Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

Application of Intelligent Sensor Algorithm in Student Management Information Fusion

In order to improve the effectiveness of college student management and promote the integration of college student management information, this paper applies intelligent sensor algorithms to student management. Moreover, this paper combines uncertainty theory with multisensor data fusion technology to establish a complete set of multisensor data processing tools for student information and provides a complete mathematical theoretical framework for the principles of student management information fusion. In addition, in view of the problem of comparing a large number of mixed data of information sources, it is necessary to transfer the information fragments obtained by each sensor to a common set so that the information fragments expressed in different sets can be integrated. Finally, this paper constructs an intelligent student management model and conducts research in combination with simulation experiments. Through simulation research, it can be known that the method proposed in this paper can effectively improve the effect of student management.

Water Dragon Boat Training Monitoring System Based on Multisensor Data Fusion Technology

With the development of science and technology, a variety of electronic devices have entered our lives, making our lives more intelligent and making our work more effective. This article is aimed at studying the application of multisensor data fusion technology to the water dragon boat training monitoring system. In that case, we can analyze the various physical indicators of dragon boat athletes based on the data reflected by these sensors, when they can reach their physical limits and can perform in the best state to obtain the best results. The sensor is used to decompose the relevant data of each part of the athlete’s limbs. This step is based on the image and understands the maximum value of the data to adjust the training goal. This article proposes some data fusion algorithms, using Kalman filter method, Bayesian estimation method, and DS evidence theory algorithm to compare data fusion systems, through the comparison to find the best fusion accuracy, and then get the most suitable method is then applied to this water dragon boat monitoring system to enhance the training efficiency of dragon boat athletes. The experimental results in this paper show that when the value of the parameter increases from 0.97 to 2.5, the average classification accuracy of the k -NN classifier decreases from 0.97 to 0.4, and the accuracy of the fusion results of the three fusion rules is also reduced correspondingly, but in this paper proposed, RP fusion rule still has better performance than the other two fusion rules. When the classifier is k -NN, the three fusion rules increase with the number of sensors, and the accuracy of the fusion results is correspondingly improved. However, the final fusion accuracy obtained by the RP fusion rule proposed in this paper is always better than NB integration rules, and WMV integration rules are higher. Through these analyses, a training program that is most suitable for dragon boat athletes can be worked out, so that the athletes will not be useless. Multisensor data fusion technology brings great convenience to water dragon boat training and can provide more reasonable and accurate data to explore a practical way on the basis of ensuring the safety of personnel.

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

An Interval Temporal Logic for Time Series Specification and Data Integration

The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.