• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.237-242
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• 2011

This paper proposes a detection algorithm for each section which detects the voiced section, unvoiced section, and the silence section at each frame using a multi-layer perceptron neural network. First, a power spectrum and FFT (fast Fourier transform) coefficients obtained by FFT are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and FFT coefficients. In this experiment, the performance of the proposed algorithm for detection of the voiced section, unvoiced section, and silence section was evaluated based on the detection rates using various speeches, which are degraded by white noise and used as the input data of the neural network. In this experiment, the detection rates were 92% or more for such speech and white noise when training data and evaluation data were the different.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.100-105
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• 2014

This paper comparatively analyze to integration case to have a influence detection range estimation about radar cross section in radar system. This paper estimate detection range used to probability of detection in radar equation that used to swerling case 1 in case of radar cross section is small and used to swerling case 3 in case of radar cross section is large. Through simulation, coherent integration and non-coherent integration about swerling case difference were comparatively analyzed. Through simulation, non-coherent integration case is outstanding detection range and we known that coherent integration don't suitable for detection range estimation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.113-117
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• 2014

This paper comparatively analyze to integration case to have a influence detection range estimation about radar cross section in radar system. This paper estimate detection range used to probability of detection in radar equation that used to swerling case 1 in case of radar cross section is small and used to swerling case 3 in case of radar cross section is large. Through simulation, coherent integration and non-coherent integration about swerling case difference were comparatively analyzed. In the result of comparative analysis, non-coherent integration case is outstanding detection range and we known that coherent integration don't suitable for detection range estimation.

• ETRI Journal
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• v.45 no.5
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• Phonetics and Speech Sciences
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• v.10 no.3
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• pp.19-29
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• 2018

We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours' worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.107_108
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• 2009

In this paper, a fault section detection algorithm to be considered variety measurement devices is proposed in ungrounded diatribution network. Ungrounded network is different from grounded netork. It's that a fault current doesn't generate when a single grounded fault by characteristic of ungrounded network. So, a fault section detection is very difficult. Thus, in this paper, a fault section detection method is proposed by data from variety measurement devices. The method is proved by matlap simulink.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.47-63
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• 2011

A traffic detection system can be deemed as a traffic data and information collection system to serve traffic policies, traffic management, and user services. The system plays a crucial role in verifying whether or not the current traffic system has issues or problems by checking out traffic data. In addition, the system does so in finding out a point or a section where an issue or a problem has occurred, if any, and in examining the causes of the issue or problem, the extent of its impact that has occurred and spread, and a method for resolving it. However, the existing point detection system of Korea has too many flaws. In order to fix the flaws, in this paper, the theoretical characteristics of the section detection system were researched in relation to the calculation of travel time. In addition, the travel time of probe cars was obtained by field survey, and it was compared to that of spot and section detection data. Then, simulation was performed to determine the optimal section detection interval. In conclusion, introduction of optimal section detection system was examined in order to achieve the advanced road management including traffic policy, traffic management, and user services.

• Journal of the Korea Society for Simulation
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• v.24 no.1
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• pp.25-34
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• 2015

It is necessary for us to undergo trial and error for eliciting the rational requirement of the acquisition of weapon systems, but the M&S is general approach due to costs and risk of the development. In addition to the acquisition of weapon systems, M&S is extensively employed in the analysis and the training of developed weapon systems. The ADD (Agency for Defense Development) has developed DEVS integrated development environment (QUEST) that provides M&S general ground technique composed of simulation model implementation services, simulation result analysis services, and simulation interface services. This paper describes the interface architecture and the implementation of torpedo target detection section interface simulation system using QUEST. The torpedo target detection section interface simulation system is composed of torpedo target detection section which calculates a result of target detection and the QUEST scenario generator which provides simulation scenario for performance test of the torpedo target detection section. The interface architecture of torpedo target detection section interface simulation system is designed to verify the interface and performance of the torpedo target detection section by linking with the QUEST scenario generator.

• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.1
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• pp.100-105
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• 2011

Generally, because received signals from radar are very bulky, the data are divided into manageable size called section, and sections are distributed into several digital signal processors. And then, target detection algorithms are applied simultaneously in each processor. CFAR(Constant False Alarm Rate) algorithm, which is the most popular target detection algorithm, can estimate accurate threshold values to determine which signals are targets or noises within center-cut of section allocated to each processor. However, its estimation precision is diminished in section edge data because of insufficient surrounding data to be referred. Especially this edge problem of CFAR is too serious if we have many sections to be processed, because it causes many false alarms in most every section edges. This paper describes false alarm issues on MCA(Minimum Cell Average)-CFAR, and proposes a false alarm elimination method by changing section size alternatively. Real received data from multi-function radar were used to evaluate a proposed method, and we show that our method drastically decreases false alarms without missing real targets, and improves detection performance.