This paper introduces an effective approach for detecting road traffic congestion. This approach uses a hybrid observer (HO) that exploits both the flexibility and simplicity of the piecewise switched linear model to estimate the traffic density parameter and employs a generalized likelihood ratio (GLR) test to detect traffic congestion. We evaluated the HO-GLR with real data from a segment of the four-lane State Route 60 (SR-60) highway in southern California. Results show that the HO-GLR approach is suitable for traffic congestion monitoring.

An approach combining the Hotelling $T^{2}$ control method with a weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling $T^{2}$ procedure is introduced to identify features corresponding to changed areas. Nevertheless, $T^{2}$ scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and $k$ -nearest neighbors) highlight the superiority of the proposed method.

Reliable detection of traffic congestion provides pertinent information for improving safety and comfort by alerting the driver to crowded roads or providing useful information for rapid decision-making. This paper addresses the problem of road traffic congestion estimation and detection from a statistical approach. First, a piecewise switched linear traffic model (PWSL)-based observer is introduced. The proposed hybrid observer (HO) estimates the unmeasured traffic density, thus reducing the cost of implementing and maintenance sensors and measurements devices. Here, the observer gains of each mode are obtained by solving a set of linear matrix inequalities. Second, a novel method for efficiently monitoring traffic congestion is proposed by combining the proposed HO with a generalized likelihood ratio (GLR) test. Also, an exponentially-weighted moving average (EWMA) filter is applied to the residual data to reduce high-frequency noise. Thus, as the EWMA filter, aggregates all of the information from past and actual samples in the decision rule, it extends the congestion detection abilities of the GLR test to the detection of incipient changes. This study shows that a better performance is achieved when GLR is applied to filtered data than to unfiltered data. The effectiveness of the proposed approach is verified on traffic data from the four-lane State Route 60 (SR-60) and the three lanes Interstate 210 (I-210) in California freeways. Results show the efficacy of the proposed HO-based EWMA-GLR method to monitor traffic congestions. Also, the proposed approach is compared to that of the conventional Shewhart and EWMA approaches and found better performance.

Denial of service (DOS) and distributed DOS (DDOS) continue to be a significant concern in internet and networking systems. This paper targets to develop an anomaly detection mechanism based on the generalized likelihood ratio (GLR) scheme to detect TCP and ICMPv6 based DOS/DDOS attacks. The anomaly detection problem is addressed as a hypothesis testing problem. The proposed approach uses GLR test to monitor internet traffic for better detecting potential cyber- attacks. The decision threshold of GLR approach has been computed non parametrically based on kernel density estimation. To evaluate the performance of this approach, two network traffic datasets have been used namely the DARPA99 and ICMPv6 datasets. Results highlight the efficiency of the proposed method.

This paper proposes an intrusion detection scheme for Denial Of Service (DOS) and Distributed DOS (DDOS) attacks detection. We used Hellinger distance (HD), which is an effective measure to quantify the similarity between two distributions, to detect the presence of potential malicious attackers. Specifically, we applied HD-based anomaly detection mechanism to detect SYN and ICMPv6-based DOS/DDOS attacks. Here, Shewhart chart is applied to HD to set up a detection threshold. The proposed mechanism is evaluated using DARPA99 and ICMPv6 traffic datasets. Results indicate that our mechanism accomplished reliable detection of DOS/DDOS flooding attacks.

Monitoring inflow measurements of water resource recovery facilities (WRRFs) is essential to promptly detect abnormalities and helpful in the decision making of the operators to better optimize, take corrective actions, and maintain downstream processes. In this paper, we introduced a flexible and reliable monitoring soft sensor approach to detect and identify abnormal influent measurements of WRRFs to enhance their efficiency and safety. The proposed data-driven soft sensor approach merges the desirable characteristics of principal component analysis (PCA) with k-nearest neighbor (KNN) scheme. PCA performed effective dimension reduction and revealed interrelationships between inflow measurements, while KNN distances demonstrated superior detection capacity, robustness to underlying data distribution, and efficiency in handling high-dimensional dataset. Furthermore, nonparametric thresholds derived from kernel density estimation further enhanced detection results of PCA-KNN approach when compared with parametric counterparts. Moreover, the radial visualization plot is innovatively employed for fault analysis and diagnosis in combination with PCA and delineated interpretable visualization of anomalies and detector performances. The effectiveness of these soft sensor schemes is evaluated by using real data from a coastal municipal WRRF located in Saudi Arabia. Also, we compared the proposed soft sensor scheme with the conventional PCA-based approaches, including standard prediction error, Hotelling’s T2, and joint univariate methods. Results demonstrate that this soft sensor-based monitoring approach outperforms conventional PCA-based methods.

