Map-assisted TDOA Localization Enhancement Based On CNN

For signal processing related to localization technologies, non line of sight (NLOS) multipaths have a significant impact on the localization error level. This study proposes a localization correction method based on convolution neural network (CNN), which extracts obstacle features from maps to predict the localization errors caused by NLOS effects. A novel compensation scheme is developed and structured around the localization error in terms of distance and azimuth angle predicted by the CNN. Four prediction tasks are executed over different building distributions within the maps for typical urban scenario, resulting in CNN models with high prediction accuracy. Finally, a thorough comparison of the accuracy performance between the time difference of arrival (TDOA) localization algorithm and the results after the error compensation reveals that, generally, the CNN prediction approach demonstrates great localization error correction performance, improving TDOA accuracy by 75%. It can be observed that the powerful feature extraction capability of CNN can be exploited by processing surrounding maps to predict the localization error distribution, showing great potential for further enhancement of TDOA performance under challenging scenarios with rich multipath propagation.Comment: 6 pages, 8 figures, 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference(IMCEC 2024

Modelling the IEC 61850 and DNP3 Protocol Using OPNET in an Electrical Substation Communication Network

Communication protocols are a composite of supervisory control and data acquisition (SCADA) and they are used by the devices connected on the SCADA network. In this paper the distributed network protocol (DNP3) and International Electrotechnical Commission IEC 61850 communication protocols were modelled in OPNET. The simulation of DNP3 and IEC 61850 communication protocol is done in different scenarios and the traffic behavior is analyzed. The DNP3 protocol is modelled as the medium protocol of communication during the maintenance of a 400kV Transformer at an Electrical Substation. Its network traffic behavior is then analyzed for this operation. The IEC 61850 protocol is then used as a medium of communication in the same Electrical Substation communication network (SCN) when a faulty backbone switch is present. In this scenario the network traffic behavior is again analyzed. The DNP3 simulation during the maintenance of the 400 kV Transformer shows that the model is working since the throughput is consistent without dropped packets at the Substation RTU end and the 400kV Transformer IED end. The IEC 61850 simulation when a faulty backbone switch is present shows that the model is working in this scenario since the throughput is again consistent. When the IEC 61850 protocol is modelled on the SCN, the time delay is 80 μs during normal operation and with a faulty switch the delay is 100 μs for this protocol. This shows that for the IEC 61850 model the time delay increases when there is a faulty backbone switch but not exceedingly since there is a backup switch in the structure. In the DNP3 model during the maintenance of the 400kV Transformer the time delay is approximately 160 μs. The IEC 61850 protocol performs approximately twice as fast as the DNP3 protocol during normal operation in an SCN.University of South AfricaElectrical and Mining Engineerin

Optimal Method for Polarization Selection of Stationary Objects Against the Background of the Earth’s Surface

Within the maximum likelihood method an optimalalgorithm for polarization target selection against the backgroundof interfering signal reflected from the earth’s surface is synthesized. The algorithm contains joint operations of spectralinterference rejection and their polarization compensation bymeans of certain combinations of interchannel subtraction ofsignals of different polarizations. The physical features of theelements of the polarization scattering matrix are investigatedfor the technical implementation of the synthesized algorithm

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems

Continuous monitoring of volatile organic compounds through sensorization. Automatic sampling during pollution/odour/nuisance episodic events

Volatile organic compounds (VOCs) are a highly diverse class of chemical contaminants and between 50 and 300 of them may be found in ambient air. In urbanized areas, VOCs are emitted from industrial activities, as well as from vehicle-related and combustion sources. VOCs outdoors can be detected in a broad range of concentrations, usually varying seasonally. The presence of VOCs at relatively high concentrations has been related to poor air quality, discomfort and odorous nuisances. Additionally, they can have negative health effects to the human organism. Hence, in locations where recurrent sporadic situations of high VOCs levels take place, episodic samples' evaluation is necessary instead of 24 h or longer sampling period's evaluations. The use of commercially available metal oxide semiconductor gas sensors for a continuous monitoring of VOCs concentrations in outdoor air is an interesting and innovative technology. Additionally, the use of these sensors for the activation of a VOCs sampler when episodic events of nuisance/odorous annoyance occur was successfully evaluated. The sensor activation is induced by higher VOCs concentrations from a wide number of VOC chemical families. Two sensor stations, developed at our laboratory and provided with sampling pumps, were located in the municipality of Santa Margarida i els Monjos (Catalunya, Spain) in January 2021. The stations started recording data continuously from two different types of VOCs sensors, temperature, relative humidity and pressure in 1.5-min periods. Automatic VOCs sampling was conducted, using multi-sorbent bed tubes, during the months of June–July when the sensors electronic values exceeded a set point value. Samples were analysed through TD-GC/MS. TVOC concentrations in episode samples ranged between 78-669 and 12–159 µg m-3 in Site 1 and Site 2, respectively. Although TVOC concentrations were not high in all cases, relevant concentrations of chloroform were observed, especially in Site 1, with concentrations ranging from 19 to 159 µg m-3.Postprint (published version

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

Hybrid load balance based on genetic algorithm in cloud environment

Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.Peer ReviewedPostprint (published version