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Article
Publication date: 18 July 2020

Arash Shahin, Ashraf Labib, Ali Haj Shirmohammadi and Hadi Balouei Jamkhaneh

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

Abstract

Purpose

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

Design/methodology/approach

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

Research limitations/implications

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

Originality/value

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 December 2002

Rebecca Löbmann

The detection of drunk driving is an important task of police organizations. The impact of police work on drunk driving depends largely on drivers’ perceptions of the probability…

1610

Abstract

The detection of drunk driving is an important task of police organizations. The impact of police work on drunk driving depends largely on drivers’ perceptions of the probability of detection. The present study explored the effects of different enforcement strategies on this perception. Participants (n=77) experienced different control strategies in a game and subsequently rated probability of detection. Degree of surveillance and efficiency of controls were varied. In the case of low detection probabilities, a substantial overestimation was found. Moreover, participants rated probability of detection higher when the same rate of detection was accomplished with few but efficient, compared to more but inefficient, controls. Assuming that a similar perception process is at work for drunk driving, the results suggest that increasing efficiency will have a greater impact on deterring drunk driving than increasing the frequency of controls. Consequences for police work are discussed.

Details

Policing: An International Journal of Police Strategies & Management, vol. 25 no. 4
Type: Research Article
ISSN: 1363-951X

Keywords

Article
Publication date: 30 July 2020

Ryan Gerald McLaughlin and Mario G. Perhinschi

An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition…

Abstract

Purpose

An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.

Design/methodology/approach

An AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.

Findings

The proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.

Research limitations/implications

The promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.

Practical implications

The use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.

Originality/value

In this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

Details

International Journal of Intelligent Unmanned Systems, vol. 9 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

Book part
Publication date: 29 May 2023

Divya Nair and Neeta Mhavan

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and…

Abstract

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

Details

Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy
Type: Book
ISBN: 978-1-80382-555-7

Keywords

Article
Publication date: 11 October 2021

Levi Anderson, Steven Love, James Freeman and Jeremy Davey

This study first aimed to investigate the differences in drug driver detection rates between a trial of randomised and targeted enforcement operations. The second aim was to…

Abstract

Purpose

This study first aimed to investigate the differences in drug driver detection rates between a trial of randomised and targeted enforcement operations. The second aim was to identify which indicator categories are most commonly used by police to target drug drivers and to assess the effectiveness of targeted drug testing. Finally, this study aimed to quantify what specific indicators and cues (of the overarching categories) triggered their decision to drug test drivers and which indicators were most successful.

Design/methodology/approach

This research examined the detection rates in a trial comparison of randomised and targeted roadside drug testing (RDT) operations as well as the methods utilised by police in the targeted operations to identify potential drug driving offenders.

Findings

Visual appearance was by far the most commonly utilised indicator followed by age, police intelligence on prior charges, vehicle appearance and behavioural cues. However, the use of police intelligence was identified as the most successful indicator that correlated with positive oral fluid testing results. During the randomised RDT operations, 3.4% of all drivers who were tested yielded a positive roadside oral fluid result compared to 25.5% during targeted RDT operations.

Research limitations/implications

The targeted RDT approach, while determined to be an effective detection methodology, limits the overall deterrent effect of roadside testing in a more general driving population, and the need for a balanced approach to ensure detection and deterrence is required. This study highlights that by focussing on night times for randomised RDT operations and the identified effective indicators for targeted operations, an effective balance of deterrence and detection could be achieved.

Practical implications

While the presence of a single indicator is not indicative of a drug driver, this study highlights for police which indicators currently used are more effective at detecting a drug driver. As a result, police could adapt current RDT procedures to focus on the presence of these indicators to support drug driver detection.

Originality/value

This is a world-first study that examines both randomised and targeted roadside drug testing. This study controls for location and time of day while using the same police unit for roadside testing, thus is able to make direct comparisons between the two methodologies to determine the effectiveness of police targeting for roadside drug testing. Furthermore, this study highlights which indicators used by police results in the highest rate of positive roadside drug tests.

Details

Policing: An International Journal, vol. 44 no. 6
Type: Research Article
ISSN: 1363-951X

Keywords

Article
Publication date: 25 November 2019

Avinash Kumar Shrivastava and Nitin Sachdeva

Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a…

Abstract

Purpose

Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues.

