• Tuberculosis and Respiratory Diseases
• /
• v.83 no.3
• /
• pp.211-217
• /
• 2020

The gold standard method for diagnosis of tuberculosis is the isolation of Mycobacterium tuberculosis through culture, but there is a probability of cross-contamination in simultaneous cultures of samples causing false-positives. This can result in delayed treatment of the underlying disease and drug side effects. In this paper, we reviewed studies on false-positive cultures of M. tuberculosis. Rate of occurrence, effective factors, and extent of false-positives were analyzed. Ways to identify and reduce the false-positives and management of them are critical for all laboratories. In most cases, false-positive is occurring in cases with only one positive culture but negative direct smear. The three most crucial factors in this regard are inappropriate technician function, contamination of reagents, and aerosol production. Thus, to reduce false-positives, good laboratory practice, as well as use of whole-genome sequencing or genotyping of all positive culture samples with a robust, extra pure method and rapid response, are essential for minimizing the rate of false-positives. Indeed, molecular approaches and epidemiological surveillance can provide a valuable tool besides culture to identify possible false positives.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.7
• /
• pp.258-266
• /
• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• Proceedings of the KSR Conference
• /
• 1999.05a
• /
• pp.307-314
• /
• 1999

The potential use of ranging sensors for reducing the occurrence of accidents in real environment is explored by many companies and laboratories. Most of the sensors under investigation utilize the FMCW(Frequency Modulated Continuous Wave) waveforms. The automotive environment presents to the FMCW radar sensor a multitude of moving and fixed targets and the sensor must detect and track only the targets which may pose a threat of collision or passengers accident. The sensor must function accurately in the presence of background echoes generated by moving and fixed targets, ground reflections, atmospheric noises, including rains, fog, and, snow and noise generated within the receiver. False detection of the desired target in this environment may issue false alarms. That may be dangerous to the passenger and the vehicle. A high false alarm rate is totally unacceptable. The false alarm mechanism consists of noise peaks, crossing the threshold and the undesired response of the system to off lane targets which are not potentially hazardous to the radar equipped vehicle. This paper presents an improve technique safety performance for driver-less operation using FMCW radar sensors.

• Proceedings of the KIEE Conference
• /
• 2000.07b
• /
• pp.1368-1370
• /
• 2000

The automotive environment presents to the FMCW radar sensor a multitude of moving and fixed targets and the sensor must detect and track only the targets which may pose a threat of collision or passengers accident. The sensor must function accurately in the presence of background echoes generated by moving and fixed targets, ground reflections, atmospheric noises, including rains, fog, and, snow and noise generated within the receiver. False detection of the desired target in this environment may issue false alarms. That may be dangerous to the passenger and the vehicle. A high false alarm rate is totally unacceptable. The false alarm mechanism consists of noise peaks, crossing the threshold and the undesired response of the system to off lane targets which are not potentially hazardous to the radar equipped vehicle. This paper presents an improve technique safety performance for driver-less operation using FMCW radar sensors.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2005.07a
• /
• pp.594-597
• /
• 2005

This paper shows that the defect detection in TFTarray panel can be improved by using newly developed software solution without adding additional hardware instruments. Some issues are reviewed in current TFT array test and new algorithm is explained for detecting more real defects without paying the penalty of reporting more false defects in TFT array test.

• Korean Journal of Clinical Laboratory Science
• /
• v.42 no.2
• /
• pp.86-91
• /
• 2010

We evaluated the performance of a novel screening test, PBP2a MRSA rapid kit (Dinona Inc., Iksan, Korea), for methicillin-resistant Staphylococcus aureus (MRSA) based on a immunochromatographic assay. The test is able to detect penicillin-binding protein 2a (PBP2a) using the nasal specimens from health care workers. The nasal specimens were obtained from 69 healthcare workers and were incubated in enrichment broth followed eight hours incubatin in BHI with cefoxitin $4{\mu}g/mL$. These broth were tested by PBP2a Rapid Kit. The enrichment broths were also directly tested for tube coagulase using the conventional identification method. 19 of 22 MRSA showed positive results by PBP2a rapid test and direct coagulase test (the sensitivity for detection of MRSA, 86.36%). While, 8 of 47 non-MRSA showed false positive results for the two tests. All of the 8 non-MRSA which showed false positive were co-colonizing isolates with MRCNS and MSSA. In addition, 46 of 49 methicillin-resistant staphylococci (MRS) showed positive results for PBP2a MRSA rapid kit (the sensitivity for detection of MRS, 93.8%), and all of 20 non-MRS showed negative results (specificity, 100%). The combination of PBP2a MRSA rapid kit and direct coagulase test showed the good sensitivity for detection of MRSA from anterior nares but frequently showed false positive results from the co-colonizing carrier with MRCNS and MSSA.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of the Korea Society for Simulation
• /
• v.16 no.2
• /
• pp.27-35
• /
• 2007

When sensor networks are deployed in open environments, all the sensor nodes are vulnerable to physical threat. An attacker can physically capture a sensor node and obtain the security information including the keys used for data authentication. An attacker can easily inject false reports into the sensor network through the compromised node. False report can lead to not only false alarms but also the depletion of limited energy resource in battery powered sensor networks. To overcome this threat, Fan Ye et al. proposed that statistical on-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and energy, where security threshold value is the number of message authentication code for verification of false report. In this paper, we propose a fuzzy rule-based system for security threshold determination that can conserve energy, while it provides sufficient detection power in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the probability of a node having non-compromised keys, the number of compromised partitions, and the remaining energy of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2576-2581
• /
• 2003

We consider fault detection and isolation (FDI) problem for inertial navigation systems (INS) which use redundant inertial sensors and propose an FDI method using average of multiple parity vectors which reduce false alarm and wrong isolation, and improve correct isolation. We suggest optimal isolation threshold based on navigation performance, and suggest optimal sample number to obtain short detection time and to enhance detectability of faults little larger than threshold.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.529-532
• /
• 2003

In this paper, we describe new method to detect face in real-time. We use color information, edge information, and binary information to detect candidate regions of eyes from input image, and then extract face region using the detected eye pall. We verify both eye candidate regions and face region using Support Vector Machines(SVM). It is possible to perform fast and reliable face detection because we can protect false detection through these verification processes. From the experimental results, we confirmed the proposed algorithm shows very excellent face detection performance.