Approach For Event Classification Research Articles

Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks

The rampant propagation of rumors in online social networks leads to potential damage to society. This phenomenon has attracted significant attention to the researches on faster rumor detection. Most recent works on rumor detection adapted the neural network-based deep learning approaches because of its high success rate. Though neural network-based models have high success rates, these models are less efficient in finding rumors at the earliest. Such models require huge training data for better and accurate results. Unfortunately, the data available in the early stages of rumor propagation are sparse in nature. This nature makes rumor detection using neural networks a complex solution, therefore rendering less efficiency for early stage rumors. To address this issue, we have proposed a certainty-factor-based convolutional neural network (CNN) approach to efficiently classify events as rumor or not by leveraging the inherent features of the set of information in spite of data sparsity. The certainty-factor-based activation function requires a minimum number of training data to obtain a generalization. In the proposed approach, two parallel CNNs are employed for the rumor event classification task that efficiently utilizes the inherent features of information, such as temporal, content, and propagation features. A decision tree combines the outputs of both CNNs and provides the classification output. This rumor event classification approach is then compared with recent and well-known state-of-the-art rumor classification approaches, and the results prove that the proposed approach detects rumor efficiently and rapidly with minimal inputs compared to other approaches.

Event classification using improved salp swarm algorithm based probabilistic neural network in fiber-optic perimeter intrusion detection system

Abstract To remarkably upgrade the performance of the intrusion detection system based on single-mode—multimode—single-mode (SMS) fiber structure which can effectively discriminate man-made event and natural event in zone perimeter, a novel event classification approach is presented in this paper. Firstly, the original intrusion signal is decomposed by the filter bank into four frequency channels with an interval of 11745 Hz. Then, the singular value and kurtosis extracted from each frequency channel as combinations of eigenvectors, being transferred to the probabilistic neural network (PNN) for training and classification. Finally, the weight factor and adaptive mutation operator are embedded in the salp swarm algorithm (SSA), which is utilized to optimize and search for the optimal smoothing factor of the PNN classifier and compared with the other four metaheuristic (MH) algorithms under five algorithm performance evaluation metrics. Large practical experiments exhibit that the presented scheme can accurately recognize man-made events (knocking, rattling) and natural events (wind, rain) respectively, shedding light on the feasibility and applicability of SMS fiber structure in zone perimeter.

Data Driven Method for Event Classification via Regional Segmentation of Power Systems

This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy.

Real-Time Event Classification in Power System With Renewables Using Kernel Density Estimation and Deep Neural Network

Real-time classification of events facilitates corrective control strategies, supervisory protection schemes, and on-line transient stability assessment of a power system. The synchrophasor-based event classification techniques face challenges like similar responses for different classes of events, i.e., inter-class similarity (ICS), applicability to limited classes of events, and moderate real-time performance for a large power system. In addition, the enhanced ICS effect of increased renewable penetration on events classification needs to be addressed. This paper proposes a kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data. The proposed method uses a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs). That have distinct scale, shape, and orientation for different classes of events. Thereafter, a set of statistical features is derived from PDFs to train a multi-layered deep neural network for event classification. The proposed method is validated for renewables in IEEE-39 bus system and real transmission system of India grid using DIgSILENT/PowerFactory and also on a real phasor measurement unit data for India grid, where it showed better performance for ICS and renewable integration cases.

Practical multi-class event classification approach for distributed vibration sensing using deep dual path network.

Influenced by severe ambient noises and nonstationary disturbance signals, multi-class event classification is an enormous challenge in several long-haul application fields of distributed vibration sensing technology (DVS), including perimeter security, railway safety monitoring, pipeline surveillance, etc. In this paper, a deep dual path network is introduced into solving this problem with high learning capacity. The spatial time-frequency spectrum datasets are built by utilizing the multidimensional information of DVS signal, especially the spatial domain information. With the novel datasets and a high-parameter-efficiency network, the proposed scheme presents good reliability and robustness. The feasibility is verified in an actual railway safety monitoring field test, as a proof-of-concept. Seven types of real-life disturbances were implemented and their f1-scores all reached up to 97% in the test. The performance of this proposed approach is fully evaluated and discussed. The presented approach can be employed to improve the performance of DVS in actual applications.

