Search results for: distributed intrusion detection system

Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, a residual generation filter with finite memory structure is proposed for fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Keywords: residual generation filter, finite memory structure, kalman filter, fast detection

Procedia PDF Downloads 671

Authors: Sangeeta Palekar, Mahima Rana, Jayu Kalambe

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In this paper, a microfluidic-based platform for the quantification of contaminants in the water is proposed. The proposed system uses microfluidic channels with an embedded environment for contaminants detection in water. Microfluidics-based platforms present an evident stage of innovation for fluid analysis, with different applications advancing minimal efforts and simplicity of fabrication. Polydimethylsiloxane (PDMS)-based microfluidics channel is fabricated using a soft lithography technique. Vertical and horizontal connections for fluid dispensing with the microfluidic channel are explored. The principle of colorimetry, which incorporates the use of Griess reagent for the detection of nitrite, has been adopted. Nitrite has high water solubility and water retention, due to which it has a greater potential to stay in groundwater, endangering aquatic life along with human health, hence taken as a case study in this work. The developed platform also compares the detection methodology, containing photodetectors for measuring absorbance and image sensors for measuring color change for quantification of contaminants like nitrite in water. The utilization of image processing techniques offers the advantage of operational flexibility, as the same system can be used to identify other contaminants present in water by introducing minor software changes.

Keywords: colorimetric, fluid contaminants, nitrite detection, microfluidics

Procedia PDF Downloads 175

Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

Procedia PDF Downloads 146

Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad

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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.

Keywords: breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration

Procedia PDF Downloads 182

Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

Procedia PDF Downloads 316

Authors: Charuvan Kasemsap

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This research was studied in Bangnampriao District, Chachernsao Province, Thailand. The primary data relating to flooding, drought, and saline intrusion problem on agriculture were collected by surveying, focus group, and in-depth interview with agricultural officers, technical officers of irrigation department, and local government leader of Bangnampriao District. The likelihood and consequence of risk were determined the risk index by risk assessment matrix. In addition, the risk index and the total coping capacity scores were investigated the vulnerability index by vulnerability matrix. It was found that the high-risk drought and saline intrusion was dramatically along Bang Pakong River owing to the end destination of Chao Phraya Irrigation system of Central Thailand. This leads yearly the damage of rice paddy, mango tree, orchard, and fish pond. Therefore, some agriculture avoids rice growing during January to May, and also pumps fresh water from a canal into individual storage pond. However, Bangnampriao District will be strongly affected by the impacts of climate change. Monthly precipitations are expected to decrease in number; dry seasons are expected to be more in number and longer in duration. Thus, the risk and vulnerability of agriculture are also increasing. Adaptation strategies need to be put in place in order to enhance the resilience of the agriculture.

Keywords: agriculture, bangnampriao, climate change, risk assessment

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

Procedia PDF Downloads 87

Authors: M. Kowalski, M. Kastek, M. Szustakowski

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Multispectral screening systems are becoming more popular because of their very interesting properties and applications. One of the most significant applications of multispectral screening systems is prevention of terrorist attacks. There are many kinds of threats and many methods of detection. Visual detection of objects hidden under clothing of a person is one of the most challenging problems of threats detection. There are various solutions of the problem; however, the most effective utilize multispectral surveillance imagers. The development of imaging devices and exploration of new spectral bands is a chance to introduce new equipment for assuring public safety. We investigate the possibility of long lasting detection of potentially dangerous objects covered with various types of clothing. In the article we present the results of comparative studies of passive imaging in three spectrums – visible, infrared and terahertz

Keywords: terahertz, infrared, object detection, screening camera, image processing

Procedia PDF Downloads 329

Authors: Carlos Gordon, Patricio Encalada, Henry Lema, Diego Leon, Dennis Chicaiza

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The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms.

Keywords: autonomous navigation, machine learning, path planning, robotic operative system, open source computer vision library

Procedia PDF Downloads 152

Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

Procedia PDF Downloads 297

Authors: M. Schapira, S. Françoise, F. Maheux, O. Pierre-Duplessix, E. Rabiller, B. Simon, R. Le Gendre

