False Detection Results Research Articles

A key performance indicator‐relevant approach based on kernel entropy component regression model for industrial system

AbstractKey performance indicator (KPI)‐relevant fault detection method has been raised for decades to hugely increase the economic interest of modern industries. However, the typical data‐driven approaches like the kernel principal component analysis (KPCA) and the kernel entropy analysis (KECA) are inefficient to consider the influence taken by the fault factor on the KPI. Thus, in this work, an algorithm called the kernel entropy regression (KECR) is proposed to enhance the interpretability between the fault and the KPI. The proposed algorithm captures the information relevant to the KPI state in the subspace and rewords the decomposition of the KECA method. The angular structure of the KECR method achieves an accurate partition for process variables to hugely decrease false detection results. In the end, an industrial case is utilized to demonstrate the effectiveness of the KECR method.

A Simultaneous Localization and Mapping Approach Based on Detection of Magnetic Beacons

In this letter, a novel-feature-based simultaneous localization and mapping (SLAM) approach based on detection of magnetic beacons is presented as a convenient and accurate navigation solution. The magnetic beacons can be accurately detected with unknown background magnetic field by modeling the background field with polynomials and performing joint estimation. By taking the beacons as landmarks, a particle-filter-based SLAM model is established with the dead-reckoning system as state prediction and the relative positions of magnetic beacons as measurement. The potential false detection results are excluded to improve the robustness of the system and reduce computation cost. Experiments based on real-world data are performed to validate the effectiveness and accuracy of the approach.

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications.

The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.

D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos

Video-based train driver operation monitoring is one of the emerging requirements for train safety management and driving operation regularization. Recent years, deep learning methods such as Faster R-CNN have achieved excellent detection performance on images. However, they are not specially designed for object detection from videos, especially for those train drivers who often perform tiny moving in the monitoring video. Spatial and temporal information of videos are not fully explored together to solve this problem. In this paper, a new framework D-STC is proposed to handles the complex situations in train cab and detect train drivers from videos in a more reliable way. The proposed framework first utilizes fine tuning Faster R-CNN framework to detect the train drivers as the initial detection results. Then, the initial detection results of each frame is processed further to suppress false detection results by using the customized spatial constraints. Finally, an optimal threshold adjustment mechanism is presented to improve detection accuracy for the whole video sequence. The D-STC framework improves the accuracy of train driver detection and fully guarantees the detection speed for videos. Experimental results demonstrate the effectiveness of the proposed framework.

An Efficient Method for Cleaning Dirty-Events over Uncertain Data in WSNs

Event detection in wireless sensor networks (WSNs) has attracted much attention due to its importance in many applications. The erroneous abnormal data generated during event detection are prone to lead to false detection results. Therefore, in order to improve the reliability of event detection, we propose a dirty-event cleaning method based on spatio-temporal correlations among sensor data. Unlike traditional fault-tolerant approaches, our method takes into account the inherent uncertainty of sensor measurements and focuses on the type of directional events. A probability-based mapping scheme is introduced, which maps uncertain sensor data into binary data. Moreover, we give formulated definitions of transient dirty-event (TDE) and permanent dirty-event (PDE), which are cleaned by a novel fuzzy method and a collaborative cleaning scheme, respectively. Extensive experimental results show the effectiveness of our dirty-event cleaning method.

Localisation et détection de stents sur des images rayons-X bas contraste

Stents are widely used to treat stenosis. Stent expansion is suspected to be an important factor of restenosis. The problem is that, in day to day practice, correct stent expansion is hard to assess. In X-ray images, stents are visible only with a low contrast. We propose to locate and detect them automatically in these images by applying dedicated algorithms. We characterize the problem of detecting stents in X-ray images as a crease detection problem. We compare classical image processing approaches. We propose two specific algorithms, one belonging to gray-level values methods and the other one based on adapted filtering. Comparison is made on the false detection results. Results have been tested on a hundred image database. In a second time, we propose a multilocal fuzzy algorithm to detect struts in a more accurate way.