scholarly journals An Alarm Method for a Loose Parts Monitoring System

2012 ◽  
Vol 19 (4) ◽  
pp. 753-761 ◽  
Author(s):  
Yanlong Cao ◽  
Yuanfeng He ◽  
Huawen Zheng ◽  
Jiangxin Yang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Monitoring System ◽  
Power Plants ◽  
Detection Rate ◽  
Recognition Rate ◽  
Support Vector ◽  
Linear Predictive Coding ◽  
Loose Parts ◽  
The Impact

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate

scholarly journals Effective Smoke Detection Using Spatial-Temporal Energy and Weber Local Descriptors in Three Orthogonal Planes (WLD-TOP)

2018 ◽  
Vol 18 (01) ◽  
pp. e05 ◽  
Author(s):  
John Adedapo Ojo ◽  
Jamiu Alabi Oladosu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Robot Vision ◽  
Fire Detection ◽  
Support Vector ◽  
False Alarms ◽  
High Detection Rate ◽  
Local Descriptor ◽  
Video Frames

Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was implemented in MATLAB 8.1.0.604 (R2013a). Accuracy of 99.30%, detection rate of 99.28% and false alarm rate of 0.65% were obtained when tested with some online videos. The output of this work would find applications in early fire detection systems and other applications such as robot vision and automated inspection.

A Method for Mass Estimation of Loose Parts in Nuclear Power Plant Based on Support Vector Machine

2010 ◽  
Vol 437 ◽  
pp. 384-388
Author(s):  
Hua Wen Zheng ◽  
Yan Long Cao ◽  
Jiang Xin Yang ◽  
Yuan Feng He
Keyword(s):  
Support Vector Machine ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Predictive Coding ◽  
Support Vector ◽  
Linear Predictive Coding ◽  
Mass Estimation ◽  
Loose Parts ◽  
The Impact

A new method for mass estimation of loose parts in nuclear power plant (NPP) based on the support vector machine (SVM) was proposed. It includes analyses of the relationship between the impact signals’ frequency spectrum and the mass of loose part, then formation of a vector consisting of linear predictive coding (LPC) parameters, which represent the shape of spectrum of impact signal. Using the vector as input data and the mass of loose part as the output data to train the SVM, the mass estimation can be done by the trained SVM model. Experimental results show that the method has higher accuracy and easier to achieve than the traditional methods. It provides a new way for mass estimation of loose part in NPP.

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

scholarly journals A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4033 ◽  
Author(s):  
Yoo ◽  
Wang ◽  
Seol ◽  
Lee ◽  
Chung ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Tracking System ◽  
Radar System ◽  
Ultra Wideband ◽  
Radar Signal ◽  
High Detection Rate ◽  
Concrete Walls ◽  
Uwb Radar

Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

scholarly journals Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Ganapathy ◽  
P. Yogesh ◽  
A. Kannan
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Outlier Detection ◽  
Intelligent Agent ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Set ◽  
Agent Based ◽  
Multiclass Svm

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Optimal Sensor Placement for Damage Detection in Complex Structures

2009 ◽  
Author(s):  
Sunilkumar Soni ◽  
Santanu Das ◽  
Aditi Chattopadhyay
Keyword(s):  
False Alarm ◽  
Damage Detection ◽  
False Alarm Rate ◽  
Structural Component ◽  
Detection Rate ◽  
Sensor Placement ◽  
False Alarms ◽  
Optimal Sensor Placement ◽  
Detection Scheme ◽  
Sensing Radius

An optimal sensor placement methodology is proposed based on detection theory framework to maximize the detection rate and minimize the false alarm rate. Minimizing the false alarm rate for a given detection rate plays an important role in improving the efficiency of a Structural Health Monitoring (SHM) system as it reduces the number of false alarms. The placement technique is such that the sensor features are as directly correlated and as sensitive to damage as possible. The technique accounts for a number of factors, like actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio (SNR) and sensing radius. These factors are not independent and affect each other. Optimal sensor placement is done in two steps. First, a sensing radius, which can capture any detectable change caused by a perturbation and above a certain threshold, is calculated. This threshold value is based on Neyman-Pearson detector that maximizes the detection rate for a fixed false alarm rate. To avoid sensor redundancy, a criterion to minimize sensing region overlaps of neighboring sensors is defined. Based on the sensing region and the minimum overlap concept, number of sensors needed on a structural component is calculated. In the second step, a damage distribution pattern, known as probability of failure distribute, is calculated for a structural component using finite element analysis. This failure distribution helps in selecting the most sensitive sensors, thereby removing those making remote contributions to the overall detection scheme.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

Development of a false alarm free, Advanced Loose Parts Monitoring System (ALPS)

2003 ◽  
Vol 43 (1-4) ◽  
pp. 243-251 ◽  
Author(s):  
G. Por ◽  
J. Kiss ◽  
I. Sorosanszky ◽  
G. Szappanos
Keyword(s):  
False Alarm ◽  
Monitoring System ◽  
Loose Parts
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