Decrease In False Alarm Rate Research Articles

Shallow water source depth discrimination based on a vertical linear array using deep learning

This study aims to explore source depth discrimination in shallow water waveguides using a vertical line array. Due to the similarity in physical characteristics of features and environmental parameters, the feature distributions of shallow and deep sources overlap in feature space. This overlap is further exacerbated by strong background noise, reducing detection reliability. Therefore, a deep learning-based source depth discrimination (DL-SDD) scheme is proposed. Specifically, this scheme is based on a residual structure, embedding channel attention mechanisms into the deep structure to eliminate noise-related information gradually. Furthermore, a specially designed loss function considers inter-class and intra-class distances to achieve compact and mutually distant distributions of source features. When this loss function is applied, the overlap of source feature distributions is suppressed in end-to-end feature learning, leading to a high detection probability. The numerical simulations demonstrate that the proposed DL-SDD outperforms traditional method, achieving a 7 % increase in detection probability and a 15 % decrease in false alarm rate, even near the discrimination depth. Furthermore, the discrimination depth is reduced by nearly half. Experimental results from the South China Sea validate the effectiveness of the proposed DL-SDD.

Multi‐Task Learning for Tornado Identification Using Doppler Radar Data

AbstractTornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events.

Remote anomaly detection for underwater gliders based on multi-feature fusion

As an autonomous platform for long-term observation in the complex ocean environment beyond visual line of sight, the underwater glider (UG) may suffer from some anomalies during the mission. Thus, the anomaly detection of UGs appears to be particularly important for finding malfunction in time and avoiding loss of prototypes. This study considers the limitation of big data transmission in the ocean environment and proposes a remote detection method for Ug anomalies based on multi-feature fusion, which only requires a small amount of data. First, the failure analysis of UGs is performed, and eight characteristic parameters are selected to reflect the anomalies of UGs. Then, the principal component analysis is adopted to fuse the characteristic parameters, which can reduce the dimensions of variables and retain the characteristic information of the original variables to the greatest extent. Finally, the Density-Based Spatial Clustering of Applications with Noise Algorithm is adopted to reduce the false alarm rate of the method. The feasibility of the proposed remote anomaly detection method is verified by the massive historical data of multiple acoustic UGs. It relys on a small amount of data to detect all the abnormal profiles and achieve a decrease in false alarm rate after optimization, which reduce the operational overhead and misson cost.

Modulation of the word frequency effect in recognition memory after an unrelated lexical decision task

• Lexical decision modulates the WFE in a subsequent unrelated recognition task. • An increase in hit rates and false alarms for high frequency words was found in RC. • A decrease in false alarms for the low frequency words was also found in RC. • Changes in drift rate and response criterion of the LBA model explain the WFE. • Previous processing history affects memory processing is subsequent tasks. The natural language frequency of a word is known to influence the ability to perform recognition judgments based on either semantic or episodic memory, an effect commonly known as the word frequency effect (WFE). For episodic recognition specifically, the WFE presents a mirrored pattern with higher hit rates and lower false alarm rates for low frequency words compared to high frequency words. Interestingly, the use of certain study tasks such as judgements of concreteness has been shown to reduce or even abolish the hit rate advantage of low frequency words. In the present study we tested the hypothesis that prior exposure to an unrelated lexical decision task leads to a modulation of the WFE in a subsequent episodic recognition test. Results showed an increase in hit rates and false alarm rates for high frequency words and a decrease in false alarm rates for low-frequency words. Analysis of behaviour with the linear ballistic accumulator model furthermore revealed that this effect was driven by changes in the rate of evidence accumulation of the decision process and in the response threshold. We discuss the relevance of these findings for current theories of memory.

A Stereo Approach to Wildfire Smoke Detection: The Improvement of the Existing Methods by Adding a New Dimension

In this paper, we present a novel approach to visual smoke detection based on stereo vision. General smoke detection is usually performed by analyzing the images from remote cameras using various computer vision techniques. The literature on smoke detection shows a variety of approaches, and the focus of this paper is the improvement of the general smoke detection process by introducing stereo vision. Two cameras are used to estimate the distance and size of the detected phenomena based on stereo triangulation. Using this information, the minimum size and overall dynamics of the detected regions are further examined to ensure the elimination of false alarms induced by various phenomena (such as the movement of objects located at short distances from the camera). Such false alarms could easily be detected by the proposed stereo system, allowing the increase of the sensitivity and overall performance of the detection. We analyzed the requirements of such system in terms of precision and robustness to possible error sources, especially when dealing with detection of smoke at various distances from the camera. For evaluation, three existing smoke detection methods were tested and the results were compared to their newly implemented stereo versions. The results demonstrated better overall performance, especially a decrease in false alarm rates for all tested methods.

