• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.276-280
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• 2016

A fixed rate speech coder based on the filter bank and the non-uniform sampling technique is proposed. The non-uniform sampling is achieved by the detection of inflection points (IPs). A speech block is band passed by the filter bank, and the subband signals are processed by the IP detector, and the detected IP patterns are compared with entries of the IP database. For each subband signal, the address of the closest member of the database and the energy of the IP pattern are transmitted through channel. In the receiver, the decoder recovers the subband signals using the received addresses and the energy information, and reconstructs the speech via the filter bank summation. As results, the coder shows fixed data rate contrary to the existing speech coders based on the non-uniform sampling. Through computer simulation, the usefulness of the proposed technique is confirmed. The signal-to-noise ratio (SNR) performance of the proposed method is comparable to that of the uniform sampled pulse code modulation (PCM) below 20 kbps data rate.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.517-524
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• 2015

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Considering sparse signal feature, two novel compressed sensing schemes are proposed, which can reduce compressed sampling rate in contrast to the traditional scheme. One algorithm is dynamically adjusting compression ratio based on modulation recognition and identification of symbol rate, which can reduce compression ratio. Furthermore, without priori information of the modulation and symbol rate, another improved algorithm is proposed with the application potential in practice, which does not need to reconstruct the signals. The improved algorithm is divided into two stages, which are the approaching stage and the monitoring stage. The overall sampling rate can be dramatically reduced without the performance deterioration of the spectrum detection compared to the conventional static compressed sampling rate algorithm. Numerous results show that the proposed compressed sensing technique can reduce sampling rate by 35%, with an acceptable detection probability over 0.9.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5153-5157
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• 2016

Objective: The benefits of screening and early detection of breast cancer, including reduced morbidity and mortality, have been well-reported in the literature. In 2011, a breast cancer screening program was launched in Meknes-Tafilalt region of Morocco. The aim of this study was to evaluate the early performance indicators of this program. Materials and Methods: This retrospective evaluative study was conducted between April 2012 and December 2014, in Meknes-Tafilalt region of Morocco. Several performance indicators of the breast cancer screening program were calculated: the compliance rate, the positivity rate, the referral rate, the cancer detection rate and the organizational indicators. Results: During 2012-2014, a total of 184,951 women participated in the breast cancer screening program. The compliance rate was 26%, the positive rate was 3.3%, the referral rate was 36.7%, and the cancer detection rate was 1.2 per 1,000 women. The median time between the date of clinical breast examination and the date of biopsy (or cyto-puncture) was 36 days. The median time between the date of positive mammography and the date of biopsy (or cyto-puncture) was 6 days. The median time between the date of clinical breast examination and the date of the first received treatment was 61 days. Conclusions: The program needs better monitoring, as well as implementation of quality assurance tools to improve performance in our country.

• Current Research on Agriculture and Life Sciences
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• v.32 no.4
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• pp.231-235
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• 2014

This study performed a benefit cost analysis of an automatic eggshell crack detection system. Based on various cost benefit analysis methods, including the net present value (NPV), internal rate of return (IRR), and benefit cost ratio (B/C Ratio), the automatic eggshell crack detection system was confirmed to have economic validity. The NPVs were 175,035,645 won at a 5% discount rate and 129,082,393 won at a 10% discount rate. Plus, the IRRs were 0.686 at a 5% discount rate and 0.660 at a 10% discount rate. Finally, the B/C ratios were 1.981 at a 5% discount rate and 1.900 at a 10% discount rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.703-711
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• 2016

IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.203-209
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• 2021

With the recent development of satellite sensor technology, high-spatial-resolution imagery of various spectral wavelength bands have become possible. Worldview-3 satellite sensor provides panchromatic images with high-spatial-resolution and VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) bands with low-spatial-resolution, so it can be used in various fields such as defense, environment, and surveying. In this study, mineral detection was performed using Worldview-3 satellite imagery. In order to effectively utilize the VNIR and SWIR bands of the Worldview-3 satellite image, the sharpening technique was applied to the spatial resolution of the panchromatic image. To confirm the utility of SWIR bands for mineral detection, mineral detection using only VNIR bands was performed and comparatively evaluated. As the mineral detection technique, SAM (Spectral Angle Mapper), a representative similarity technique, was applied, and the pixels detected as minerals were selected by applying an empirical threshold to the analysis result. Quantitative evaluation was performed using reference data on the results of similarity analysis to evaluate the accuracy of mineral detection. As a result of the accuracy evaluation, the detection rate and false detection rate of mineral detecting using SWIR bands were calculated to be 0.882 and 0.011, respectively, and the results using only VNIR bands were 0.891 and 0.037, respectively. It was found that the detection rate when the SWIR bands were additionally used was lower than that when only the VNIR bands were used. However, it was found that the false detection rate was significantly reduced, and through this, it was possible to confirm the applicability of SWIR bands in mineral detection.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1149-1151
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• 2003

We performed a validation study of the MODIS active fire detection algorithm using high resolution SPOT image as the reference data set. Fire with visible smoke plumes are detected in the SPOT scenes, while the hotspots in MODIS data are detected using NASA's new version 4 fire detection algorithm. The detection performance is characterized by the commission error rate (false alarms) and the omission error rate (undetected fires). In the Sumatra and Kalimantan study area, the commission rate and the omission rate are 27% and 34% respectively. False alarms are probably due to recently burnt areas with warm surfaces. False negative detection occur where there are long smoke plumes and where fires occur in densely vegetated areas.