SOURCE LOCATION MECHANISM OF MICROSEISMIC MONITORING USING P-S WAVES AND ITS EFFECT ANALYSIS

Seismic source location is the most fundamental and most important problem in microseismic monitoring. However, only P wave has been mostly applied in the existing microseismic monitoring networks, with low location accuracy and poor stability of location result for the microseismic events occurring beyond monitoring networks. The seismic source location was implemented using P wave and S wave in this study to expand the effective monitoring area of a microseismic monitoring network and improve its location accuracy for microseismic events nearby the monitoring network. Then, the seismic source location mechanism using P-S wave was revealed through theoretical derivation and analysis. Subsequently, the program development and numerical simulation were combined to analyze and compare systematically the location effects of differently distributed monitoring networks, those consisting of different quantities of sensors, and those with S wave contained in some sensors under two circumstances: combination of P wave and S wave and single use of P wave. Results demonstrate that adding S wave in the plane enhances the accuracy control in the radius direction of the monitoring network. After S wave is included, the location accuracy within a certain area beyond the monitoring network is improved considerably, the effective monitoring area of the whole network is expanded, and the unstable location zones using only P wave are eliminated. The location results of differently distributed monitoring networks and the influence laws of the quantity of sensors constituting the networks on the location results are acquired. This study provides evidence for microseismic monitoring to realize accurate and stable location within a larger range. Keywords: seismic source location, P wave and S wave, mechanism, location effect

A Personalized Eccentric Cyber-Physical System Architecture for Smart Healthcare

The development and technological advancement of wireless sensor networks in different fields has been a revolution for mankind. To meet the high-end requirements, the support of the cloud that provides the resources for the application is very much essential. This paper presents an architecture called cloud sense to connect cyber and physical spaces for wireless body area networks with varying high-end workflow at different perspectives. The scalability issue in collecting patient data and processing the data is established using ganglia that is a scalable, distributed monitoring system to support high-performance computing in clusters for the set of input events such as electrocardiogram (ECG), blood pressure (BP), saturation of peripheral oxygen (SPO2), temperature, and skin conductance of the kind of human body parameters. Various parameter metrics have been analyzed based on the equivalent creation of instances. The connectivity mechanism behind the proposed cyber-physical system is unique of its kind; it is exhibited through wireless Internet on a small scale of three remote locations; the system works well with specific network parameter metrics; and the results proved that availability and scalability issues were addressed with numerical analysis.

Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis

This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its “validity” (invalidating any false events caused by voltage transformers saturation) and its “origin”(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas.

A Spiral Distributed Monitoring Method for Steel Rebar Corrosion

This paper proposes a novel spiral-wound, optic-fiber sensor to monitor the corrosion of steel bars. At the same time, the winding parameters, such as winding angle and pitch, were first theoretically deduced. Then, to decrease light loss, a practically distributed sensor wound onto the protective mortar layer was developed by increasing the winding curvature radius. The spiral distributed sensors were experimentally verified for their feasibility. Experimental results showed that the spiral fiber strain depended on the thickness of the protective mortar layer. Furthermore, the spiral distributed strain well reflected the cracking process of concrete. In addition, the concrete cracking time depended on the thickness of the protective concrete layer. Accordingly, this method is feasible for evaluating the initial and final cracking behaviors of concrete structures and provides a sight for steel bar corrosion.

Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes

In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

Optimizing the power quality by reducing harmonic distortion in the power supply network for isolated territories

New technologies of electric power generation, such as generation based on renewable energy sources, distributed generation, make extensive use of modern power electronics. All these non-linear elements generate harmonic components in the supply voltage. This leads to a deterioration in the quality of the supplied energy. In this regard, improving the quality of supplied energy is an urgent task. The article proposes a method of continuous distributed monitoring of the quality of electrical energy in the isolated power systems. This method is based on the use of the PMU infrastructure to continuously obtain information on instantaneous values of currents and voltages. It is proposed to use a smart meter proposed earlier by the authors as a primary measuring instrument for monitoring the level and direction of harmonics in various sections of the power system. This procedure is carried out in the current time mode, and thus there is a continuous adjustment of the power supply system to a mode close to optimal in terms of the content of harmonic components (harmonics) in the supply voltage at the selected points of connection.