Agent-based Anomalies Monitoring in Distributed Systems

In this paper an agent-based approach for anomalies monitoring in distributed systems such as computer networks, or Grid systems is proposed. This approach envisages on-line and off-line monitoring in order to analyze users’ activity. On-line monitoring is carried in real time, and is used to predict user actions. Off-line monitoring is done after the user has ended his work, and is based on the analysis of statistical information obtained during user’s work. In both cases neural networks are used in order to predict user actions and to distinguish normal and anomalous user behavior

Human - computer interface for Doğuş unmanned sea vehicle

Unmanned vehicle systems are becoming increasingly prevalent on the land, in the sea, and in the air. Human-Computer interface design for these systems has a very important role in mission planning. The objective of this work is to design a unmanned sea vehicle and necessary software that can perform off-line path planning, vision management, communication, sensor control, and data management and monitoring of the unmanned sea vehicles

ADVANTAGES OF AN INFORMATION SYSTEM MONITORING AND STOCKS AGRICULTURAL PRICES. CASE STUDY – ROSIM

Abstract agricultural policy in our country is based on information dispersed, especially because there is no centralized monitoring system, who to provide reliable information, while the agricultural and food market is experiencing a general feeling of instability - basically, it consists of channels and a dysfunctional organizational structure, based on communication systems do not operate in real time.. An integrated on-line monitoring of prices of agricultural products is of great interest due to the integration of computer technology (communications and agricultural sciences, based on specific concepts: client / server architecture, the integrated platform software, decision support, database distributed relational distance communication through the web, object oriented programming, mathematical modeling, interactivity etc.)

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

3D Tracking Using Multi-view Based Particle Filters

Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios

Development Of Electrochemical Biosensor Systems For Chlorpyrifos Pesticide Detection

Environmental monitoring systems are of great interest and highly significant in our life since every day we are exposed to many and various dangerous and fatal contaminants. Organophosphate pesticides such as Chlorpyrifos are widely used in agriculture to eliminate plant destroying pests. However, these pesticides may affect the environmental equilibrium unless continuous monitoring on their presence in water, soil, and agricultural products is carried out to protect human health and other living organisms. One of the approaches that can be adopted is to develop an on-line monitoring system. The widely used electronic and computer technology can simplify the development process of acquiring and monitoring system. In addition, the invention of biosensors to detect biologically-based materials such as Chlorpyrifos pesticide in the agricultural sector, glucose level in blood, heavy elements in the drinking water etc., have attracted interest

Real-Time Hydraulic Modelling of a Water Distribution System in Singapore

This paper describes the implementation of a real-time hydraulic model of a water distribution system in Singapore. This on-line system is based on the Integration of real-time hydraulic data with hydraulic computer simulation models and statistical prediction tools. To facilitate this implementation, a network of wireless sensor nodes continuously sample hydraulic data such as pressure and flow rate, transmitting it to cloud-based servers for processing and archiving. Then, data streams from the sensor nodes are integrated into an on-line hydraulic modeling subsystem that is responsible for on-line estimation and prediction of the water distribution system's hydraulic state for a rolling planning horizon of 24 hours ahead. This online hydraulic model is one of the components of the WaterWiSe (Wierless Water Sentinel) platform which is an end-to-end integrated hardware and software system for monitoring, analyzing, and modeling urban water distribution systems in real-time.Singapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

CNC Machine Tool's wear diagnostic and prognostic by using dynamic bayesian networks.

International audienceThe failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657