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This paper compares three unsupervised machine-learning algorithms – local outlier factor (LOF), Isolation Forest (iForest) and one-class support vector machine (OCSVM) – for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains several Au, Cu and Pb–Zn mineral occurrences. The methodology incorporates single-element geochemistry, multivariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co–Cr–Ni–V–Sn association indicating a lithological influence; PC2 shows a Au–Bi–Cu–W association suggesting epithermal Au mineralization; PC3 shows variability in Zn–V–Co–Sb–Cu–Cr; PC4 shows a Au–Cu–Ba–Sr–Ag association indicating Au and polymetallic mineralization; PC5 reflects Zn–Ag–Ni–Pb related to hydrothermal mineralization; and PC6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better than LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF outperformed iForest and OCSVM in identifying all four Pb–Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associated with Au–Pb mineralization, while iForest detected anomalies associated with Pb–Zn–Cu occurrences and neighbouring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE and west by detecting anomalies associated with two Cu occurrences and a Pb–Cu occurrence. This study examined the influence of contamination fraction on detection of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases from 5 to 10%. However, even with a 35% contamination fraction, some Cu occurrences remained outside the anomaly category, indicating potentially overlooked geochemical signals from mineral occurrences due to sampling schemes. [ABSTRACT FROM AUTHOR]

Usually, resource-constrained sensors operate unattended in an infrastructure-less environment. It attracts various active and passive attacks where wormhole attack gains prime attention due to its effortless implementation in comparison to its devastating effect on localization schemes. Further, an implicit wormhole with a single tunnel makes it difficult to uncover it. A wormhole disturbs hop counts erratically among the sensor pairs which in turn destroys most of the localization algorithms like DV-Hop and its various successors. Therefore, an algorithm is required to detect anomalous sensors as outliers and remove them from participating as reference points in localization. Thus, in this paper, a secure optimized localization in large-scale WSN (SOLLW) is proposed where outliers are detected and removed by using a one-class support vector machine. Further, SOLLW recovers location through linear optimization by introducing error factors in distance values between every sensor pair. The simulation validates SOLLW in comparison to other algorithms. [ABSTRACT FROM AUTHOR]

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. [ABSTRACT FROM AUTHOR]

The real disaster information is an important reference for preventing and reducing heavy rainfall disaster losses effectively. Taking process rainfall intensity as the index, this paper constructs a heavy rainfall disaster event database matching the multi-source meteorological disasters and disaster-causing processes in Hebei Province during 1984-2020. After manual quality control, 2305 groups of real disaster information and 263 groups of false information are obtained. In this study, correlation analysis was used to determine and select the rainfall eigenfactors that are significantly related to the disaster degree. Based on the One-class support vector machine (OCSVM) and 10 folds cross-validation method, 10 samples were randomly selected to establish the meteorological factor disaster discriminant model and test and optimize, so as to explore the intelligent and easy-to-use intelligent discriminant method of multi-source heavy rainfall disaster credibility. The results showed that: (1) there are eleven rainfall eigenfactors related to disaster degree at 0.01 significant level, which are maximum rainfall, minimum rainfall, average process rainfall, average daily rainfall, average hourly rainfall intensity, hourly maximum rainfall, 3-hour maximum rainfall, 6-hour maximum rainfall, 12-hour maximum rainfall, 24-hour maximum rainfall and rainfall in the first 10 days. (2) Ten models (M1-M10) were established using 11 rainfall eigenfactors. According to the identification accuracy of the real disaster, the optimal model was determined to be M9, with the authenticity rate of 96.4% and the falsification rate of 67.6%, which indicated that the model is one-sided in determining the authenticity of the disaster situation and should be further optimized. (3) Through the autocorrelation test, maximum rainfall, average hourly rainfall intensity, hourly maximum rainfall and rainfall in the first 10 days were taken as input factors. Ten models (M11-M20) were reconstructed, and the optimal model is M20 with 96.2% proof rate and 82.9% false rate. Based on comprehensive analysis, the model established by 4 factors is more reliable than the model established by 11 factors. [ABSTRACT FROM AUTHOR]

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.

Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method. [ABSTRACT FROM AUTHOR]

• Pavement bleeding detection based on image processing is proposed. • In real-world data collection, having access to rare datasets such as pavement bleeding images is challenging. • Due to the rare occurrence of pavement bleeding, the imbalanced-class distribution problem appears. • The Gray-Level Co-Occurrence Matrix is employed in the feature extraction stage. • To overcome the imbalanced classification problem, an anomaly detection approach is used. Bleeding is a type of pavement distress that can reduce the road's skid resistance and safety, as a result, it should be detected during routine inspections. This study proposes an automatic detection of bleeding in asphalt pavement using an anomaly detection-based method. Due to the scarcity nature of bleeding in asphalt pavement, bleeding and no-bleeding images in Pavement Management System datasets tend to become imbalanced causing the model to be relatively biased toward the no-bleeding class. Therefore, One-Class Support Vector Machine (OCSVM), which is an anomaly detection approach, can be beneficial for classifying images of bleeding asphalt. Computer vision techniques are used to extract efficient features, which serve as inputs for the OCSVM model. In order to evaluate the suggested model's performance, two separate imbalanced datasets of images are utilized to train and test the model. Finally, in terms of F1-Score, the OCSVM achieved 97.29 % in the testing dataset. [ABSTRACT FROM AUTHOR]

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In this paper, we propose H-IDFS, a Histogram-based Intrusion Detection and Filtering framework, which assembles the CAN packets into windows, and computes their corresponding histograms. The latter are fed to a multi-class IDS classifier to identify the class of the traffic windows. If the window is found malicious, the filtering system is invoked to filter out the normal CAN packets from each malicious window. To this end, we propose a novel one-class SVM, named OCSVM-attack that is trained on normal traffic and considers the invariant and quasi-invariant features of the attack. Experimental results on two CAN datasets: OTIDS and Car-Hacking, show the superiority of the proposed H-IDFS, as it achieves an accuracy of 100% for window classification, and correctly filters out between 94.93% and 100% of normal packets from malicious windows. [ABSTRACT FROM AUTHOR]

In the analysis of ECG data, it is necessary to first automatically classify the quality of ECG data to facilitate the subsequent processing. PhysioNet/Computing in Cardiology Challenge 2011 and existing researches involve the quality of the entire record of ECG, which fails to maximize utilization of ECG data. In fact, single-lead ECG has been widely used in many fields, such as wearable devices, sleep apnea monitoring, and deriving respiratory rate. However, the present methods for evaluating the data quality by each channel only divide them into two categories: acceptable and unacceptable, which is relatively coarse. This paper proposes a new method for the automatic classification of ECG data quality by channel. This method divides them into four categories: (1) electrode shedding, marked as C3; (2) serious noise interference, under which it is difficult to detect R wave, marked as C2; (3) partial noise interference, under which part of R waves may not be detected correctly, marked as C1; (4) high quality signal, marked as C0. The 2011 competition data was re-marked according to the channel with the help of our designed auxiliary program. This paper defined some features and designed a tree classifier using One-Class Support Vector Machine(OCSVM). The test results of our method show that the detection accuracy of electrode shedding is 93.22%, serious noise interference is 90%, partial noise interference is 89.22%, and high quality signal is 97.19%. It shows that the method has a broad prospect in the automatic preprocessing of ECG data.

With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion detection system (IDS) is expected to adapt and secure the computing infrastructures from the ever-changing sophisticated threat landscape. Many deep learning approaches have recently been proposed; however, these techniques face significant challenges in identifying all types of attacks, especially rare attacks due to network traffic imbalances and the lack of a sufficient number of abnormal traffic samples for model training. To overcome these shortcomings and improve detection performance, this paper presents an unsupervised deep learning approach for intrusion detection. Unlike the existing IDS model that extracts features and trains a classifier in two separate stages, a single-stage IDS approach that integrates a one-dimensional convolutional autoencoder (1D CAE) and a one-class support vector machine (OCSVM) as a classifier into a joint optimization framework is introduced in this paper for the first time. Using only the normal traffic samples, the approach simultaneously optimizes the 1D CAE for compact feature representation and the OCSVM for classification by defining a unified objective function combining reconstruction error with classification error. Thus, the generated compact feature representation has not only reconstruction ability but also discriminative ability for classification. An in-depth ablation analysis validates the design decisions and provides further insight of the proposed approach. An extensive set of experiments on two benchmark intrusion datasets, NSL-KDD and UNSW-NB15, demonstrates the generalization ability of the proposed model for unseen attacks and confirms it as a competitive approach over the recent state-of-the-art intrusion detection baselines. Overall, the obtained results emphasize that the proposed approach has potential to serve as a baseline for building an effective IDS. [ABSTRACT FROM AUTHOR]

