By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC se...
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By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services' large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services' current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (lstm) autoencoder for detecting MEC services' runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named lstm autoencoder to characterize the MEC services' historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services' historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance lstm autoencoder with a weight-based reservoir sampling technique and an lstm boosting algorithm. The reconstruction loss of the trained lstm autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services' runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.
A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in pow...
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A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in power flow forecasting owing to the inherent variability of renewable power generation and diverse power load behavior. Accurate forecasting is vital for monitoring the imported power during peak regional load periods and surplus power generation exported from the studied region. This study addressed the challenge of multistep bidirectional power flow forecasting by proposing an lstm autoencoder model. During the training stage, the proposed model and baseline models were developed using autotune hyperparameters to fine-tune the models and maximize their performance. The model utilized the last 6 h leading up to the current time (24 steps of 15 min intervals) to predict the power flow 1 h ahead (4 steps of 15 min intervals) from the current time. In the model evaluation stage, the proposed model achieved the lowest RMSE and MAE scores with values of 32.243 MW and 24.154 MW, respectively. In addition, it achieved a good R-2 score of 0.93. The evaluation metrics demonstrated that the lstm autoencoder outperformed the other models for multistep forecasting task in a regional grid cluster proposal.
Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies beca...
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Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long shortterm memory (lstm) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity. (c) 2022 Elsevier B.V. All rights reserved.
In the manufacturing industry, anomalies are an unfortunate but inevitable reality. If left unaddressed, they can lead to costly production defects and halted production lines. However, with the rise of Industry 4.0, ...
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ISBN:
(纸本)9783031381645;9783031381652
In the manufacturing industry, anomalies are an unfortunate but inevitable reality. If left unaddressed, they can lead to costly production defects and halted production lines. However, with the rise of Industry 4.0, many industrial machines are now equipped with sensors that can be used to detect anomalous behaviors, allowing for early identification and prevention of defects. Therefore, this study presents a solution using a Long Short-Term Memory (lstm) autoencoder to detect abnormal behavior in an industrial machine temperature sensor dataset. The algorithm is compared with conventional methods, further demonstrating its capabilities in anomaly detection. Additionally, an implementation architecture is proposed using InfluxDB and Telegraf software, providing a simulated real-world application of the proposed solution.
Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns ...
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Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns on multi-time scales utilizing electricity consumption data (ECD). Therefore, we propose a novel framework to characterize residential load patterns on multi-time scales. The long-short-term memory autoencoder (lstm-AE) model is designed for dimensionality reduction and feature extraction. Furthermore, a two-level clustering method is proposed to discover and characterize typical load patterns (TLPs) and multifaceted load patterns (MLPs) on multi-time scales. The proposed framework is comprehensively evaluated via extensive experiments on three real ECD. Results show that: (1) Reconstruction errors of lstm-AE are lower than 6 benchmark models across different time scales, which validates the superiority of lstm-AE. (2) TLPs and MLPs on daily, weekly, monthly and yearly scale are discovered by the two-level clustering method. TLPs profile the resident's electricity usages from a global view. (3) MLPs present the consumer segmentation and characterize residential load patterns of individual and groups. Especially, customer groups and electricity usage habits or lifestyles are revealed thoroughly to customize personal demand response strategies. This study can provide new valuable insights for smart grid applications.
Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be "air-gapped", are becoming mor...
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ISBN:
(纸本)9781914587962;9781914587979
Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be "air-gapped", are becoming more susceptible to cyber-attack. Most real-time systems that communicate using the Military Standard 1553B Multiplex data bus (MIL-STD-1553B) protocol do not have the ability to detect cyber-attacks. These systems were originally developed with safety and redundancy in mind, not security. These two factors introduce attack vectors to MIL-STD-1553B communication buses and expose associated avionics systems to exploitation. Recent approaches to anomaly detection for the MIL-STD-1553B data bus have leveraged statistical analysis, Markov Chain modelling, remote terminal fingerprinting and signature-based detection. However, their comparative effectiveness is unknown. Regarding the statistical analysis technique, the lack of accuracy and precision in detecting the start and stop time of anomalous events are not ideal for conducting investigations due to the sheer volume of messages still required to be manually analysed. Deep learning techniques offer an effective means of anomaly detection and applying these techniques to the MIL-STD-1553B data bus could provide more accurate and precise detection times when anomalies or attacks are present, when compared to known statistical analysis, leading to more efficient forensic investigations of anomalous events. The aim of this research is to improve the time-related performance metrics when detecting attacks on the MIL-STD-1553B data bus traffic using a Long Short-Term Memory (lstm) autoencoder. In order to accomplish this aim, an lstm autoencoder detector was developed and tested on two separate datasets from different MIL-STD-1553B network architectures, totalling 15 threat instances over 5 scenarios. The detector was then compared to the MIL-STD-1553B Anomaly-Based Intrusion Detection System (MAIDENS) detector, a statisti
Introduction: Unscheduled machine downtime can cause treatment interruptions and adversely impact patient treatment outcomes. Conventional Quality Assurance (QA) programs of a proton Pencil Beam Scanning (PBS) system ...