Design/methodology/approach

The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy.

Findings

From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance.

Originality/value

The authors are proposing a generalized model for software scheduling.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 6/7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 20 June 2019

Tie Zhang and JingDong Hong

Successful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial…

Abstract

Purpose

Successful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate.

Design/methodology/approach

The error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved.

Findings

The proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions.

Originality/value

A collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 4
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 1 December 2004

Sanjay Rawat, V.P. Gulati and Arun K. Pujari

This paper discusses a new similarity measure for the anomaly‐based intrusion detection scheme using sequences of system calls. With the increasing frequency of new attacks, it is…

Abstract

This paper discusses a new similarity measure for the anomaly‐based intrusion detection scheme using sequences of system calls. With the increasing frequency of new attacks, it is getting difficult to update the signatures database for misuse‐based intrusion detection system (IDS). While anomaly‐based IDS has a very important role to play, the high rate of false positives remains a cause for concern. Defines a similarity measure that considers the number of similar system calls, frequencies of system calls and ordering‐of‐system calls made by the processes to calculate the similarity between the processes. Proposes the use of Kendall Tau distance to calculate the similarity in terms of ordering of system calls in the process. The k nearest neighbor (kNN) classifier is used to categorize a process as either normal or abnormal. The experimental results, performed on 1998 DARPA data, are very promising and show that the proposed scheme results in a high detection rate and low rate of false positives.

Details

Information Management & Computer Security, vol. 12 no. 5
Type: Research Article
ISSN: 0968-5227

Keywords

Article
Publication date: 5 October 2020

Josemila Baby Jesuretnam and Jeba James Rose

This paper aims to propose a multi-dimensional hierarchical K-means clustering algorithm for the purpose of intrusion detection. Initially, the clustering set of rules is proposed…

Abstract

Purpose

This paper aims to propose a multi-dimensional hierarchical K-means clustering algorithm for the purpose of intrusion detection. Initially, the clustering set of rules is proposed to shape some of clusters in the network and then the most beneficial clusters are decided on by the use of Cuckoo search optimization set of rules. Finally, an Artificial Bee Colony primarily based selection tree (ABC-DT) classifier is rented to classify the regular and unusual instances present in the network with the aid of the extracted features.

Design/methodology/approach

Intrusion detection system (IDS) is crucial for the network system; the intruder can take sensitive details about the network. IDS are said to be more effective when it has both high intrusion detection rate and low false alarm rate. Numerous strategies including gadget mastering, records mining and statistical techniques were tested for IDS mission. Recent study reveals that combining multiple classifiers, i.e. classifiers ensemble, can also own better performance than unmarried classifier. In this paper, a comparative study is conducted of the overall performance of four classifiers, i.e. hybrid ABC-DT particle swarm optimization-based K-means clustering (PSO-KM), help vector device (SVM) and K-Nearest neighbour (KNN). All the four classifiers are tested with exceptional packet sizes 1470, 1024, 512 and 256. The experiment is carried out for the speed ranging from turned into done for the velocity ranging from 250Mbps, 500Mbps, 750Mbps, 1.0Gpbs, 1.5Gbps, and 2.0Gbps in terms of accuracy, detection charge, specificity, false alarm charge and computational time. The experimental results reveals that the hybridization of classifiers performs better than the base classifiers in all scenarios.

Findings

This study analyses the performance of hybrid ABC-DT classifier and compares the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The performances of all the four classifiers are tested with Discovery in Data Mining (KDD) CUP 99 dataset with different packet sizes 1470, 1024, 512 and 256. The results show the classifier performance variations with different speed ranges. From the experimental results and analysis, the hybridization of classifiers such as ABC-DT outperforms the base classifiers in all scenarios.

Originality/value

The novel approach in this paper is used to study the hybrid ABC-DT classifier and compare the performance against three well-known classifiers such as PSO-KM, SVM and K-NN. The discussed concept is used within the network to monitor the traffic to and from all the devices connected in that network.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 8 September 2023

Tolga Özer and Ömer Türkmen

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use…

Abstract

Purpose

This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.

Design/methodology/approach

This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.

Findings

The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.

Originality/value

The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.

Details

Robotic Intelligence and Automation, vol. 43 no. 6
Type: Research Article
ISSN: 2754-6969

Keywords

1 – 10 of over 20000