• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1117-1122
• /
• 2017

Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.1
• /
• pp.395-405
• /
• 2012

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

• MALSORI
• /
• no.66
• /
• pp.117-130
• /
• 2008

This paper presents a method for automatically detecting Korean prosodic boundaries using both acoustic and grammatical information for the performance improvement of speech information processing systems. While most of previous works are solely based on grammatical information, our method utilizes not only grammatical information constructed by a Maximum-Entropy-based grammar model using 10 grammatical features, but also acoustical information constructed by a GMM-based acoustic model using 14 acoustic features. Given that Korean prosodic structure has two intonationally defined prosodic units, intonation phrase (IP) and accentual phrase (AP), experimental results show that the detection rate of AP boundaries is 82.6%, which is higher than the labeler agreement rate in hand transcribing, and that the detection rate of IP boundaries is 88.7%, which is slightly lower than the labeler agreement rate.

• Convergence Security Journal
• /
• v.13 no.1
• /
• pp.3-10
• /
• 2013

In this paper, reliability software model considering fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the S-shaped distribution model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model was used. In a software failure data analysis considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of failure time data and reliability make out.

• Journal of Multimedia Information System
• /
• v.7 no.2
• /
• pp.125-136
• /
• 2020

Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.43 no.1
• /
• pp.83-88
• /
• 2010

Acoustic telemetry is a useful method to investigate fish behavior and is widely used to obtain biological information. In this study, the detection ability of a mooring-type acoustic telemetry system and the seasonal changes were studied for survey design and data analysis. The system detection range was examined with an underwater noise model, and seasonal changes were estimated with a ray-tracing program and underwater temperature profile data. The field experiment was conducted with two sets of pingers and six receivers to estimate the difference in detection rate by installation depth and to compare the model estimate. Results indicated that the long-range detection ability of the acoustic telemetry system was significantly affected by underwater temperature. The detection rate rapidly decreased near the sea surface or bottom despite that the near-range Signal to noise ratio was sufficient.

• ETRI Journal
• /
• v.41 no.4
• /
• pp.437-451
• /
• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Journal of Information Processing Systems
• /
• v.15 no.1
• /
• pp.137-150
• /
• 2019

Although the research of immune-based anomaly detection technology has made some progress, there are still some defects which have not been solved, such as the loophole problem which leads to low detection rate and high false alarm rate, the exponential relationship between training cost of mature detectors and size of self-antigens. This paper proposed an intrusion detection method based on changes of antibody concentration in immune response to improve and solve existing problems of immune based anomaly detection technology. The method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in the immune system drawing the experience of cloud model, and divides the risk levels to guide immune responses. Experimental results show that the method has better detection performance and adaptability than traditional methods.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1992.06a
• /
• pp.43-48
• /
• 1992

In the area of speech vocoder systems, the MBE vocoder allows the high quality and low bit rate. In the MBE parameters detection, the dicision methods of V/UV region proposed until now are dependent highly to the other parameters, fundamental frequency and formant information. In this paper, thus, we propose a new V/UV detection method that uses a zero-crossing rate of flatten harmonices spectrum. This method can reduce the influences of the other parameters for the V/UV regions detection.

• Investigative and Clinical Urology
• /
• v.59 no.6
• /
• pp.363-370
• /
• 2018

Purpose: The aim of this study is to describe the technique and to report early results of transperineal magnetic resonance imaging and ultrasonography (MRI-US) fusion biopsy. Materials and Methods: A total of 75 patients underwent MRI-US fusion transperineal biopsy. Targeted biopsy via MRI-US fusion imaging was carried out for cancer-suspicious lesions with additional systematic biopsy. Detection rates for overall and clinically significant prostate cancer (csPCa) were evaluated and compared between systematic and targeted biopsy. In addition, further investigation into the detection rate according to prostate imaging reporting and data system (PI-RADS) score was done. Results of repeat biopsies were also evaluated. Results: Overall cancer detection rate was 61.3% (46 patients) and the detection rate for csPCa was 42.7% (32 patients). Overall detection rates for systematic and targeted biopsy were 41.3% and 57.3% (p<0.05), respectively. Detection rates for csPCa were 26.7% and 41.3%, respectively (p<0.05). The cancer detection rates via MRI fusion target biopsy were 30.8% in PI-RADS 3, 62.1% in PI-RADS 4 and 89.4% in PI-RADS 5. Rates of csPCa missed by targeted biopsy and systematic biopsy were 0.0% and 25.0%, respectively. The cancer detection rate in repeat biopsies was 61.1% (11 among 18 patients) in which 55.5% of cancer suspected lesions were located in the anterior portion. Conclusions: Transperineal MRI-US fusion biopsy is useful for improving overall cancer detection rate and especially detection of csPCa. Transperineal MRI-US targeted biopsy show potential benefits to improve cancer detection rate in patients with high PIRADS score, tumor located at the anterior portion and in repeat biopsies.