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Introduction: Unscheduled machine downtime can cause treatment interruptions and adversely impact patient treatment outcomes. Conventional Quality Assurance (QA) programs of a proton Pencil Beam Scanning (PBS) system ensure its operational performance by keeping the beam parameters within clinical tolerances but often do not reveal the underlying issues of the device prior to a machine malfunction event. In this study, we propose a Predictive Maintenance (PdM) approach that leverages an advanced analytical tool built on a deep neural network to detect treatment delivery machine issues ***: Beam delivery log file data from daily QA performed at the Burr Proton Center of Massachusetts General Hospital were collected. A novel PdM framework consisting of long short-term memory-based autoencoder (lstm-AE) modeling of the proton PBS delivery system and a Mahalanobis distance-based error metric evalu-ation was constructed to detect rare anomalous machine events. These included QA beam pauses, clinical operational issues, and treatment interruptions. The model was trained in an unsupervised fashion on the QA data of normal sessions so that the model learned characteristics of normal machine operation. The anomaly is quantified as the multivariate deviation between the model predicted data and the measured data of the day using Mahalanobis distance (M-Score). Two-layer and three-layer Long short-term memory-based stacked autoencoder (lstm-SAE) models were optimized for exploring model performance improvement. Model vali-dation was performed with two clinical datasets and was analyzed using the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic (AUROC).Results: lstm-SAE models showed strong performance in predicting QA beam pauses for both clinical validation datasets. Despite severe skew in the dataset, the model achieved AUPRC of 0.60 and 0.82 and AUROC of 0.75 and 0.92 in the respective 2018 and 2020 datasets. M
Gas metal arc welding (GMAW) is commonly used for joining metals. Despite the widespread adoption of robotic GMAW, welding errors still occur frequently [1]. They can be costly and time consuming to discover and fix a...
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ISBN:
(纸本)9798350315684
Gas metal arc welding (GMAW) is commonly used for joining metals. Despite the widespread adoption of robotic GMAW, welding errors still occur frequently [1]. They can be costly and time consuming to discover and fix after welding has been completed. This paper presents a method for detecting welding anomalies using unsupervised machine learning on sound data. Earlier attempts have yielded unsatisfactory results, we propose using a long short-term memory (lstm) autoencoder model to detect welding anomalies in sound from the flux cored arc welding (FCAW) method. The main findings are that the model is well suited, and that it outperforms previous methods.
Primary activity statistics contribute (PAS) significantly in increasing the efficiency of the dynamic spectrum access/cognitive radio system. PAS can be estimated from the spectrum sensing observations. To achieve a ...
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ISBN:
(纸本)9781728191270
Primary activity statistics contribute (PAS) significantly in increasing the efficiency of the dynamic spectrum access/cognitive radio system. PAS can be estimated from the spectrum sensing observations. To achieve a precise estimation of PAS, accurate spectrum sensing is required. However, it is difficult to maintain perfect spectrum sensing in a realistic scenario because of various hardware and sensing errors (false alarms and miss detections). In this work, Long-Short Term Memory autoencoder based deep learning framework is proposed to detect the sensing errors in imperfect spectrum sensing scenarios. Moreover, to correct the sensing errors, we propose a simple single iteration reconstruction algorithm and further estimate the PAS. The error in the estimated PAS is quantified through the Kolmogorov Smirnov distance. Finding suggests that relative error of estimated mean decreases from 80% to 9%. The proposed framework doesn't require any prior knowledge of PU activity statistics to achieve this performance making it feasible in realistic scenarios.
This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (L...
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This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (lstm) autoencoder model trained using response datasets under intact conditions was used to reconstruct the measured acceleration data of the target structure. Further, the data reconstruction error was used as the input for the deep neural network algorithm trained in advance using the reconstruction error pattern in various tether damage cases. The proposed method was verified by conducting a well-validated simulation based on hydrodynamics. The damage-detection accuracy of the proposed method was directly compared with that of a conventional supervised learning algorithm-based approach. In addition, the case study results confirmed that the proposed approach was applicable to other submerged floating tunnel (SFT) structures by retraining the lstm autoencoder and deep neural network algorithms with intact datasets only. Thus, this approach does not require a large amount of training data or simulation model updates for other SFT structures.
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