• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.388-390
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• 2006

This paper presents a hybrid SoC design for phase detection of single tone signal. The designed hybrid SoC is composed of three functional blocks, i.e., an analog to digital converter module, a phase detection module and a controller module. A design of the controller module is based on a 16-bit RISC architecture. An I/O interface and an LCD control interface for transmission and display of phase measurement values are included in the design of the controller module. A design of the phase detector is based on a recursive sliding-DFT. The recursive architecture effectively reduces the gate numbers required in the implementation of the module. The ADC module includes a single-bit second-order sigma-delta modulator and a digital decimation filter. The decimation filter is designed to give 98dB of SNR for the ADC. The effective resolution of the ADC is enhanced to 98dB of SNR by the incorporation of a pre FIR filter, a 2-stage cascaded integrator- comb(CIC) filter and a 30-tab FIR filter in the decimation. The hybrid SoC is verified in FPGA and implemented in 0.35 CMOS Technology.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.621-629
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• 2016

This paper suggests a new structure of hybrid DRC to enhance the psychoacoustic sound quality of a conventional multiband DRC. The proposed hybrid DRC consists of two serially cascaded stages. The front stage DRC is multiband, and it compresses input based on RMS level detection, whereas, the back stage DRC is single band, and it regulates input according to peak level detection. The proposed hybrid DRC shows better loudness while suppressing distortion by clipping. The proposed algorithm was verified through MATLAB simulation, and it was implemented using an FPGA board for listening test. The test result showed that the proposed hybrid structure enhances overall psychoacoustic sound quality compared to conventional structures, which is based on only RMS or peak level detection.

• Environmental Engineering Research
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• v.22 no.2
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• pp.186-192
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• 2017

The carbon paste electrode (CPE) was modified with the pristine bentonite and hybrid material (HDTMA-modified bentonite). The modified-CPEs are then employed as working electrode in an electrochemical detection of As(V) from aqueous solutions using the cyclic voltammetric measurements. Cyclic voltammograms revealed that As(V) showed reversible behavior onto the working electrode. The hybrid material-modified carbon paste electrode showed significantly enhanced electrochemical signal which was then utilized in the low level detection of As(V). Moreover, the studies were conducted at neutral pH conditions. The electrochemical studies were conducted with scan rates (20 to 200 mV/s) to deduce the mechanism of redox processes involved at the electrode surface. The anodic current was linearly increased, increasing the concentration of As(V) from 5.0 to $35.0{\mu}g/g$ using the hybrid material-modified electrode. This provided fairly a good calibration line for As(V) detection. The presence of varied concentrations of As(III) in the determination of total arsenic was studied. The influence of several cations and anions viz., Cu(II), Mn(II), Zn(II), Pb(II), Cd(II), Fe(III), $Cl^-$, $NO_3{^-}$, $PO_4{^{3-}}$, EDTA and glycine in the detection of As(V) from aqueous solution was also studied. Further, in an attempt to simulate the real matrix analysis, the tap water sample was spiked with As(V) and subjected for As(V) detection using the modified-CPE.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4465-4466
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• 2014

HPV type-specific detection may promote cervical screening program and vaccination development worldwide. We conduct a study comparing HPV Hybrid capture II (HC II) Test and Hybribio GenoArray test, a newly developed HPV type-specific assay, in patients with cervical epithelial neoplasm. Results showed a good concordance in cervical HPV detection between two tests (kappa value 0.80, p<0.05, McNemar test). Our study may promote utilization of type-specific HPV detection that is helpful for cervical cancer screening and vaccination.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.11
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• pp.19-23
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• 2010

In this paper, we propose a combining scheme for partial IR based hybrid ARQ in MIMO systems. The proposed combining scheme is a symbol-level combining scheme for repeatedly transmitted systematic symbols in partial IR based hybrid ARQ. In this paper, it is shown that the proposed combining scheme can also enhance the detection performance of the parity symbols that are newly transmitted in each retransmission. Simulation results show that the proposed combining scheme significantly improves the performance of the partial IR based hybrid ARQ compared to the cases of the conventional bit-level combining scheme, especially with the ZF detection.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.117-122
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• 2007

For multiple antenna systems, we consider the hybrid iterative detection of the maximum a posteriori probability(MAP) detection and the linear detection such as the minimum-mean-square-error(MMSE) filtering with soft cancelation. We devise methods to obtain both the lower complexity of the linear detection and the superior performance of the MAP detection. Using the a prior probability of the coded bit which is extrinsic of the outer decoder, we compute the threshold of grouping and determine the detection scheme symbol by symbol. Through the simulation results, it is shown that the proposed receiver obtains the superior performance to the MMSE detector and the lower complexity than the MAP detector.