The research presents a model for detecting potential security attacks in the Industry 5.0’s Internet of Things (IIoT) model using an anomaly detection algorithm, with Blockchain technology to further enhance securit...
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In recent years, we have witnessed an unpresented increase localized heavy weather phenomena such as tornadoes and localized heavy rain which can not be expected by the conventional weather forecast system. However, t...
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ISBN:
(纸本)9781479968336
In recent years, we have witnessed an unpresented increase localized heavy weather phenomena such as tornadoes and localized heavy rain which can not be expected by the conventional weather forecast system. However, the number of observation posts is few little for forecasting for tornadoes and heavy rain. It is necessary to increase dramatically the observation points in order to perform ware correct prediction using real data. We have developed a compact and low-cost pressure information acquisition system, to detect the signs of localized abnormal weather. This research proposes an algorithm to predict local weather by detecting anomalous pressure values in the time series of the pressure sensor information, and then to notify users.
The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the com...
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The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomalydetection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV -based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy.
Smart mobility is a key component of smart cities, and the switch from traditional automotive systems to connected and automated vehicles (CAVs) is recognized as one of the evolving technologies on urban roads. Althou...
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ISBN:
(纸本)9798350397420
Smart mobility is a key component of smart cities, and the switch from traditional automotive systems to connected and automated vehicles (CAVs) is recognized as one of the evolving technologies on urban roads. Although the current autonomous vehicle (AV) mobility environment may be geared toward infrastructure and road users, it cannot facilitate the adoption of CAV in the future due to the presence of different modules that are nested in the cyberspace. Furthermore, the ability to make accurate decisions in real-time is essential for the success of autonomous systems. However, cyberattacks on these entities might skew the decision-making processes, which can result in complex CAV mishaps. Furthermore, the method utilized by the police to conduct accident investigations cannot be used to identify road accidents brought on by cyberattacks. Therefore, this paper proposes a 5Ws & 1H-based investigation approach to deal with cyberattack-related accidents. Also, a stochastic anomalydetection system is proposed to identify the abnormal activities of the automated driving system (ADS) functions during a crash analysis. Further, two case studies are shown to validate the results of the proposed algorithms.
State of charge (SOC) is the most direct embodiment of the state of a lead-acid battery, and accurate estimation of SOC is helpful to ensure the safe use of the battery. However, the traditional estimation model has l...
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State of charge (SOC) is the most direct embodiment of the state of a lead-acid battery, and accurate estimation of SOC is helpful to ensure the safe use of the battery. However, the traditional estimation model has low precision and weak anti-interference. In this study, a new SOC estimation structure is proposed. This structure is based on the effective combination of the Isolation Forest (IF) anomaly detection algorithm and Long Short-Term Memory (LSTM) Network combined with Attention Mechanism (IF-LSTM-Attention). The Isolation Forest algorithm is used to effectively detect the missing values and outliers contained in the original data. Based on the actual charging and discharging data, a sliding window is constructed as the data of the model to give full play to the LSTM network length dependence. And LSTM network combined with Attention Mechanism achieves high-precision SOC estimation. In addition, the conventional dropout technique and Bayesian optimizer are used to improve the model training convergence rate. The results show that the IF-LSTM-Attention model proposed in this study has higher accuracy and better generalization ability than the conventional LSTM model and Back Propagation (BP) neural network model.
This paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traf...
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Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradig...
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Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomalydetection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomalydetection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomalydetection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with
It is known that overlapping tissues cause highly complex projections in chest radiographs. In addition, artificial objects, such as catheters, chest tubes and pacemakers can appear on these radiographs. It is importa...
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It is known that overlapping tissues cause highly complex projections in chest radiographs. In addition, artificial objects, such as catheters, chest tubes and pacemakers can appear on these radiographs. It is important that the anomaly detection algorithms are not confused by these objects. To achieve this goal, the authors propose an approach to train a convolutional neural network (CNN) to detect chest tubes present on radiographs. To detect the chest tube skeleton as the final output in a better manner, non-uniform rational B-spline curves are used to automatically fit with the CNN output. This is the first study conducted to automatically detect artificial objects in the lung region of chest radiographs. Other automatic detection schemes work on the mediastinum. The authors evaluated the performance of the model using a pixel-based receiver operating characteristic (ROC) analysis. Each true positive, true negative, false positive and false negative pixel is counted and used for calculating average accuracy, sensitivity and specificity percentages. The results were 99.99% accuracy, 59% sensitivity and 99.99% specificity. Therefore they obtained promising results on the detection of artificial objects.
Traditional Intrusion detection Systems (IDS) can be complemented by an anomaly detection algorithm (ADA) to also identify unknown attacks. We argue that, as each ADA has its own strengths and weaknesses, it might be ...
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ISBN:
(纸本)9781479923588
Traditional Intrusion detection Systems (IDS) can be complemented by an anomaly detection algorithm (ADA) to also identify unknown attacks. We argue that, as each ADA has its own strengths and weaknesses, it might be beneficial to rely on multiple ADAs to obtain deeper insights. ADAs are very resource intensive;thus, real-time detection with multiple algorithms is even more challenging in high-speed networks. To handle such high data rates, we developed a controlled load allocation scheme that adaptively allocates multiple ADAs on a multi-core system. The key idea of this concept is to utilize as many algorithms as possible without causing random packet drops, which is the typical system behavior in overload situations. We developed a proof of concept anomalydetection framework with a sample set of ADAs. Our experiments confirm that the detection performance can substantially benefit from using multiple algorithms and that the developed framework is also able to cope with high packet rates.
With the development of DLNA technology, more and more DLNA devices appear in our lives. We can extract users' activity events from the operation history of DLNA devices, and provide various services based on them...
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