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-theart performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate and timely acquisition of crop spatial distribution is a prerequisite for growth monitoring and yield forecasting. Currently, the automatic acquisition of crop distribution at large scales is still a challenge due to the time-consuming processing of remotely sensed imagery and manual collection of sufficient training samples. Although the advent of cloud computing platforms has proved to improve the efficiency and automation of crop type classification, how to obtain sufficient training samples in an efficient and cost-effective way remains unclear. In this research, we developed a new approach integrating the automatic generation of training samples and one-class machine learning classification (AGTOC) for mapping winter wheat over Jiangsu Province, China on Google Earth Engine (GEE). After extracting spatial objects from Sentinel-2 imagery in the season of 2017–2018, this method performed recognition of winter wheat objects based on the unique phenology and spectral features of winter wheat. Then the generated winter wheat objects were further refined and regarded as training samples for provincial winter wheat classification with one-class support vector machine (OCSVM). Furthermore, the transferability of AGTOC was evaluated by applying the classification approach to different seasons (2016–2017 & 2019–2020) and a different sensor (Landsat-8 OLI). According to independent ground truth data, the winter wheat mapping with AGTOC achieved an overall accuracy (OA) of 92.61%. When compared with agricultural census data, the winter wheat area accounted for 99% and 90% of the variability at the municipal and county levels. Furthermore, the OA achieved 88.94% and 90.17% while transferring the AGTOC from 2017–2018 to 2016–2017 and 2019–2020. The transferability of the AGTOC model to Landsat-8 OLI imagery of the same season yielded an OA of 85.98%. These results demonstrated that AGTOC exhibited high efficiency and accuracy across the province, different seasons and sensors without the need of extensive field visits for training sample collection. This proposed approach has great potential in the automatic mapping of winter wheat on GEE at country or global levels.

Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.

In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM's hinge-loss objective function and the random Fourier feature fitting radial basis function (RBF) kernel method, the OCSVM model is represented and solved using the deep neural network and gradient descent method. Finally, the decision function of the OCSVM model is constructed by using the solved parameter information to realize the detection of abnormal data. The proposed algorithm is compared with other machine-learning-based anomaly detection algorithms in terms of multiple indicators such as precision, recall, and F 1 score. The experimental results using various datasets show that the proposed algorithm has a better outlier recognition ability than the machine-learning-based anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

Unusual Spatio-Temporal fluctuations in cellular network traffic may lead to drastic network management misbehaviors and at least abnormal drops in quality of experience. It is also expected that the management of future cellular networks will mostly rely on machine learning and automation. In this article, we present a dynamic on-line data mining technique to detect these network anomalies allowing, network operators to pro-actively monitor and control a variety of real-world phenomena with less damage to the overall experience. To overcome the network performance degradation that can occur in real time, the network manager must imperatively and instantly identify abnormalities and hence provide a better continuous quality of service for the subscribers. Based on real cellular communication traces, we propose an automated framework, called STAD, ensuring spatio-temporal detection outliers using a combination of machine learning techniques including One-class SVM (OCSVM), Support Vector Regression (SVR) and recurrent neural networks, Long Short-Term Memory (LSTM). STAD is double checked with two real datasets of CDRs where results show high accuracy compared to the Isolation Forest and Auto-Regressive Integrated Moving Average (ARIMA) models. [ABSTRACT FROM AUTHOR]

Recent development of Internet of Things (IoT) technologies has triggered a soaring application of intelligent monitoring systems. A sensitive online fault detection for industrial equipment is of utmost significance to improve industrial intelligence based on video sensor network. In this paper, we propose the Appearance and Motion SVM (AMSVM), an online fault detection method fusing multimodal information based on One-Class SVM (OCSVM), to monitor the working conditions of unsupervised equipment in the field. It utilizes multimodal features to detect faults in terms of the appearance and motion patterns of equipment. The motion pattern was generated using the OCSVM-encoded histogram of optical flow orientation (HOFO), and meanwhile we employed Local Binary Pattern Histogram (LBPH) to extract texture features to train OCSVM, depicting appearance patterns. Then, decision level information (i.e., appearance and motion patterns) are combined to produce a more precise characteristic for fault detection. The proposed method herein was validated on several industrial video surveillance data set. [ABSTRACT FROM AUTHOR]

