• Title/Summary/Keyword: False Detection

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Research Progress in Potential Urinary Markers for the Early Detection, Diagnosis and Follow-up of Human Bladder Cancer

  • Wang, Hai-Feng;Wang, Jian-Song
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.5
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    • pp.1723-1726
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    • 2012
  • Objective: To summarize and evaluate various urinary markers for early detection, diagnosis and follow-up of human bladder cancer. Methods: A MEDLINE and PUBMED search of the latest literature on urinary markers for bladder cancer was performed. We reviewed these published reports and made a critical analysis. Results: Most urinary markers tend to be less specific than cytology, yielding more false-positive results, but demonstrating an advantage in terms of sensitivity, especially for detecting low grade, superficial tumors. Some tumor markers appear to be good candidates for early detection, diagnosis, and follow-up of human bladder cancer. Conclusion: A number of urinary markers are currently available that appear to be a applicable for clinical detection, diagnosis, and follow-up of bladder cancer. However, further studies are required to determine their accuracy and widespread applicability.

SHIP DETECTION APPROACH BASED ON CROSS CORRELATION FROM ENVISAT ASAR AP DATA

  • Yang, Chan-Su;Ouchi, Kazuo
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.262-265
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    • 2007
  • Preliminary results are reported on ship detection using coherence images computed from cross-correlating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, cross-correlation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look Images.

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SHIP DETECTION APPROACH BASED ON CROSSCORRELATION FROM DUAL-POLARIZATION DATA (ASAR AP 다중편파 및 MULTI-LOOK 에 의한 선박탐지 연구)

  • Yang, Chan-Su;Ouchi, Kazuo
    • Proceedings of the KSRS Conference
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    • 2008.03a
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    • pp.180-184
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    • 2008
  • Preliminary results are reported on ship detection using coherence images computed from crosscorrelating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, crosscorrelation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look images.

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Multilevel multiuser detection system in multi-cell MFSK/FH-CDMA environment

  • ;Ryuji Kohno
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.23 no.4
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    • pp.865-872
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    • 1998
  • This paper proposes a multiuse detection system in a multi-cell M-ary Frequency Shift Keying(MFSK)/frequency hopping(FH)-Code Division Multiple Access(CDMA) environment, in which the channel model is an OR-channel and in the reverse link. We have proposed a multiuse detection system in a single cell. However, this sitye is not adequate to detect multiuser in a multi-cell environment. Therefore, we propose a multiuser detection system based on 3 level OR decision with two threholds. The proposed detection system can delete interference as well as intra-cell interference, receive the weakened desired signal and reject the false alarm. computer simulation shows the performance improvement.

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Performance Evaluation of Conflict Detection Schemes for Concurrent Temporal Tranactions (시간지원 크랙잭션을 위한 충돌 검출 기법의 성능평가)

  • 구경이;하봉옥;김유성
    • Journal of KIISE:Software and Applications
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    • v.26 no.1
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    • pp.80-80
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    • 1999
  • As Temporal DataBase Systems(TDBSs) manages both the historical versions and the current version of each data item, a temporal transaction may access more data records than atransaction in traditional database systems. Hence, the concurrency control subsystem of temporaldatabase management system should be able to correctly and efficiently detect actual conflicts amongconcurrent temporal transactions while the cost of detecting conflicts is maintained in low levelwithout detecting false conflicts which cause severe degradation of system throughput.In this paper, Two-Level Conflict Detection(TLCD) scheme is proposed for efficient conflictdetection between concurrent temporal transactions in TDBs. In the proposed TLCD scheme, sincechecking conflict between concurrent temporal transactions is performed at two levels, i, e., logicallevel and physical level, conflicts between concurrent temporal transactions are efficiently and correctlydetected,Furthermore, we also evaluate the performance of the proposed TLCD scheme with those oftraditional conflict detection schemes, logical-level conflict detection scheme and physical-level conflictdetection scheme by simulation approach, The result of the simulation study shows that the proposedTLCD scheme outperforms the previous conflict detection schemes with respect to the averageresponse time.