A New Protection Scheme for Multi-Bus DC Power Systems Using an Event Classification Approach

DC microgrid systems with hybrid energy resources are of significant interest in shipboard and spacecraft applications. In such a system, coordinated operation of circuit breakers (CBs) and disconnecting switches (DSs) can rapidly isolate short-circuit faults and restore the system very fast. In this paper, we investigate a new protection scheme for a Multi-Bus dc systems based on an event classification approach. The proposed protection scheme transfers less data when compared with commonly used data-based protection methods and does not require high-speed communication and synchronization. The results show that the developed protection scheme is able to identify the type of fault accurately, isolate the faulted area, and restore the system very quickly while maintaining continuous power to the loads.

Event Classification in Foreign Language Aviation Reports

When adverse aviation events occur, narrative reports describing the events and their associated flights provide a valuable record for improving safety. Manual examination of large collections of such reports is challenging. Tools for automated event classification (assignment of type labels to individual reports) can help to mitigate this challenge. While several studies have developed and systematically empirically evaluated event classification tools on English aviation narratives, we are not aware of any that have done the same on foreign language narratives. We developed and implemented an approach for event classification based on Bayesian logistic regression and a novel feature selection technique. For comparison purposes, we also implemented an approach described in the literature. We collected and annotated a corpus of Japanese aviation incident reports, as well as, a corpus of French incident reports. We carried out a series of experiments comparing the accuracy of our approach and the other approach.

Event classification in foreign language aviation reports

When adverse aviation events occur, narrative reports describing the events and their associated flights provide a valuable record for improving safety. Manual examination of large collections of such reports is challenging. Tools for automated event classification (assignment of type labels to individual reports) can help to mitigate this challenge. While several studies have developed and systematically empirically evaluated event classification tools on English aviation narratives, we are not aware of any that have done the same on foreign language narratives. We developed and implemented an approach for event classification based on Bayesian logistic regression and a novel feature selection technique. For comparison purposes, we also implemented an approach described in the literature. We collected and annotated a corpus of Japanese aviation incident reports, as well as, a corpus of French incident reports. We carried out a series of experiments comparing the accuracy of our approach and the other approach.

Traffic event classification at intersections based on the severity of abnormality

This paper proposes a novel traffic event classification approach using event severities at intersections. The proposed system basically learns normal and common traffic flow by clustering vehicle trajectories. Common vehicle routes are generated by implementing trajectory clustering with Continuous Hidden Markov Model. Vehicle abnormality is detected by observing maximum likelihoods of partial vehicle locations and velocities on underlying common route models. The second part of the work is based on extracting the severities of abnormality by deviation measurement using Coefficient of Variances method. By using abnormal event samples, two severity classes are built in order to recognize event severities by Support Vector Machines and k-Nearest Neighborhood algorithms. Experimental results show that the proposed model has high precision with satisfactory incident detection and event severity classification performance.

DEFACTO: Distributed Event ClassiFicATiOn in Wireless Sensor Networks

Sensing is a fundamental function in wireless sensor networks. Researchers have built WSN platforms with a wide spectrum of sensors, ranging from simple thermostats to micro power impulse radars. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. In this work, we propose a distributed approach for event classification in wireless sensor networks. This approach is based on the assumption that events to be detected can be characterized by a set of features where each can be measured by a specific kind of sensors. The paradigm is composed of two phases: a training phase and a classification phase. In the training phase, for each event type E and a feature F, a set of values is determined. Then, in each node with a sensor corresponding to a feature F we maintain a 2-dimensional array where columns represent event types and rows represent the divisions in the readings' range of the corresponding sensor. In the classification phase, the network detects and classifies events in a distributed fashion using a voting-like technique in which individual nodes contribute to the classification. Our algorithms are validated through extensive simulations and analysis.