Electrical Monitoring Research Articles

Evaluation of the quality of electrochemical study in the diagnosis of infection of pancreatic cysts

The formation of pancreatic cysts is a serious complication of acute pancreatitis, chronic pancreatitis and pancreatic injuries. Joulemetry is an integral method for evaluating the electrochemical properties of biological objects. To date, this method has not been used in the study of the electrochemical properties of the contents of pancreatic cysts. The purpose of this study was to evaluate the effectiveness of electrochemical analysis in the detection of infection in the contents of necrotic pancreatic cysts. An electrochemical study of contents of necrotic pancreatic cysts carried out on 106 patients. Group 1 included 84 patients without signs of infection of pancreatic cysts; group 2 included 22 patients with signs of infection of pancreatic cysts. The electrochemical study was conducted as follows: 10 ml of the contents of a pancreatic cyst was injected into a liquid ˛ow sensor of a joule meter, where it was exposed to a electrical current for a short period of time. The resulting data was analyzed using a diagnostic research complex. During the study of the electrochemical properties of the contents of postnecrotic pancreatic cysts by using joulemetry, it was revealed that the current work in patients of group 1 ranged from 0.92 to 18.31 mkJ (on average 5.86±5.02 mkJ), in patients of group 2 – from 19.01 to 26.3 mkJ (on average 22.32±1.92 mkJ). When evaluating the quality of the joulemetric study in determining the early signs of in˛ammation of the contents of postnecrotic pancreatic cysts, it was proved that the threshold differential diagnostic value of 19.1 mkJ provides 81.8% sensitivity of the proposed method and 80.7% specificity (AUC = 91.3) with a statistically signi˝cant difference in current work (p < 0.001).

Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes

Substrate-free electrodes are promising dry electrodes for long-term physiological electrical signal monitoring due to their ultra-thinness, conformal contact, and stable skin–electrode impedance. However, the response of substrate-free electrodes to various disturbances during electrocardiogram (ECG) monitoring and the corresponding optimization needs to be investigated. This paper investigates the specific effects of various influencing factors on skin–electrode impedance and ECG during electrocardiogram (ECG) detection. The research utilizes substrate-free poly(3,4-ethylenedioxythiophene)/poly(styrene-sulfonate) (PEDOT:PSS) electrodes. The investigation employs several methods, including skin–electrode impedance comparison, ECG waveform analysis, spectrum analysis, and signal-to-noise ratio (SNR) evaluation. To avoid the impact of physiological state differences in subjects at different times, relevant data were only compared with the same group of experiments conducted in the same period. The results demonstrate that the substrate-free conformal contact PEDOT:PSS electrode has more stable skin–electrode impedance and could obtain a more stable ECG than partial contact electrodes (the SNR of the partial contact and conformal contact electrodes are 1.2768 ± 4.0299 dB and 7.2637 ± 1.4897 dB, respectively). Furthermore, the ECG signal quality of the substrate-free conformal contact PEDOT:PSS electrode was independent of the electrode area and shape (the SNRs of the large, medium, and small electrodes are 4.0447 ± 0.4616 dB, 3.9115 ± 0.5885 dB, and 4.1556 ± 0.5557 dB, respectively; the SNRs of the circular, square, and triangular electrodes are 9.2649 ± 0.6326 dB, 9.2471 ± 0.6806 dB, and 9.1514 ± 0.6875 dB, respectively), showing high signal acquisition capability that is the same as microneedle electrodes and better than fabric electrodes. The results of clothing friction effects show that skin–electrode impedance stability was important for ECG stability, while the impedance value was not (the SNRs of friction and non-friction electrodes are 2.4128 ± 7.0784 dB and 9.2164 ± 0.6696 dB, respectively). Moreover, the skin–electrode impedance maintains stability even at a high breathing frequency, but the ECG signal fluctuates at a high breathing frequency. This experiment demonstrates that even when the skin–electrode impedance remains stable, the ECG signal can still be susceptible to interference from other factors. This study suggests that substrate-free PEDOT:PSS that could form conformal contact with the skin has higher skin–electrode impedance stability and could measure a high ECG signal even with a small electrode area, demonstrating its potential as dry ECG electrodes, but the interference from other physiological electrical signals may require better circuit design.

