Biblio

Event detection and classification are crucial to power system stability. The Wide Area Measurement System (WAMS) technology helps in enhancing wide area situational awareness by providing useful synchronized information to the grid control center in order to accurately identify various power system events. This paper demonstrates the viability of using EWAMS (Egyptian Wide Area Measurement System) data as one of the evolving technologies of smart grid to identify extreme events within the Egyptian power grid. The proposed scheme is based on online synchronized measurements of wide-area monitoring devices known as Frequency Disturbance Recorders (FDRs) deployed at selected substations within the grid. The FDR measures the voltage, voltage angle, and frequency at the substation and streams the processed results to the Helwan University Host Server (HUHS). Each FDR is associated with a timestamp reference to the Global Positioning System (GPS) base. An EWAMS-based frequency disturbance detection algorithm based on the rate of frequency deviation is developed to identify varies types of events such as generator trip and load shedding. Based on proper thresholding on the frequency and rate of change of frequency of the Egyptian grid, different types of events have been captured in many locations during the supervision and monitoring the operation of the grid. EWAMS historical data is used to analyze a wide range of data pre-event, during and post-event for future enhancement of situational awareness as well as decision making.

Optical time domain reflectometry (OTDR) plays an important role in optical fiber communications. To improve the performance of OTDR, we propose a method based on the Variational Mode Decomposition (VMD) and Hilbert transform (HT) for fiber events detection. Firstly, the variational mode decomposition is applied to decompose OTDR data into some intrinsic mode functions (imfs). To determine the decomposition mode number in VMD, an adaptive estimation method is introduced. Secondly, the Hilbert transform is utilized to obtain the instantaneous amplitude of the imf for events localization. Finally, the Dynamic Time Warping (DTW) is used for identifying the type of event. Experimental results show that the proposed method can locate events accurately. Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable.

Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems.

The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.

Due to environmental influence and technology limitation, a wireless sensor/sensors module can neither store or process all raw data locally nor reliably forward it to a destination in heterogeneous IoT environment. As a result, the data collected by the IoT's sensors are inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities. Therefore, it is highly recommended and desirable to acquire trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for trustworthy data acquisition and faulty sensor detection in the IoT environment. The key idea of IGTDC is to allow a sensor's module to examine locally whether the raw data is trustworthy before transmitting towards upstream nodes. It further distinguishes whether the acquired data can be trusted or not before data aggregation at the sink/edge node. Besides, IGTDC helps to recognize a faulty or compromised sensor. For a reliable data collection, we use collaborative IoT technique, gate-level modeling, and programmable logic device (PLD) to ensure that the acquired data is reliable before transmitting towards upstream nodes/cloud. We use a hardware-based technique called “Gray Code” to detect a faulty sensor. Through simulations we reveal that the acquired data in IGTDC framework is reliable that can make a trustworthy data collection for event detection, and assist to distinguish a faulty sensor.

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time. In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.

Supervisory Control and Data Acquisition (SCADA) systems play a critical role in the operation of large-scale distributed industrial systems. There are many vulnerabilities in SCADA systems and inadvertent events or malicious attacks from outside as well as inside could lead to catastrophic consequences. Network-based intrusion detection is a preferred approach to provide security analysis for SCADA systems due to its less intrusive nature. Data in SCADA network traffic can be generally divided into transport, operation, and content levels. Most existing solutions only focus on monitoring and event detection of one or two levels of data, which is not enough to detect and reason about attacks in all three levels. In this paper, we develop a novel edge-based multi-level anomaly detection framework for SCADA networks named EDMAND. EDMAND monitors all three levels of network traffic data and applies appropriate anomaly detection methods based on the distinct characteristics of data. Alerts are generated, aggregated, prioritized before sent back to control centers. A prototype of the framework is built to evaluate the detection ability and time overhead of it.

We present a system for identifying interesting social media posts on Twitter and delivering them to users' mobile devices in real time as push notifications. In our problem formulation, users are interested in broad topics such as politics, sports, and entertainment: our system processes tweets in real time to identify relevant, novel, and salient content. There are three interesting aspects to our work: First, instead of attempting to tame the cacophony of unfiltered tweets, we exploit a smaller, but still sizeable, collection of curated tweet streams corresponding to the Twitter accounts of different media outlets. Second, we apply distant supervision to extract topic labels from curated streams that have a specific focus, which can then be leveraged to build high-quality topic classifiers essentially "for free". Finally, our system delivers content via Twitter direct messages, supporting in situ interactions modeled after conversations with intelligent agents. These ideas are demonstrated in an end-to-end working prototype.

The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.

This work introduces concepts and algorithms along with a case study validating them, to enhance the event detection, pattern recognition and anomaly identification results in real life video surveillance. The motivation for the work underlies in the observation that human behavioral patterns in general continuously evolve and adapt with time, rather than being static. First, limitations in existing work with respect to this phenomena are identified. Accordingly, the notion and algorithms of Dynamic Clustering are introduced in order to overcome these drawbacks. Correspondingly, we propose the concept of maintaining two separate sets of data in parallel, namely the Normal Plane and the Anomaly Plane, to successfully achieve the task of learning continuously. The practicability of the proposed algorithms in a real life scenario is demonstrated through a case study. From the analysis presented in this work, it is evident that a more comprehensive analysis, closely following human perception can be accomplished by incorporating the proposed notions and algorithms in a video surveillance event.

Event discovery from single pictures is a challenging problem that has raised significant interest in the last decade. During this time, a number of interesting solutions have been proposed to tackle event discovery in still images. However, a large scale benchmarking image dataset for the evaluation and comparison of event discovery algorithms from single images is still lagging behind. To this aim, in this paper we provide a large-scale properly annotated and balanced dataset of 490,000 images, covering every aspect of 14 different types of social events, selected among the most shared ones in the social network. Such a large scale collection of event-related images is intended to become a powerful support tool for the research community in multimedia analysis by providing a common benchmark for training, testing, validation and comparison of existing and novel algorithms. In this paper, we provide a detailed description of how the dataset is collected, organized and how it can be beneficial for the researchers in the multimedia analysis domain. Moreover, a deep learning based approach is introduced into event discovery from single images as one of the possible applications of this dataset with a belief that deep learning can prove to be a breakthrough also in this research area. By providing this dataset, we hope to gather research community in the multimedia and signal processing domains to advance this application.