A Speaker Change Detection Experiment that Uses a Statistical Method (통계적 기법을 이용한 화자변화 검출 실험)

  • Lee, Kyong-Rok;Kim, Jin-Young
    • Speech Sciences
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    • v.8 no.4
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    • pp.59-72
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    • 2001
  • In this paper, we experimented with speaker change detection that uses a statistical method for NOD (News On Demand) service. A specified speaker's change can find out content of each data in speech if analysed because it means change of data contents in news data. Speaker change detection acts as preprocessor that divide input speech by speaker. This is an important preprocessor phase for speaker tracking. We detected speaker change using GLR(generalized likelihood ratio) distance base division and BIC (Bayesian information criterion) base division among matrix method. An experiment verified speaker change point using BIC base division after divide by speaker unit using GLR distance base method first. In the experimental result, FAR (False Alarm Rate) was 63.29 in high noise environment and FAR was 54.28 in low noise environment in MDR (Missed Detection Rate) 15% neighborhood.

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Earthquake Event Auto Detection Algorithm using Accumulated Time-Frequency Changes and Variable Threshold (시간-주파수 누적 변화량과 가변 임계값을 이용한 지진 이벤트 자동 검출 알고리즘)

  • Choi, Hun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.8
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    • pp.1179-1185
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    • 2012
  • This paper presents a new approach for the detection of seismic events using accumulated changes on time-frequency domain and variable threshold. To detect seismic P-wave arrivals with rapidness and accuracy, it is that the changes on the time and the frequency domains are simultaneously used. Their changes are parameters appropriated to reflect characteristics of earthquakes over moderate magnitude(${\geq}$ magnitude 4.0) and microearthquakes. In addition, adaptively controlled threshold values can prevent false P-wave detections due to low SNR. We tested our method on real earthquakes those have various magnitudes. The proposed algorithm gives a good detection performance and it is also comparable to STA/LTA algorithm in computational complexity. Computer simulation results shows that the proposed algorithm is superior to the conventional popular algorithm (STA/LTA) in the seismic P-wave detection.

Implementation of Lane Departure Warning System using Lightweight Deep Learning based on VGG-13 (VGG-13 기반의 경량화된 딥러닝 기법을 이용한 차선 이탈 경고 시스템 구현)

  • Kang, Hyunwoo
    • Journal of Korea Multimedia Society
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    • v.24 no.7
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    • pp.860-867
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    • 2021
  • Lane detection is important technology for implementing ADAS or autonomous driving. Although edge detection has been typically used for the lane detection however, false detections occur frequently. To improve this problem, a deep learning based lane detection algorithm is proposed in this paper. This algorithm is mounted on an ARM-based embedded system to implement a LDW(lane departure warning). Since the embedded environment lacks computing power, the VGG-11, a lightweight model based on VGG-13, has been proposed. In order to evaluate the performance of the LDW, the test was conducted according to the test scenario of NHTSA.

A Video Smoke Detection Algorithm Based on Cascade Classification and Deep Learning

  • Nguyen, Manh Dung;Kim, Dongkeun;Ro, Soonghwan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.12
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    • pp.6018-6033
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    • 2018
  • Fires are a common cause of catastrophic personal injuries and devastating property damage. Every year, many fires occur and threaten human lives and property around the world. Providing early important sign for early fire detection, and therefore the detection of smoke is always the first step in fire-alarm systems. In this paper we propose an automatic smoke detection system built on camera surveillance and image processing technologies. The key features used in our algorithm are to detect and track smoke as moving objects and distinguish smoke from non-smoke objects using a convolutional neural network (CNN) model for cascade classification. The results of our experiment, in comparison with those of some earlier studies, show that the proposed algorithm is very effective not only in detecting smoke, but also in reducing false positives.

Haze Scene Detection based on Hue, Saturation, and Dark Channel Distributions

  • Lee, Y.;Yang, Seungjoon
    • International Journal of Advanced Culture Technology
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    • v.8 no.4
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    • pp.229-234
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    • 2020
  • Dehazing significantly improves image quality by restoring the loss of contrast and color saturation for images taken in the presence. However, when applied to images not taken according to the prior information, dehazing can cause unintended degradation of image quality. To avoid unintended degradations, we present a hazy scene detection algorithm using a single image based on the distributions of hue, saturation, and dark channel. Through a heuristic approach, we find out statistical characteristics of the distribution of hue, saturation, and dark channels in the hazy scene and make a detection model using them. The proposed method can precede the dehazing to prevent unintended degradation. The detection performance evaluated with a set of test images shows a high hit rate with a low false alarm ratio. Ultimately the proposed method can be used to control the effect of dehazing so that the dehazing can be applied to wide variety of images without unintended degradation of image quality.