Fuzzy Learning-Based Electric Measurement Data Circulation Monitoring and Security Risk Anomaly Evaluation

With the circulation of massive electric measurement data, data anomaly caused by security attacks imposes security risks on reliable operation of smart grid. Long short-term memory (LSTM) based data circulation monitoring and security risk anomaly evaluation has been intensively studied. However, some issues remain unsolved, including learning overfitting and large prediction error. In this paper, we investigate fuzzy learning to infer the abnormal level of security risk. In particular, an adaptive grey wolf optimization-LSTM-fuzzy petri network (AGWO-LSTM-FPN) based electrical measurement data circulation monitoring and security risk anomaly evaluation algorithm is proposed. Specifically, AGWO is utilized to optimize LSTM parameter updating and improve traffic prediction accuracy. Furthermore, FPN is combined with multi-dimensional monitoring indicators to enhance anomaly level evaluation. Simulation results illustrate the excellent performance of AGWO-LSTM-FPN.

Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors

Abstract. Using electrochemical gas sensors for quantitative measurements of trace gas components in ambient air introduces several challenges, of which interference, drift and aging of the sensor are the most significant. Frequent and precise calibration as well as thorough characterization of the sensor helps to achieve reliable and repeatable results. We therefore propose the use of a simple, lightweight and inexpensive setup to produce hydrogen calibration gases with precisely known concentrations in ambient air. The hydrogen is produced by electrolysis with electric current monitoring, and the output can be set to any value between ∼ 3 and ∼ 11 µgH2min-1. With a dilution flow of 500 mL min−1, for example, this results in a concentration range from ∼ 70 up to ∼ 240 ppm, but concentrations significantly below or above this range can also be covered with accordingly modified dilution flows. This setup can be used not only for calibration, but also for thorough and long-term characterization of electrochemical gas sensors to evaluate sensitivity, zero voltage and response time over extended periods of time.

Electrical impedance based condition monitoring of machine elements–a systematic review

Condition monitoring of machine elements becomes more important over the last years. Different approaches to detect failures in mechanical components have been developed. All these methods are located at a distance from the point of interest, the observed machine element. This leads to uncertainties in the data, which influences the data quality negatively. Using the electrical impedance for condition monitoring enables in situ measurement with reduced uncertainties and higher data quality. In the last years, research considering this topic was done, but a systematic overview is missing. In this article, a systematic literature research according to the PRISMA approach is fulfilled. The main questions are, what application fields for electrical impedance-based condition monitoring approaches exists and which research gaps are not addressed yet. At the end, 21 articles are categorized in their application fields. Analyzing their content, research questions are identified which have to be addressed in further investigations.

Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety.

Impact damage characterization approach for CFRP pipes via self-sensing

The growing usage of carbon-fiber-reinforced polymers (CFRPs) has necessitated the development of structural health monitoring (SHM) for ensuring their integrity and safe operation. This paper presents a novel technique for monitoring the structural health of 3D CFRP pipe structures subjected to multiple impacts based on real-time electrical resistance monitoring. Damage characteristics such as its location and type were identified using the monitoring data and machine learning techniques. Additionally, finite element analysis was deployed to rationalize the electromechanical behaviors of pipes with varying stacking types and thus different fracture mechanisms. Analysis of the electrical resistance data coincided with the results of the finite element analysis in terms of damage modes and types. Therefore, a simple real-time electrical resistance measurement can detect the damage type without numerical analysis. A damaged section was identified by comparing the intensity of the data. Through machine learning techniques, the timepoints of damage severity demarcation were accurately predicted within error of 10 s and accuracies over 96 %. The machine-learning tools applied are unsupervised, thus reducing the effects of data dependency, labeling, and imbalance, as well as ensuring flexibility and general applications. The proposed technique was able to detect the damage location, severity, and type in real-time; hence, it has various potential applications in structural health monitoring including CFRP materials and is one of the few methods used to assess 3D structure characterization using self-sensing, which can result in mature technology and the optimal operation of CFRP systems.