• A dictionary learning-based one-class classification model was established for multivariate anomaly detection in telemetry. • A novel optimisation function to learn dictionary and OCSVM decision boundary jointly was constructed. • We proposed an iterative approach to solve the optimisation function. • The proposed method improved detection precision, F1-score, FPR and FAR. Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies. [ABSTRACT FROM AUTHOR]

Process monitoring (PM) has been part of the safeguards approach for fuel cycle facilities for many years, but its use has been limited. For example, aqueous reprocessing plants may use bulk level measurements to generate a bulk material balance that can be correlated with traditional nuclear material accountancy measurements. However, advanced measurement technologies and modern data analytics approaches may provide new approaches for PM. New facility types may also drive the need for better approaches. Pyroprocessing plants have unique safeguards challenges and unique measurements that could be used to monitor operations. The purpose of the work presented here is to examine improved PM approaches for both aqueous and pyroprocessing facilities. Both unique measurements specific to those facility types as well as machine learning techniques to correlate various data types are being examined. The success metrics are either to improve detection probability or timeliness of detection or to reduce safeguards burden through increased use of unattended monitoring systems. This work relies on safeguards performance modeling to generate simulation data for training followed by diversion or misuse scenarios for testing the approaches. The approaches being considered and preliminary results are presented. [ABSTRACT FROM AUTHOR]

Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. We first analyzed how well MODIS NDVI and LST data quantify the properties of urban areas. Considering that urban area is the only class of interest, we applied the one-class support vector machine (OCSVM) to classify different combinations of the three datasets. We evaluated the effectiveness of the proposed method and compared with the locally optimized threshold method in regional urban extent mapping in China. The experimental results demonstrate that DMSP/OLS NTL data, MODIS NDVI and LST data provide different but complementary information sources to quantify the urban extent at a regional scale. The results also indicate that the OCSVM classification of the combination of all three datasets generally outperformed the locally optimized threshold method. The proposed method effectively and efficiently extracted the urban extent at a regional scale, and is applicable to other study areas.

Air quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems. This paper reports the development of an unsupervised and efficient scheme to detecting anomalies in unlabeled ozone measurements. This scheme combines a deep belief networks (DBNs) model and a one-class support vector machine (OCSVM). The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Isère in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, restricted Boltzmann machines-based OCSVM, and DBN-based clustering procedures (i.e., K-means, Birch, and expectation–maximization). The results show that the developed strategy is able to identify anomalies in ozone measurements. [ABSTRACT FROM AUTHOR]

A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles. [ABSTRACT FROM AUTHOR]

With the growing complexity of modern vehicles in the digital era, ensuring the reliability of vehicle system becomes a challenge that needs a responsive framework to monitor and alert the unhealthy states. An unhealthy state refers to a timestamp in which vehicle operates off the expected range of performance. Being able to detect an unhealthy state early not only helps to identify imminent performance failures but also indicates the necessity of deploying preventive maintenance. It is almost impossible to be aware of an imminent failure without modeling the behavior of various subsystems of vehicle. This study proposes a behavior modeling and anomaly detection approach to accurately analyze the operation of various vehicle subsystems and trigger early alerts of unhealthy states. We propose a Multi-Layer Long Short Term Memory (LSTM) network that is integrated with an Autoencoder architecture (ML-LSTMAE) to monitor and predict the operation of different subsystems of vehicle. Modern vehicles involve various subsystems with respective sensors that record various operating signals. The proposed model leverages multivariate time series data sequences that are recorded by different sensors to train an encoding–decoding scheme. We use One-Class Support Vector Machine (OCSVM) to analyze the reconstruction errors and build a support boundary. Then, the trained support boundary evaluates the prediction errors in test data to distinguish the healthy and unhealthy states of vehicle. We validate the efficiency of the proposed behavior modeling and anomaly detection approaches via evaluating two case study problems. First, a case study of an operating vehicle data that consists of 15 months time series records of 101 data channels. Second, a NASA bearing case study data that includes 20,110,378 observational records of four bearings time series sequences. The results of both case studies confirm the high accuracy of the proposed ML-LSTMAE network for learning the latent behavior of different operating subsystems. Moreover, the proposed OCSVM algorithm classifies the healthy and unhealthy states in both case studies, precisely. [ABSTRACT FROM AUTHOR]

Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications.

