMI measurements were used as inputs to the model. Other atmospheric input parameters such as surface wind and total column water vapor were taken from NCEP/NCAR reanalysis data. The 5-day averaged radiances over all targets show that the percent differences between simulated and observed radiances are within about ${\pm}5%$ in year 2005, indicating that the calculated radiances are in good agreement with satellite measurements. It has also been shown that the algorithm can produce the SeaWiFS radiances within about ${\pm}5%$ uncertainty range. It has been suggested that the algorithm can be used as a tool for calibrating the VIS bands within about 5% uncertainty range.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.879-891
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• 2019

This paper provides performance evaluations of various channel estimation schemes for Advanced Television Systems Committee (ATSC) 3.0 multiple-input multiple-output (MIMO) system under a fixed reception environment. ATSC 3.0 MIMO system can obtain high spectral efficiency and improved reception performance compared to conventional terrestrial broadcasting systems. The ATSC 3.0 MIMO defines Walsh-Hadamard and null pilot encoding algorithms and the amplitude and phase of MIMO pilots are different from those of single-input single-output pilots. At the receiver, linear and discrete Fourier transform (DFT)-based interpolations can be used for the channel estimation. This paper provides the various combinations of the interpolation schemes for channel estimation in time and frequency dimensions, and then analyzes the performance of the various combinations through the computer simulation. The results of computer simulation show that the combination of the linear interpolation in the time dimension and then DFT-based interpolation in the frequency dimension can obtain the best performance among the considered combinations.

• Journal of Korea Water Resources Association
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• v.52 no.spc2
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• pp.777-789
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• 2019

The purpose of this study is to evaluate changes in water quality and aquatic ecosystem health by additional dam release of stream maintenance flow from multipurpose dams in Han river basin ($34,148km^2$) using SWAT (Soil and Water Assessment Tool). The period of additional release was spring (April to June) and autumn (August to October) to evaluate the changes with the data of aquatic ecosystem health survey. The amount of additional release was set proportional to the present dam release, and the maximum release amount was controlled not to exceed the officially notified stream maintenance flow from dam. The 10 percent to 50 percent additional releases showed that the stream water quality (T-N, $NH_4$, T-P, and $PO_4-P$) concentrations except $NO_3-N$ decreased in spring while increased in autumn period. Using the stream water quality results and applying with Random Forest algorithm, the grade of aquatic ecosystem health index (FAI, TDI, and BMI) was improved for both periods especially in the downstream of basin. This study showed that the additional release of stream maintenance flow was more effective in spring than autumn period for the improvement of water quality and aquatic ecosystem.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1125-1133
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• 2021

This study attempted to understand seawater characteristics like chlorophyll concentration (CHL), total suspended matter concentration (TSM), absorption characteristics, and remote sensing reflectance around Weno Island, Micronesia, located in the South Pacific Ocean near the equator. 50 in-situ measurement from May to June 2013 were analyzed and compared with data from around Korean peninsula. CHL around Weno Island was 0.11-0.49 mg/m³ (average 0.26 mg/m³), and TSM was 0.03-0.31 g/m³, (average 0.16 g/m³), showing typical clear water characteristics. Absorption coefficient of total suspended matters at 443 nm showed over 0.5 times less than that from East Sea and the slope of absorption coefficient spectrum of dissolved organic matter showed much larger than that of Korean peninsula, indicating the concentration of organic matter is very low and dissolved organic matter of marine origin is considered to be the main component in the study area. As a result of comparing the remote sensing reflectance spectrum with that of coastal waters around the Korean peninsula, coastal waters around Weno Island showed typical CASE-1 water properties. It was possible to understand the marine optical characteristics of coral reef habitats in tropical waters, and it can be used to develop seawater algorithms specialized in the study area.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.