Reducing Worm Detection Time and False Alarm in Virus Throttling

2005 ◽  
pp. 297-302 ◽  
Author(s):  
Jangbok Kim ◽  
Jaehong Shim ◽  
Gihyun Jung ◽  
Kyunghee Choi
Keyword(s):  
False Alarm ◽  
Detection Time ◽  
Worm Detection

scholarly journals Reducing False Alarm and Shortening Worm Detection Time in Virus Throttling

2005 ◽  
Vol 12C (6) ◽  
pp. 847-854
Author(s):  
Jae-Hong Shim ◽  
Jang-bok Kim ◽  
Hyung-Hee Choi ◽  
Gi-Hyun Jung
Keyword(s):  
False Alarm ◽  
Detection Time ◽  
Worm Detection

scholarly journals End-to-End Airport Detection in Remote Sensing Images Combining Cascade Region Proposal Networks and Multi-Threshold Detection Networks

2018 ◽  
Vol 10 (10) ◽  
pp. 1516 ◽  
Author(s):  
Yuelei Xu ◽  
Mingming Zhu ◽  
Shuai Li ◽  
Hongxiao Feng ◽  
Shiping Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
False Alarm ◽  
Convolutional Neural Networks ◽  
Detection Rate ◽  
Detection Methods ◽  
Detection Time ◽  
Remote Sensing Images ◽  
Threshold Detection ◽  
Airport Detection

Fast and accurate airport detection in remote sensing images is important for many military and civilian applications. However, traditional airport detection methods have low detection rates, high false alarm rates and slow speeds. Due to the power convolutional neural networks in object-detection systems, an end-to-end airport detection method based on convolutional neural networks is proposed in this study. First, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airport images using a limited amount of data. Second, to further improve the detection rate and reduce the false alarm rate, the concepts of “divide and conquer” and “integral loss’’ are introduced to establish cascade region proposal networks and multi-threshold detection networks, respectively. Third, hard example mining is used to improve the object discrimination ability and the training efficiency of the network during sample training. Additionally, a cross-optimization strategy is employed to achieve convolution layer sharing between the cascade region proposal networks and the subsequent multi-threshold detection networks, and this approach significantly decreases the detection time. The results show that the method established in this study can accurately detect various types of airports in complex backgrounds with a higher detection rate, lower false alarm rate, and shorter detection time than existing airport detection methods.

Data Segmentation Criteria Assessment for Fault Detection Techniques Based on Principal Component Analysis for Natural Gas Transmission System

2018 ◽  
Author(s):  
Cinthia Audivet ◽  
Horacio Pinzón ◽  
Jesus García ◽  
Marlon Consuegra ◽  
Javier Alexander ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Natural Gas ◽  
Fault Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Detection Time ◽  
Case Scenario ◽  
Data Segmentation ◽  
Natural Gas Transmission

Statistical analytics, as a data extraction and fault detection strategy, may incorporate segmentation techniques to overcome its underlying limitations and drawbacks. Merging both techniques shall provide a more robust monitoring structure to address the proper identification of normal and abnormal conditions, to improve the extraction of fundamental correlation among variables, and to improve the separation of both main variation and natural variation (noise) subspaces. This additional feature is key to limit the false alarm rate and to optimize the fault detection time when it is implemented on industrial applications. This paper presents an analysis to determine whether a segmentation approach, as a previous step of detection, enhances the fault detection strategies, specifically the principal component analysis performance. The data segmentation criteria assessed in this study includes two approaches: a) Sources (well) of the transmitted natural gas and b) Promigas’ natural gas pipeline division defined by the Energy and Gas Regulation Commission (CREG in Spanish). The performance assessment of segmentation criteria was carried out evaluating the false alarm rate and detection time when the natural gas transmission network presents faults of different magnitude. The results show that the implementation of a segmentation criteria provides an advantage in terms of the detection time, but it depends of the fault magnitude and the number of clusters. The detection time is improved by 25% in the case scenario I, when transition zones are considered. On the other hand, the detection time is slightly better with less than 10% in the case scenario II, where the segmentation is geographical.

Hit and False-Alarm Rates of Selected ABR Indices in Differentiating Cochlear Disorders From Acoustic Tumors

1996 ◽  
Vol 5 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Frank E. Musiek ◽  
Cynthia A. McCormick ◽  
Raymond M. Hurley
Keyword(s):  
False Alarm ◽  
Amplitude Ratio ◽  
High Specificity ◽  
Auditory Brainstem ◽  
Latency Difference ◽  
Cochlear Pathology ◽  
Brainstem Response ◽  
Absolute Latency ◽  
Two Indices ◽  
Low Sensitivity

We performed a retrospective study of 26 patients with acoustic tumors and 26 patients with otologically diagnosed cochlear pathology to determine the sensitivity (hit rate), specificity (false-alarm rate), and efficiency of six auditory brainstem response indices. In addition, a utility value was determined for each of these six indices. The I–V interwave interval, the interaural latency difference, and the absolute latency of wave V provided the highest hit rates, the best A’ values and good utility. The V/I amplitude ratio index provided high specificity but low sensitivity scores. In regard to sensitivity and specificity, using the combination of two indices provided little overall improvement over the best one-index measures.

The Fusion of False Alarm and False Hope

1985 ◽  
Vol 30 (3) ◽  
pp. 207-208
Author(s):  
Jeffrey Z. Rubin
Keyword(s):  
False Alarm

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Chest pain in pregnancy: a false alarm

2019 ◽  
Vol 178 (3) ◽  
Author(s):  
Muhammad Azrai Abu ◽  
Kah T. Chew ◽  
Abdul G. Nur Azurah ◽  
Mohammad Shawal Faizal ◽  
Chai S. Ngiu ◽  
...  
Keyword(s):  
Chest Pain ◽  
False Alarm ◽  
In Pregnancy
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