• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.679-688
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• 2010

Lee (2010) developed a confidence interval for the difference of binomial proportions in two doubly sampled data subject to false-positive errors. The confidence interval seems to be adequate for a general double sampling model subject to false-positive misclassification. However, in many applications, the false-positive error rates could be the same. On this note, the construction of asymptotic confidence interval is considered when the false-positive error rates are common. The coverage behaviors of nine likelihood based confidence intervals are examined. It is shown that the confidence interval based Rao score with the expected information has good performance in terms of coverage probability and expected width.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.403-414
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• 2005

Most of the currently used spam-filters are based on a Bayesian classification technique, where some serious problems occur such as a limited precision/recall rate and the false positive error. This paper addresses a solution to the problems using a modified Bayesian classifier based on chi-square statistics. The resulting spam-filter is more accurate and flexible than traditional Bayesian spam-filters and can be a personalized one providing some parameters when the filter is teamed from training data.

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.108-114
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• 2005

While E-mail has become an important way of communications in IT societies, it creates various social problems due to increase of spam mails. Even though many organizations and corperations have been doing researches to develop spam mail blocking technologies, more cost and system complexities are required because of varieties of blocking technologies. In case of adopting spam blocking technologies, system reliability largely relies on the False-positive error rate with the order of employing spam blocking filters. In this paper, a False-positive mail recovery technique based on privacy information is proposed and implemented in order to improve the reliability of spam locking filters. Through the implemented prototype, recovery procedure for False-positive mails is verified and the results are summarized and analyzed.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.697-706
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• 2012

An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.809-815
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• 2014

As the size of the data is getting larger and larger due to improvement of the telecommunication techniques, it would be main issues to develop and process the database. The bloom filter used to lookup a particular element under the given set is very useful structure because of the space efficiency. In this paper, we introduce the error probabilities in Bloom filter. Especially, we derive the revised false positive rates of the Bloom filter using experimental method. Finally we analyze and compare the original false positive probability of the bloom filter used until now and the false decision probability proposed in this paper.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1289-1300
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• 2009

The double sampling scheme is effective in reducing the sampling cost. However, the doubly sampled data is contaminated by two types of error, namely false-positive and false-negative errors. These would make the statistical analysis more difficult, and it would require more sophisticate analysis tools. For instance, the Wald method for the interval estimation of a proportion would not work well. In fact, it is well known that the Wald confidence interval behaves very poorly in many sampling schemes. In this note, the property of the Wald interval is investigated in terms of the coverage probability and the expected width. An alternative confidence interval based on the Agresti-Coull's approach is recommended.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.107-114
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• 2012

Fire detection system is used for detection and alarm-generation of danger in case of fire. Most fire detection systems being used these days often malfunction from false positive and false negative errors. To improve detection reliability, an integrated fire detection algorithm using multi-senor information of heat, smoke and carbon monoxide detectors is suggested, then built and tested using the LabVIEW environment. Simulated using sensor measurement data offered by National Institute of Standards and Technology (NIST), possibility of reducing false positive and false negative errors is verified.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.7-12
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• 2009

The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.12
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• pp.716-722
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• 2005

In this paper, we propose the audio watermarking algorithm based on the critical band of HAS(human auditory system) without audibly affecting the quality of the watermarked audio and implement the detecting algorithm on the BSS(broadcast synchronizing system) for testing the proposed algorithm. According to the audio quality test, the SNR(signal to noise ratio) of the watermarked audio objectively is 66dB above. In the robustness test, the proposed algorithm can detect the watermark more than $90\%$ from various compression(MP3, AAC), A/D and D/A conversions, sampling rate conversions and especially asynchronizing attacks. The BSS automatically switches the programs between the key station and the local station in broadcasting system. The result of reliability test of implemented system by using the real broadcasting audio has no false positive error during 30 days. Because of detecting once processing per 0.5 second, we can judge that the false positive error does not occur.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.