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Pico- and nano-phytoplankton are abundant and ecologically critical components of the autotrophic communities in the pelagic realm. While the role of physical forcing related to tidal cycle, water mass intrusion, nutrient availability, mixing and stratification on microphytoplankton blooms have been widely investigated, these are often overlooked for pico- and nano-phytoplankton especially in estuarine waters. This study investigates changes in abundances and community composition of pico- and nano-phytoplankton under different estuarine tidal conditions in the mouth of the Seine River in relation to nutrient availability, water column stratification and spatially localized currents. Samples were collected each day at high tide, over spring tide to neap tide cycle, from 21 stations homogeneously distributed in the Seine river month in May 2011. Vertical profiles of temperature, salinity and fluorescence were realized at each sampling station. Sub-surface water samples (i.e. 1 m depth) were collected for nutrients (i.e. N, P and Si), phytoplankton biomass (i.e. Chl a) and pico- and nano-phytoplankton enumeration and identification. Pico- and nano-phytoplankton populations were identified and quantified using flow cytometry. Total abundances tend to decrease from spring tide to neap tide. Samples were characterized by high abundances of Synechococcus and Cryptophyceae. The composition of the pico- and nano-phytoplankton varied greatly under the different estuarine tidal conditions. Moreover, at the scale of the river mouth, the pico- and nano-phytoplankton population exhibited patchy distribution patterns that were closely controlled by water mass intrusion from the Sea, freshwater inputs from the Seine River and the geomorphology of the river mouth. This study highlights the importance of physical forcing to the community composition of pico- and nano-phytoplankton that may be critical for the structure of the pelagic food webs in estuarine and adjacent coastal seas.

Keywords: nanophytoplancton, picophytoplankton, physical forcing, river mouth, tidal cycle

Procedia PDF Downloads 331

Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj K. Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

Procedia PDF Downloads 486

Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

Abstract:

This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

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Authors: Carlos Gonzalez-Flores, Ernesto Liñan-García

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A simple and robust approach for developing secure software. A Four Phase methodology consists in developing the non-secure software in phase one, and for the next three phases, one phase for each of the secure developing types (i.e. self-protected software, secure code transformation, and the secure shield). Our methodology requires first the determination and understanding of the type of security level needed for the software. The methodology proposes the use of several teams to accomplish this task. One Software Engineering Developing Team, a Compiler Team, a Specification and Requirements Testing Team, and for each of the secure software developing types: three teams of Secure Software Developing, three teams of Code Breakers, and three teams of Intrusion Analysis. These teams will interact among each other and make decisions to provide a secure software code protected against a required level of intruder.

Keywords: secure software, four phases methodology, software engineering, code breakers, intrusion analysis

Procedia PDF Downloads 375

Authors: Muazzam A. Khan

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In order to avoid dangerous environmental disasters, robots are being recognized as good entrants to step in as human rescuers. Robots has been gaining interest of many researchers in rescue matters especially which are furnished with advanced sensors. In distributed wireless robot system main objective for a rescue system is to track the location of the object continuously. This paper provides a novel idea to track and locate human in disaster area using stereo vision system and ZigBee technology. This system recursively predict and updates 3D coordinates in a robot coordinate camera system of a human which makes the system cost effective. This system is comprised of ZigBee network which has many advantages such as low power consumption, self-healing low data rates and low cost.

Keywords: stereo vision, segmentation, classification, human tracking, ZigBee module

Procedia PDF Downloads 464

Authors: Sharjeel Aslam, Vassil Vassilev, Karim Ouazzane

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Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

Keywords: distributed ontologies, parallel querying, semantic indexing, shared vocabulary, SPARQL

Procedia PDF Downloads 170

Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

Procedia PDF Downloads 190

Authors: G. Punithavathy

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The research proposes a statistical model for the distributed acoustic sensor interrogation units that broadcast a laser pulse into the fiber optics, where interactions within the fiber determine the localized acoustic energy that causes light reflections known as backscatter. The backscattered signal's amplitude and phase can be calculated using explicit equations. The created model makes amplitude signal spectrum and autocorrelation predictions that are confirmed by experimental findings. Phase signal characteristics that are useful for researching optical time domain reflectometry (OTDR) system sensing applications are provided and examined, showing good agreement with the experiment. The experiment was successfully done with the use of Python coding. In this research, we can analyze the entire distributed acoustic sensing (DAS) component parts separately. This model assumes that the fiber is in a static condition, meaning that there is no external force or vibration applied to the cable, that means no external acoustic disturbances present. The backscattered signal consists of a random noise component, which is caused by the intrinsic imperfections of the fiber, and a coherent component, which is due to the laser pulse interacting with the fiber.

Keywords: distributed acoustic sensing, optical fiber devices, optical time domain reflectometry, Rayleigh scattering

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Authors: Sharanya C. H., Pragathi M., Vathsala R. S., Theja K. V., Yashaswini S.

