1,721,081 research outputs found
Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling
No full textThis paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The
system is able to understand whether the novel data belongs to the fault dictionary, is fault-free or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer Perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.JRC.G.5 - Security technology assessmen
Fault diagnosis for smart grids in pragmatic conditions
No full textDue to the advancements of electrical networks, the operators are able to employ a gamut of information for assessing the state of the infrastructure facilitating diagnosis of potential malfunctions appearing in one or more components of the grid. This paper presents a cognitive fault diagnosis framework for smart grids (SG) which exploits the temporal and functional relationships existing within the datastreams coming from the nodes of the network. The protection of SGs can rely not only on conventional techniques (e.g. circuit breakers) but also on processing information which is available thanks to the information and communication layer. We propose a framework which is able to autonomously learn the model of the nominal state using the respective data by means of hidden Markov models operating in the parameter space of linear time-invariant models. Subsequently, the framework is able to detect data not belonging to the nominal state and localize the potential fault at the cognitive level. The isolation is based on a graph representation of the SG revealing the correlations among the nodes based on the Granger causality. We conducted thorough experiments on the IEEE-9 bus system model achieving encouraging results in terms of false positive/negative rate, and detection/isolation delay
Directed Acyclic Graphs for Content Based Sound, Musical Genre, and Speech Emotion Classification
No full textThis work introduces the methodology of Decision Directed Acyclic Graphs (DDAG)11 This work uses the following abbreviations as regards to directed graphs: Directed Acyclic Graph (DAG), Decision Directed Acyclic Graph (DDAG) and Directed Acyclic Graph Hidden Markov Model (DAGHMM). to the scientific domain of content based audio signal processing. We apply the particular methodology to three multiclass classification problems involving the categories of generalized sound events, musical genres, and speech expressing emotional states. A decision graph is constructed which breaks the overall problem into a series of two-class ones. The order of the graph nodes is revealed using a clustering criterion based on the Kullback-Leibler divergence. Every graph node is composed by two hidden Markov models, each one representing the class which participates in the specific problem. We extract three heterogeneous feature sets (Mel-Filterbank, MPEG-7 Audio Spectrum Projection and Perceptual Wavelet Packets) out of each recording and fuse them for training the HMMs. Extensive comparative experiments are conducted using the following three datasets: (a) a combination of professional sound effects collections, (b) GTZAN musical genre database, and (c) BERLIN emotional speech corpus. The results demonstrate the superiority of the DDAG classification approach over the standard HMM approach regardless the application task
Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling
No full textThis paper presents an anomaly-based methodology for reliable detection of integrity attacks in cyber-physical critical infrastructures. Such malicious events compromise the smooth operation of the infrastructure while the attacker is able to exploit the respective resources according to his/her purposes. Even though the operator may not understand the attack, since the overall system appears to remain in a steady state, the consequences may be of catastrophic nature with a huge negative impact. Here, we apply a computational intelligent technique which incorporates the merits of two of the heterogeneous modeling approaches (linear time-invariant and neural networks), while considering both temporal and functional dependencies existing among the elements of an infrastructure. The experimental platform includes a power grid simulator of the IEEE 30 bus model and a cyber network emulator. Subsequently, we implemented a wide range of integrity attacks (replay, ramp, pulse, scaling, and random) with different intensity levels. A thorough evaluation procedure is carried out while the results demonstrate the ability of the proposed method to produce a desired result in terms of false positive rate, false negative rate, and detection delay
Universal background modeling for acoustic surveillance of urban traffic
No full textTraffic congestion in modern cities is an increasing problem having significant consequences in our daily lives. This work proposes a non-intrusive, passive monitoring framework based on the acoustic modality which can be used either autonomously or as a part of a multimodal system and provide valuable information to an intelligent transportation system. We consider a large number of audio classes which are typically encountered in urban areas. We introduce a combination of a powerful audio representation mechanism based on time, frequency and wavelet domain features with universal background modeling which leads to higher recognition accuracies and detection rates (in terms of false alarm and miss probability rates) with respect to commonly employed methodologies. The basic advantage of a class-specific model derived using the universal background modeling logic is its tolerance to data which belong to other sound classes. Another important feature of the proposed system is its ability to detect crash incidents, which apart from their catastrophic impact on human life and property, have negative consequences on the traffic flow. Our experiments are based on the concurrent usage of professional sound effect collections which include audio recordings of high quality. We thoroughly examine the performance of the proposed system on isolated sound events as well as continuous audio streams using confusion matrices and detection error trade-off curves
Audio pattern recognition of baby crying sound events
No full textThis article addresses a problem arising within the paralinguistic audio signal processing domain-that of classifying the state of an infant based on the patterns exhibited by the crying sound events. More specifically we propose a methodology able to distinguish among the following five states: (a) hungry, (b) uncomfortable (need change), (c) need to burp, (d) in pain, and (e) need to sleep. A great variety of audio parameters (Perceptual Linear Prediction, Mel Frequency Cepstral Coefficients, Perceptual Wavelet Packets, Teager Energy Operator, Temporal Modulation) related to the task at hand along with a series of classification techniques (Multilayer Perceptron, Support Vector Machine, Random Forest, Reservoir Network, Gaussian Mixture model, Hidden Markov model) were customized for addressing the issue in a reliable manner. The final implementation exploits a representation of the audio structure including a set of descriptors capturing heterogeneous aspects of the signal. Subsequently we introduce the usage of Reservoir Networks to the specific problematic that demonstrated quite encouraging performance. The final goal of the method is to provide an automatic and non-invasive framework for monitoring infants and helping inexperienced/trainee pediatricians and/or parents and babysitters to diagnose their pathological status
Automatic identification of integrity attacks in cyber-physical systems
No full textModern society relies on the availability and smooth operation of complex engineering systems, such as electric power systems, water distributions networks, etc. which due to the recent advancements in information and communication technologies (ICT) are usually controlled by means of a cyber-layer. This design may potentially improve the usage of the components of the cyber-physical system (CPS), however further protection is needed due to the emerging threat of cyber-attacks. These may degrade the quality of the communicated information which is of fundamental importance in the decision making process. This paper proposes a novel methodology for automatic identification of the type of the integrity attack affecting a CPS. We designed a feature set for capturing the characteristics of each attack in the spectral and wavelet domains while its distribution is learned by pattern recognition algorithms of different modelling properties customized for the specific application scenario. In addition a novelty detection component is incorporated for dealing with previously unseen types of attacks. The proposed approach is applied onto data coming from the IEEE-9 bus model achieving promising identification performance
A Novel Holistic Modeling Approach for Generalized Sound Recognition
No full textNowadays, generalized sound recognition technology is constantly gaining attention within the generic context of scene analysis and understanding (smart-home, surveillance, bioacoustics, etc.). It is typically achieved using a set of relevant to the task at hand descriptors modelled by means of a statistical tool, e.g., hidden Markov model. This work exhaustively applies the Universal Modeling (UM) (or class-independent) approach on the particular task. The feature extraction engine extracts descriptors belonging to time, frequency and wavelet domains. We describe a novel data selection scheme based on Gaussian mixture model clustering for the creation of the UM. The scheme takes into account the dataset characteristics, adapts itself to them and leads to higher recognition rates than the standard UM approach
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