Thermoelectric-Feedback Nanocomposite Hydrogel for Temperature-Synchronized Monitoring and Regulation in Accurate Photothermal Therapy.

Photothermal therapy (PTT) is a promising approach for tumor ablation and cancer treatment. However, controlling the therapeutic temperature during treatment remains challenging, and imprecise thermal regulation can harm adjacent healthy tissues, reduce therapeutic accuracy, and promote the thermotolerance of cellular phenotypes, potentially leading to tumor invasion and recurrence. Although existing methods provide basic temperature control by adjusting irradiation power and photothermal agent dosing, they lack real-time temperature monitoring and feedback control capabilities, underscoring the urgent need for more integrated and precise PTT systems. In this context, an innovative photothermoelectric (PTE) cobalt-infused chitosan (CS) nanocomposite hydrogel (PTE-Co@CS) is developed for precise temperature-regulated PTT, exhibiting desirable mechanical properties and exceptional biocompatibility. Enhanced by embedded nanoparticles, PTE-Co@CS demonstrates superior photothermal conversion efficiency compared with existing methods, while also featuring thermoelectric responsiveness and increased sensitivity to photostimuli. Its advantageous PTE response characteristics ensure a linear correlation between temperature shifts and resistance changes (e.g., R2 = 0.99919 at 0.5Wcm⁻2), enabling synchronized qualitative and quantitative control of PTT temperature through electrical signal monitoring. This allows for real-time monitoring and regulation during PTT, effectively addressing the issue of uncontrollable temperatures and improving therapeutic efficacy.

A Comprehensive IoT Solution for Electrical Energy Consumption Monitoring: System Development Using NodeMCU ESP32, SCT-013, ZMPT101B, and Blynk Platform

A Comprehensive IoT Solution for Electrical Energy Consumption Monitoring: System Development Using NodeMCU ESP32, SCT-013, ZMPT101B, and Blynk Platform

Stability Study of Synthetic Diamond Using a Thermally Controlled Biological Environment: Application towards Long-Lasting Neural Prostheses.

This paper demonstrates, for the first time, the stability of synthetic diamond as a passive layer within neural implants. Leveraging the exceptional biocompatibility of intrinsic nanocrystalline diamond, a comprehensive review of material aging analysis in the context of in-vivo implants is provided. This work is based on electric impedance monitoring through the formulation of an analytical model that scrutinizes essential parameters such as the deposited metal resistivity, insulation between conductors, changes in electrode geometry, and leakage currents. The evolution of these parameters takes place over an equivalent period of approximately 10 years. The analytical model, focusing on a fractional capacitor, provides nuanced insights into the surface conductivity variation. A comparative study is performed between a classical polymer material (SU8) and synthetic diamond. Samples subjected to dynamic impedance analysis reveal distinctive patterns over time, characterized by their physical degradation. The results highlight the very high stability of diamond, suggesting promise for the electrode's enduring viability. To support this analysis, microscopic and optical measurements conclude the paper and confirm the high stability of diamond and its strong potential as a material for neural implants with long-life use.

1 × 4 nano-pixel power splitter designed using an electric profile correlation coefficient monitor method

The power splitter is one of the fundamental elements in a photonic IC. Among various power splitter structures, nano-pixel-based ones have attracted attention in recent years because of their flexible design capability. As there is no rigid design rule in nano-pixel layout, typically, inverse design algorithms are employed to realize the target function. In inverse design, general criteria are needed during the design process, and one typical criterion is the excess loss, however, there are no specific criteria for mode field evaluation. When designing a 1 × N power splitter, considering the power balance among the N output ports is crucial, therefore, we propose a correlation coefficient method to evaluate the output electric field profile. In this study, we adopted vector criteria including both an excess loss and correlation coefficient method during the inverse design process. As a result, the simulated results show all four 0th order modes and the output power to be 24.490%, 24.494%, 24.494%, and 24.490% with a low excess loss of 0.1 dB.