Retrieval dynamics of the strength based mirror effect in recognition memory

Abstract The strength based mirror effect (SBME) refers to an increase in hit rates (HR) and a decrease in false alarm rates (FAR) for the test lists that follow a strongly encoded study list. Earlier investigation of accuracy and reaction time distributions by fitting the diffusion model indicated a mirror effect in the drift rate parameter, which was interpreted as an indication of more conservative responses due to a shift in the drift criterion. Additionally, the starting point for the evidence accumulation was found to be more liberal for the strong test lists. In order to further investigate this paradoxical effect of list strength on these two kinds of bias estimated from the diffusion model, we employed the response-deadline procedure which provided a direct assessment of response bias early in retrieval, prior to evidence accumulation. Results from the retrieval functions indicated more liberal response bias in the list strength paradigm with both pure- and mixed-strength study lists. On the contrary, the SBME was observed at the asymptotic accuracy, suggesting that the conservative response bias might be observed later in retrieval when memory evidence has fully accumulated. In addition, comparison of the SBME across pure and mixed lists revealed that the SBME was most prominent in the pure-list paradigm, suggesting that both the differentiation and criterion shift accounts jointly explain the SBME in recognition memory.

Prediction of Satellite Image Sequence for Weather Nowcasting Using Cluster-Based Spatiotemporal Regression

The flawed characterization of transitions between different meteorological structures is often regarded as one of the largest sources of error in weather forecasting. This paper attempts to improve upon the satellite-image-based nowcasting capability of models by coupling a clustering technique into a spatiotemporal autoregression method. Experimental results indicate the superiority of clustering-based regression algorithm in terms of statistically significant skill scores. The tests show an improvement in probability of detection with a decrease in false alarm rate as compared to unclassified predictions. The developed model has also been demonstrated to be useful in nowcasting of convective systems.

Improving Bee Algorithm Based Feature Selection in Intrusion Detection System Using Membrane Computing

Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed ‘intrusion’. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS) to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA), Linear Genetic Programming (LGP), Support Vector Decision Function Ranking (SVDF), Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS) have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC) approach is a better technique. This is because our approach produced very high attack detection rate of 89.11%, classification accuracy of 95.60% and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC) curve was used for results interpretation.

An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System.

An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System.

Study time effects in recognition memory.

We empirically tested the assumption that study time increases recognition accuracy because the storage of information is better when study time is longer as Shiffrin and colleagues have reported, an assumption common to parallel models of recognition. In the present study with 123 subjects, we examined the effect of item strength on four measures: hit rate, false alarm rate, d', and beta, for a single-word recognition task with longer study times than those usually used in the literature. Analysis indicated significant increase for hit rate and d' and a decrease in false alarm rate, as one goes from weak to stronger study conditions, and a change in ln(beta) when study time is greater than 1 sec. These results suggest that familiarization is one, but not the only, factor underlying the strength-mirror effect.

A Lightning Prediction Index that Utilizes GPS Integrated Precipitable Water Vapor*

The primary weather forecast challenge at the Cape Canaveral Air Station and Kennedy Space Center is lightning. This paper describes a statistical approach that combines integrated precipitable water vapor (IPWV) data from a global positioning system (GPS) receiver site located at the Kennedy Space Center (KSC) with other meteorological data to develop a new GPS lightning index. The goal of this effort is to increase the forecasting skill and lead time for prediction of a first strike at the KSC. Statistical regression methods are used to identify predictors that contribute skill in forecasting a lightning event. Four predictors were identified out of a field of 23 predictors that were tested, determined using data from the 1999 summer thunderstorm season. They are maximum electric field mill values, GPS IPWV, 9-h change in IPWV, andK index. The GPS lightning index is a binary logistic regression model made up of coefficients multiplying the four predictors. When time series of the GPS lightning index are plotted, a common pattern emerges several hours prior to a lightning event. Whenever the GPS lightning index falls to 0.7 or below, lightning occurs within the next 12.5 h. An index threshold value of 0.7 was determined from the data for lightning prediction. Forecasting time constraints based on KSC weather notification requirements were incorporated into the verification. Forecast verification results obtained by using a contingency table revealed a 26.2% decrease from the KSC’s previous-season false alarm rates for a nonindependent period and a 13.2% decrease in false alarm rates for an independent test season using the GPS lightning index. In addition, the index improved the KSC desired lead time by nearly 10%. Although the lightning strike window of 12 h is long, the GPS lightning index provides useful guidance to the forecaster in preparing lighting forecasts, when combined with other resources such as radar and satellite data. Future testing of the GPS lightning index and the prospect of using the logistic regression approach in forecasting related weather hazards are discussed.

A novel signal processing technique for clutter reduction in GPR measurements of small, shallow land mines

A signal processing technique is developed to reduce clutter due to ground bounce in ground penetrating radar (GPR) measurements. This technique is especially useful when a GPR is used to detect subsurface antipersonnel mines. The GPR clutter is modeled using a simple parametric model. Buried mine and clutter contributions are separated through a pair of coupled iterative procedures. The algorithm outperforms existing clutter reduction approaches and also yields target features that are useful for detection and identification of these mines. The proposed technique effectively reduces clutter resulting in a significant decrease in false alarm rates.