• 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.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.215-217
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• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• The Korean Journal of Cytopathology
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• v.8 no.1
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• pp.11-19
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• 1997

Clinical lymphadenopathies are subjected to fine needle aspiration cytology(FNAC) for diagnosing not only benign lesions but also malignant ones, as the first diagnostic procedure. While the diagnostic reliability in metastatic carcinoma is high, it is difficult to differentiate malignant lymphoma from reactive conditions. We evaluated the diagnostic reliability of FNAC in lymphadenopathy, and discuss the diagnostic limitation and its place in clinical practice in this study, Over 8 years from January 1988, FNAC of 1,216 lymphadenopathies were analyzed and among them 170 cases were compared with histopathology. The results are as follows. 1. Of ail the cases, 890 cases(73.2%) were diagnosed cytologically as benign, 312 cases(25.7%) as malignant, and 14 cases(1.1%) as unsatisfactory material. Reactive hyperplasia was diagnosed in 585 cases(65.7%) of the benign lesions, and among the malignant diseases, metastatic carcinoma was diagnosed in 248 cases(79.5%), and malignant lymphoma in 62 cases(19.9%). 2. The overall diagnostic accuracy was 89.2%, and no false positive case and 9 false negative results were observed among 170 cases which were proven by histopathology. Six cases of sampling error of false negative diagnoses included 3 of metastatic carcinomas and 3 of malignant lymphomas. The causes were difference between aspiration and biopsy site, poor fixation, or scanty cellularity with bloody smear. All 3 cases of misinterpretation error were malignant lympliomas, one of mixed type on biopsy which was diagnosed as reactive hyperplasia cytologically. In summary, FNAC technique is thought to be useful in the initial diagnosis of lymphadenopathies as well as in the follow-up of patients with known malignancy. Although the results of malignant lymphoma was less accurate than other malignant lesions, the application of strict cytologic criteria or lymphoid marker studies of aspiration material will reduce the false negative rate.

• 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.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.

• The Korean Journal of Cytopathology
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• v.19 no.2
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• pp.111-118
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• 2008

Background : Cervicovaginal cytology is a screening test of uterine cervical cancer. The sensitivity of cervicovaginal cytology is less than 50%, but studies of cytologic/histologic correlation are limited. We analyzed the diagnostic accuracy of cervicovaginal cytology in the detection of the squamous epithelial lesions of the uterine cervix and investigate the cause of diagnostic discordance. Materials and Methods : We collected a total of 481 sets of cervicovaginal cytology and biopsies over 5 years. The cytologic diagnoses were categorized based on The Bethesda System and the histologic diagnoses were classified as negative, flat condyloma, cervical intraepithelial neoplasia (CIN) I, CIN II, CIN III, or squamous cell carcinoma. Cytohistologic discrepancies were reviewed. Results: The concordance rate between the cytological and the histological diagnosis was 79.0%. The sensitivity and specificity of cervicovaginal cytology were 80.6% and 92.6%, respectively. Its positive predictive value and negative predictive value were 93.7% and 77.7%, respectively. The false negative rate was 19.4%. Among 54 false negative cytology cases, they were confirmed by histology as 50 flat condylomas, 2 CIN I, 1 CIN III, and 1 squamous cell carcinoma. The causes of false negative cytology were sampling errors in 75.6% and interpretation errors in 24.4%. The false positive rate was 7.4%. Among 15 false positive cytology cases, they were confirmed by histology as 12 atypical squamous cells of undetermined significance (ASCUS) and 3 low grade squamous intraepithelial lesions (LSIL). The cause of error was interpretation error in all cases. The overall diagnostic accuracy of cervicovaginal cytology was 85.7%. Conclusions : Cervicovaginal cytology shows high overall diagnostic accuracy and is a useful primary screen of uterine cervical cancer.

• Journal of the Korean Society for information Management
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• v.26 no.4
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• pp.319-336
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• 2009

Semantic web technology is the evolution of current World Wide Web including a machine-understandable knowledge database, ontology, it may be enable machine and people to work together. However, problems arise when we try to communicate with different data, which are annotated by different ontologies created by different people with different concepts. Thus, to communicate between ontologies, it needs to align between heterogeneous ontologies. When it is aligned between concept nodes of heterogeneous ontologies, one of main problems is a misalignment situation caused by false negative of automatic ontology mapping. So, in this paper, we present a new method to minimize the false negative error in the process of aligning concept nodes of different ontology.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.194-201
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• 2003

In speech recognition, confidence measuring is to decide whether it can be accepted as the recognized results or not. The confidence is measured by integrating frames into phone and word level. In case of word recognition, the confidence measuring verifies the results of recognition and Out-Of-Vocabulary (OOV). Therefore, the post-processing could improve the performance of recognizer without accepting it as a recognition error. In this paper, we measure the confidence modifying log likelihood ratio (LLR) which was the previous confidence measuring. It accumulates only those which the log likelihood ratio is negative when integrating the confidence to phone level from frame level. When comparing the verification performance for the results of word recognizer with the previous method, the FAR (False Acceptance Ratio) is decreased about 3.49% for the OOV and 15.25% for the recognition error when CAR (Correct Acceptance Ratio) is about 90%.