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This paper aims to present a data-processing methodology combining kernel change detection (KCD) and efficient global optimization algorithms for solving inverse problem in eddy current non-destructive testing. The main purpose is to reduce the computation cost of eddy current data inversion, which is essentially because of the heavy forward modelling with finite element method and the non-linearity of the parameter estimation problem.

The KCD algorithm is adapted and applied to detect damaged parts in an inspected conductive tube using probe impedance signal. The localization step allows in reducing the number of measurement data that will be processed for estimating the flaw characteristics using a global optimization algorithm (efficient global optimization). Actually, the minimized objective function is calculated from data related to defect detection indexes provided by KCD.

Simulation results show the efficiency of the proposed methodology in terms of defect detection and localization; a significant reduction of computing time is obtained in the step of defect characterization.

This study is the first of its kind that combines a change detection method (KCD) with a global optimization algorithm (efficient global optimization) for defect detection and characterization. To show that such approach allows to reduce the numerical cost of ECT data inversion.

This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample.

To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed.

The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices.

With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

Fused deposit modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts. This paper presents a solution to the problem of detecting defects on the outer surface in the additive process of FDM.

A multi-view and all-round vision detection method is introduced where the detection field of view is changing with the vector of the outer surface during the printing process on the six degrees of freedom robot FDM printer.

After the image is preprocessed, this paper can identify the defects effectively according to its laminate structure, and introduces a mathematical matrix to represent the defects which will be classified into three typical types according to the geometry shape and area distribution.

This research only focuses on the feasibility of the defects detection method. To create the object of high quality, more research is needed to account for the process parameters which significantly cause the defects.

This work will help to detect the defects online, monitor the printing quality of the outer surface, reduce the waste of printed filaments, etc.

This study is among the first to present a multi-view and all-round vision detection method to detect defects on the outer surface in the additive process of FDM; proposes a means of identifying defects according to its laminate structure; and introduces a mathematical matrix to represent the defects which may be used in quality assessment.

The increasing frequency and intensity of the extreme weather events could cause devastating consequences in tourism. Climate change–related extreme weather events and their relation to tourism is an emerging field for education and research. The purpose of this study is to categorize the impact of climate change on tourist destinations with regard to extreme weather-related risks in outdoor recreation and tourism. Managerial implications for policymakers and stakeholders are discussed.

To outline the risks from climate change associated with tourism, this study uses the Prisma analysis for identification, screening, checking for eligibility and finding relevant literature for further categorization.

Based on a thoroughly examination of relevant literature, risks and threats posed by climate change could be categorized into following four areas: reduced experiential value in outdoor winter recreation; reduced value in beach scenery and comfort; land degradation and reduced biodiversity; and reduced value in personal safety and comfort in tourism. It also focuses on the significance of using big data applications in catastrophic disaster management and risk reduction. Recommendations with technology and data analytics to continuously improve the disaster management process in tourism education are provided based on findings of this study.

Primary contributions of this study include the following: providing a summarized overview of the risks associated with climate change in terms of tourist experiential value for educational implications; and revealing the role of data analytics in disaster management in the context of tourism and climate change for tourism education.

This paper aims to describe a recently proposed algorithm in terrain‐based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature. It also aims to use an extended Kalman filter (EKF) to estimate the fused position of the UAVs and to apply the 2‐D splinegon technique to build the map of the complex shaped obstacles. The path of the UAVs are dictated by the Dubins path planning algorithm. The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.

An extended Kalman filter is used to estimate the position of the UAVs, and the 2‐D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.

The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2‐D splinegon technique. This is a newly proposed algorithm, the most efficient and a robust way in terrain based mapping of the complex obstacles. The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.

The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2‐D splinegon technique. This is a new approach where the fused EKF estimated positions are used with the limited number of sensors' measurements in building the map of the complex obstacles.

The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities. There have been many intrusion detection schemes proposed, most of which apply both normal and intrusion data to construct classifiers. However, normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect. Internet intrusion detection can be considered as a novelty detection problem, which is the identification of new or unknown data, to which a learning system has not been exposed during training. This paper aims to address this issue.

In this paper, a novelty detection‐based intrusion detection system is proposed by combining the self‐organizing map (SOM) and the kernel auto‐associator (KAA) model proposed earlier by the first author. The KAA model is a generalization of auto‐associative networks by training to recall the inputs through kernel subspace. For anomaly detection, the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns. The hybrid SOM/KAA model can also be applied to classify different types of attacks.

Using the KDD CUP, 1999 dataset, the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state‐of‐art novelty detection methods, showing marked improvements in terms of the high intrusion detection accuracy and low false positives. Simulations on the classification of attack categories also demonstrate favorable results of the accuracy, which are comparable to the entries from the KDD CUP, 1999 data mining competition.

The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.

Many applications in intelligent transportation demand accurate categorization of vehicles. The purpose of this paper is to propose a working image-based vehicle classification system. The first component vehicle detection is implemented by applying Dalal and Triggs's histograms of oriented gradients features and linear support vector machine (SVM) classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by an improved stacked generalization. As an effective ensemble learning strategy, stacked generalization has been proposed to combine multiple models using the concept of a meta-learner. However, it was found that the well-known meta-learning scheme multi-response linear regression (MLR) for stacked generalization performs poorly on the vehicle classification.

A new meta-learner is then proposed based on kernel principal component regression (KPCR). The stacked generalization scheme consists of a heterogeneous classifier ensemble with seven base classifiers, i.e. linear discriminant classifier, fuzzy k-nearest neighbor, logistic regression, Parzen classifier, Gaussian mixture model, multiple layer perceptron and SVM.

Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the proposed approach. The improved stacked generalization produced consistently better results when compared to any of the single base classifier used and four other beta learning algorithms, including MLR, majority voting, logistic regression and decision template.

With the seven base classifiers, the KPCR-based stacking offers a performance of 96 percent accuracy and 95 percent κ coefficient, thus exhibiting promising potentials for real-world applications.

This paper primarily aims to focus on a review of convolutional neural network (CNN)-based eye control systems. The performance of CNNs in big data has led to the development of eye control systems. Therefore, a review of eye control systems based on CNNs is helpful for future research.

In this paper, first, it covers the fundamentals of the eye control system as well as the fundamentals of CNNs. Second, the standard CNN model and the target detection model are summarized. The eye control system’s CNN gaze estimation approach and model are next described and summarized. Finally, the progress of the gaze estimation of the eye control system is discussed and anticipated.

The eye control system accomplishes the control effect using gaze estimation technology, which focuses on the features and information of the eyeball, eye movement and gaze, among other things. The traditional eye control system adopts pupil monitoring, pupil positioning, Hough algorithm and other methods. This study will focus on a CNN-based eye control system. First of all, the authors present the CNN model, which is effective in image identification, target detection and tracking. Furthermore, the CNN-based eye control system is separated into three categories: semantic information, monocular/binocular and full-face. Finally, three challenges linked to the development of an eye control system based on a CNN are discussed, along with possible solutions.

This research can provide theoretical and engineering basis for the eye control system platform. In addition, it also summarizes the ideas of predecessors to support the development of future research.

In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.

Specifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.

The research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.

The YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.

The proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.

Automatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.

The proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.