• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.41-48
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• 2020

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.131-134
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• 1999

This paper considers the acoustical characteristics of the mechanical valve employed in the Korean type Artificial Heart. $Bj\"{o}rk-Shiley$ tilting disc valve was chosen for the study and acoustic measurements were performed for the artificial heart operated in a mock circulation system as well as implanted to an animal as a Bi Ventricular Assist Device (BVAD). In the mock system, three different conditions of the valve were examined which were normal, damaged (torn off), pseudothrombus attached. Microphone measurements for the BVAD were carried out at a regular time interval for 5 days after the implantation operation. Of the recorded acoustic emissions from the artificial heart, click sounds mainly originated from the valves were further analyzed using Multiple Signal Classification (MUSIC) for estimating their spectral properties. It was shown that the spectral peaks below 4 kHz and the optimal order number for MUSIC, equivalent to the number of the spectral component, might be the key parameters which were highly correlated to the physiological states of the valve like the mechanical damage of the valve or the formation of thrombus on the valves.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1183-1186
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• 2004

Degradation and insulation failure of traction motor depend on the continuous stress imposed on it. And knowing on insulation condition is important thing for safety operation of EMU(electric multiple unit). In this paper, PD(partial discharge) characteristics for degradation analysis of stator coil is studied. For PD data acquisition, two models are made; one is normal condition coil, the other is aged condition coil. And PD data for discrimination were acquired from PD detector. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for degradation of stator coil.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.681-688
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• 2007

Applying an appropriate received signal processing algorithm based on the channel characteristics is important to improve the receiver performance. Wireless channels in general exhibit various time-delay characteristics depending on their power delay profile. When the estimated channel power summation is used to determine the amount of time delay, a channel adaptive receiver structure can be implemented. In this paper, we derive a closed-form expression for the error probability of the channel classification when the estimated channel power summation is used to classify channel groups having different time delay characteristics, and present the performance gain utilizing multiple estimation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.492-494
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• 2021

The decision tree technique is used as a classification technique in machine learning. However, the decision tree has a problem of consuming a lot of speed or resources due to the problem of overfitting. To solve this problem, there are bagging and boosting techniques. Bagging creates multiple samplings and models them using them, and boosting models the sampled data and adjusts weights to reduce overfitting. In addition, recently, techniques Xgboost have been introduced to improve performance. Therefore, in this paper, we collect wifi signal data for indoor positioning, apply it to the existing method and Xgboost, and perform performance evaluation through it.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.30-37
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• 2008

Dynamic excitation and response analysis is an acceptable method to determine some of physical properties of agricultural product for quality evaluation. There is a difference in the internal viscoelasticity between sound and defective fruits due to the difference of geometric structures, thereby showing different vibration characteristics. This study was carried out to develop a portable piezoelectric film-based glove sensor system that can separate internally damaged watermelons from sound ones using an acoustic impulse response technique. Two piezoelectric sensors based on polyvinylidene fluoride (PVDF) films to measure an impact force and vibration response were separately mounted on each glove. Various signal parameters including number of peaks, energy ratio, standard deviation of peak to peak distance, zero-crossing rate, and integral value of peaks were examined to develop a regression-estimated model. When using SMLR (Stepwise Multiple Linear Regression) analysis in SAS, three parameters, i.e., zeros value, number of peaks, and standard deviation of peaks were selected as usable factors with a coefficient of determination ($r^2$) of 0.92 and a standard error of calibration (SEC) of 0.15. In the validation tests using twenty watermelon samples (sound 9, defective 11), the developed model provided good capability showing a classification accuracy of 95%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.9
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• pp.806-813
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• 2015

In order to satisfy several conditions with respect to size, weight, and costs, automotive radars use an antenna consisting of a small number of receiving channels. If RELAX technique is applied to the automotive radars, angles of targets located in antenna beam can be estimated as well as the number of the targets. However, a small number of receiving channels in the antenna leads to inaccurate spectral estimation in angle domain, which in turn degrades performance of RELAX technique. Therefore, in this study, root-MUSIC technique coupled with MDL criterion is introduced to decide accurate angles of targets in antenna beam. In simulations, we show superior performance of proposed scheme using simulation results when three point targets are located in antenna beam.