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Identifying counterfeit products have become increasingly important in the product manufacturing industries in recent decades. This current ongoing product issue of counterfeiting has an impact on company sales and profits. To address the aforementioned issue, a functional blockchain technology was implemented, which effectively prevents the product from being counterfeited. By utilizing the blockchain technology, consumers are no longer required to rely on third parties to determine the authenticity of the product being purchased. Blockchain is a distributed database that stores data records known as blocks and several databases known as chains across various networks. Counterfeit products are identified using a QR code reader, and the product's QR code is linked to the blockchain management system. It compares the unique code obtained from the customer to the stored unique code to determine whether or not the product is original.

Keywords: blockchain, ethereum, QR code

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Hussin K. Ragb, Vijayan K. Asari

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

Procedia PDF Downloads 459

Authors: Tatsuya Takino, Izuru Nomura, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera.

Keywords: crater detection, PCA, FPGA, image processing

Procedia PDF Downloads 519

Authors: Ling Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

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The installation of photovoltaic based distributed generation (PVDG) in active distribution system can lead to voltage fluctuation due to the intermittent and unpredictable PVDG output power. This paper presented a method in mitigating the voltage rise by optimally locating and sizing the battery energy storage system (BESS) in PVDG integrated distribution network. The improved firefly algorithm is used to perform optimal placement and sizing. Three objective functions are presented considering the voltage deviation and BESS off-time with state of charge as the constraint. The performance of the proposed method is compared with another optimization method such as the original firefly algorithm and gravitational search algorithm. Simulation results show that the proposed optimum BESS location and size improve the voltage stability.

Keywords: BESS, firefly algorithm, PVDG, voltage fluctuation

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Mohammad Sarchami, Mohsen Zeinalkhani

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Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

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Authors: Somia Bouzid, Messaoud Ramdani

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The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a detection and diagnosis sensor faults method based on a Bottleneck Neural Network (BNN). The BNN approach is used as a statistical process control tool for drinking water distribution (DWD) systems to detect and isolate the sensor faults. Variable reconstruction approach is very useful for sensor fault isolation, this method is validated in simulation on a nonlinear system: actual drinking water distribution system. Several results are presented.

Keywords: fault detection, localization, PCA, NLPCA, auto-associative neural network

Procedia PDF Downloads 362

Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Tao Feng, Wei-Wei Zhang, Chang-Ming Ding

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Cross-site scripting (XSS) is one of the most popular WEB Attacking methods at present, and also one of the most risky web attacks. Because of the population of JavaScript, the scene of the cross site scripting attack is also gradually expanded. However, since the web application developers tend to only focus on functional testing and lack the awareness of the XSS, which has made the on-line web projects exist many XSS vulnerabilities. In this paper, different various techniques of XSS attack are analyzed, and a method automatically to detect it is proposed. It is easy to check the results of vulnerability detection when running it as a plug-in.

Keywords: XSS, no target attack platform, automatic detection，XSS detection

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Authors: Xiaoqing Wang, Jianjian Zong, Li Li, Yanxing Zheng, Jinrong Tong, Mao Zhan

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With the rapid development of network and communication technology, a great deal of data has been generated in different domains of a network. These data show a trend of increasing scale and more complex structure. Therefore, an effective and flexible cross-domain data-sharing system is needed. The Cross-domain Data Sharing System(CDSS) in a net-centric environment is composed of three sub-systems. The data distribution sub-system provides data exchange service through publish-subscribe technology that supports asynchronism and multi-to-multi communication, which adapts to the needs of the dynamic and large-scale distributed computing environment. The access control sub-system adopts Attribute-Based Access Control(ABAC) technology to uniformly model various data attributes such as subject, object, permission and environment, which effectively monitors the activities of users accessing resources and ensures that legitimate users get effective access control rights within a legal time. The cross-domain access security negotiation subsystem automatically determines the access rights between different security domains in the process of interactive disclosure of digital certificates and access control policies through trust policy management and negotiation algorithms, which provides an effective means for cross-domain trust relationship establishment and access control in a distributed environment. The CDSS’s asynchronous,multi-to-multi and loosely-coupled communication features can adapt well to data exchange and sharing in dynamic, distributed and large-scale network environments. Next, we will give CDSS new features to support the mobile computing environment.

Keywords: data sharing, cross-domain, data exchange, publish-subscribe

Procedia PDF Downloads 103