Modern Supervisory Control and Data Acquisition (SCADA) systems used by the electric utility industry to monitor and control electric power generation, transmission and distribution are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasing numbers of widely distributed components. The presence of a real time intrusion detection mechanism, which can cope with different types of attacks, is of great importance in order to defend a system against cyber attacks. This defense mechanism must be distributed, cheap and above all accurate, since false positive alarms or mistakes regarding the origin of the intrusion mean severe costs for the system. Recently an integrated detection mechanism, namely IT-OCSVM , was proposed, which is distributed in a SCADA network as a part of a distributed intrusion detection system (DIDS), providing accurate data about the origin and the time of an intrusion. In this paper we also analyze the architecture of the integrated detection mechanism and we perform extensive simulations based on real cyber attacks in a small SCADA testbed in order to evaluate the performance of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China’s Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.

Abstract: As one of the methods to solve one-class classification problems (OCC), one-class support vector machines (OCSVM) have been applied to fault detection in recent years. Among all the kernels available for OCSVM, the Gaussian kernel is the most commonly used one. The selection of Gaussian kernel parameters influences greatly the performances of classifiers, which remains as an open problem. In this paper two methods are proposed to select Gaussian kernel parameters in OCSVM: according to the first one, the parameters are selected using the information of the farthest and the nearest neighbors of each sample; using the second one, the parameters are determined via detecting the “tightness” of the decision boundaries. The two proposed methods are tested on UCI data sets and Tennessee Eastman Process benchmark data sets. The results show that, the two proposed methods can be used to select suitable parameters for the Gaussian kernel, enabling the resulting OCSVM models to perform well on fault detection. [Copyright &y& Elsevier]

Abstract: In experimental design for functional molecules, materials and products, complex relationships exist between the various experimental parameters and the target physical and chemical properties. Regression analyses with experimental data are a useful way to understand these relationships. A carefully constructed regression model can be used to search for functional products effectively. However, although these products can be found in the extrapolation domains of the existing data, the predictive ability of the model tends to be low in regions where the data density is low, and new candidates where the predicted values of a property are unreliable will not achieve the desired property values. Therefore, to search for new candidates in appropriate extrapolation domains, we consider the probability that a new candidate will have the intended property values and the reliability of a predicted property value for this candidate. The probability is calculated from a predicted value and the estimated prediction error, and the reliability is based on the data density. The proposed method is applied to both simulation data and aqueous solubility data, and the efficiency of the method is demonstrated through data analyses. [Copyright &y& Elsevier]

• Support Vector Data Description (SVDD) is a popular kernel-based unsupervised one-class classification method. The Gaussian kernel is the most common used kernel. • The Gaussian kernel has a tuning parameter, the kernel bandwidth, and it is important to choose it correctly. • We propose an automated, unsupervised, bandwidth selection method for SVDD. • Our proposed bandwidth is also appropriate for selecting the bandwidth for One Class Support Vector Machines (OCSVM). Support vector data description (SVDD) is a popular anomaly detection technique. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly to ensure good results. A small bandwidth leads to overfitting, and the resulting SVDD classifier overestimates the number of anomalies, whereas a large bandwidth leads to underfitting and an inability to detect many anomalies. In this paper, we present a new, unsupervised method for selecting the Gaussian kernel bandwidth. Our method exploits a low-rank representation of the kernel matrix to suggest a kernel bandwidth value. Our new technique is competitive with the current state of the art for low-dimensional data and performs extremely well for many classes of high-dimensional data. This method is also applicable to one-class support vector machines (OCSVM). [ABSTRACT FROM AUTHOR]

Background: Image reconstruction is the mathematical process which converts the signals obtained from the scanning machine into an image. The reconstructed image plays a fundamental role in the planning of surgery and research in the medical field., Discussion: This paper introduces the first comprehensive survey of the literature about medical image reconstruction related to diseases, presenting a categorical study about the techniques and analyzing advantages and disadvantages of each technique. The images obtained by various imaging modalities like MRI, CT, CTA, Stereo radiography and Light field microscopy are included. A comparison on the basis of the reconstruction technique, Imaging Modality and Visualization, Disease, Metrics for 3D reconstruction accuracy, Dataset and Execution time, Evaluation of the technique is also performed., Conclusion: The survey makes an assessment of the suitable reconstruction technique for an organ, draws general conclusions and discusses the future directions., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)