Predicting Sonar False Alarm Rate Inflation Using Acoustic Modeling and a High-Resolution Terrain Model

Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5&thinsp;% to 96.4&thinsp;% and the false alarm rate mitigated from 11.5&thinsp;% to 3.6&thinsp;% compared with CFAR respectively.

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

TAPPI Journal ◽

10.32964/tj13.1.33 ◽

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.

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Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1634-07 ◽

1998 ◽

Vol 1634 (1) ◽

pp. 56-63 ◽

Cited By ~ 13

Author(s):

Sherif S. Ishak ◽

Haitham M. Al-Deek

Keyword(s):

Neural Networks ◽

False Alarm ◽

False Alarm Rate ◽

Back Propagation ◽

Incident Detection ◽

Traffic Patterns ◽

Traffic Pattern ◽

Detection Algorithms ◽

Fuzzy Art ◽

Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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