Fault detection in robotic swarm systems is imperative to guarantee their reliability, safety, and to maximize operating efficiency and avoid expensive maintenance. However, data from these systems are generally contaminated with noise, which masks important features in the data and degrades the fault detection capability. This paper introduces an effective fault detection approach against noise and uncertainties in data, which integrates the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed scheme has been applied to the uncorrelated residuals form principal component analysis model. A simulated data from ARGoS simulator is used to evaluate the effectiveness of the proposed method. Also, we compared the performance of the proposed approach to that of the conventional principal component-based approach and found improved sensitivity to faults and robustness to noises. For all the fault types tested–abrupt faults, random walks, and complete stop faults–our approach resulted in a significant enhancement in fault detection compared with the conventional approach.

This paper presents a model-based anomaly detection method for supervising the direct current (dc) side of photovotaic (PV) systems. Toward this end, a framework combining the benefits of k-nearest neighbors (kNN) with univariate monitoring approaches has been proposed. Specifically, kNN-based Shewhart and exponentially weighted moving average (EWMA) schemes with parametric and nonparametric thresholds have been introduced to suitably detect faults in PV systems. The choice of kNN method to separate normal and abnormal features is motivated by its capacity to handle nonlinear features and do not make assumptions on the underlying data distribution. In addition, because the EWMA approach is sensitive in detecting small changes. First, a simulation model for the inspected PV array is constructed. Then, residuals generated from this model are employed as the input for kNN-based schemes for anomaly detection. Parametric and nonparametric thresholds using kernel density estimation have been used to detect faults. The effectiveness of the kNN-based procedures is verified using actual measurements from a 9.54-kWp grid-connected system in Algeria. Results proclaim the efficiency of the proposed strategy to supervise the dc side of PV systems.

One of the greatest challenges in a photovoltaic solar power generation is to keep the designed photovoltaic systems working with the desired operating efficiency. Towards this goal, fault detection in photovoltaic plants is essential to guarantee their reliability, safety, and to maximize operating profitability and avoid expensive maintenance. In this context, a model-based anomaly detection approach is proposed for monitoring the DC side of photovoltaic systems and temporary shading. First, a model based on the one-diode model is constructed to mimic the characteristics of the monitored photovoltaic array. Then, a one-class Support Vector Machine (1SVM) procedure is applied to residuals from the simulation model for fault detection. The choice of 1SVM approach to quantify the dissimilarity between normal and abnormal features is motivated by its good capability to handle nonlinear features and do not make assumptions on the underlying data distribution. Experimental results over real data from a 9.54 kWp grid-connected plant in Algiers, show the superior detection efficiency of the proposed approach compared with other binary clustering schemes (i.e., K-means, Birch, mean-shift, expectation–maximization, and agglomerative clustering).

Monitoring the operation condition of photovoltaic (PV) systems is crucial to improving their efficiency. In this paper, an effective method to supervise the DC part of PV plants under noisy environment is provided. In fact, noisy measurements make the supervision more challenging as the feature extraction of the fault is more difficult. The designed approach merges the desirable proprieties of the discrete wavelet transform and the exponentially weighted moving average scheme to appropriately detect faults in PV system. Specifically, this approach is employed to check the residuals generated by a simulation model based on a single-diode modeling for fault detection. We evaluated the efficiency of the proposed approach on a real PV system in Algeria. Results indicated that the proposed approach has good capacity in supervising the DC part of PV plants.