IoT based Breaking Control System for EV Vehicle and Monitoring System

It is imperative that we transition to the use of electric vehicles because these vehicles are the next generation of transportation. Overcharging or over discharging can damage the batteries in electric vehicles, therefore it's important for them to correctly assess the charge status to keep them working for longer and protect the components they power. In this article, we offer an inexpensive, IoT-based system for electric vehicle battery management and monitoring. A user-friendly application that leverages the power of the Internet of Things is at the heart of its design. Important information about the battery's status, such as its capacity and the current entering and leaving it, is provided by the system. You might be able to observe these changes unfold in real time. This system is built using the Blynk Internet of Things platform, the Blynk mobile app, and an ESP32 microcontroller. Keywords— EV Vehicle, Breaking Control System, Monitoring System.

Electrical Energy Monitoring and Analysis System at Home using IoT-based Prophet Algorithm

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System were developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home.

Concerns on digital health from a cardiac implantable electrical device remote monitoring clinic perspective: results from an international survey

Concerns on digital health from a cardiac implantable electrical device remote monitoring clinic perspective: results from an international survey

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

Monitoring the Use of Electrical Energy and Water in Homes Based on IoT

Increased awareness of energy efficiency and sustainable resource management has encouraged the development of intelligent electrical energy and water usage monitoring systems. In this context, the Internet of Things (IoT) has become a promising solution for connecting household devices to the internet, enabling users to monitor and control electricity and water consumption in real-time. This research aims to design and implement an IoT-based monitoring system for electrical energy and water usage in homes. This system uses a powerful Arduino ESP32 microcontroller and can connect to the internet via WiFi. This microcontroller acts as a control center that collects data from sensors installed in the house. Based on these problems, this research was carried out, with the aim of creating a prototype to record electrical power loads and display the amount of electricity consumption in rupiah levels to monitor the use of electrical power loads and convert them into rupiah. To conduct this research, the author used observational research methodology or field study and literature study. In this research, it is necessary to pay attention to understanding how to think and how to carry out the results of thinking according to scientific steps.

FFF print defect characterization through in-situ electrical resistance monitoring

Fused filament fabrication is a popular fabrication technique. Currently there is a need for in-situ monitoring modalities to gather real-time information on prints, both for quality control and closed-loop control. Despite current advancements, effective and affordable in-situ monitoring techniques for non-destructive defect detection of voids and bonding quality are still limited. This work demonstrates in-situ monitoring of fused filament fabrication through electrical resistance measurements as an alternative to thermal and optical methods. A new, easy-to-implement setup is demonstrated which measures the electrical resistance of a conductively doped filament between the nozzle and single or multi-electrodes on the bed. Defects can be located in an unprecedented way with the use of encoded axes in combination with the observed resistance variations throughout the part. A model of the anisotropic electrical conduction is used to interpret the measurements, which matches well with the data. Warping, inter-layer adhesion, under-extrusion and overhang sagging print defects can be observed in the measurements of parts with a complex geometry, which would be difficult to measure otherwise. Altogether in-situ electrical resistance monitoring offers a tool for optimising prints by online studying the influence of the print parameters for quality assessment and it opens up possibilities for closed-loop control.

Research on the prediction method of porcelain insulator pollution degree based on electric field monitoring

The online monitoring of insulator surface pollution states is crucial for predicting/preventing pollution flashover accidents. This paper conducts artificial pollution tests on porcelain insulators at five pollution levels to obtain the electric field signal during the flashover process. Subsequently, the time-frequency performance of the electric field signal waveform at various discharge stages is discussed in detail. Finally, a method for predicting pollution degrees by combining back propagation (BP) neural networks and non-contact electric field signal monitoring is proposed and validated. Research results indicate that during the pollution flashover process of the porcelain insulator, the peak value of the electric field signal shows an upward trend in sections. As the equivalent salt deposit density (ESDD) increases, the curves of electric field values rise. The development of dry-band arcing clearly distorts the electric field signal waveform at its crest and trough parts, increasing the total harmonic distortion (THD) rate. The THD can reach 100–130 % when closing to flashover, with the third harmonic acting as the main component. By taking kmax (the rising rate of the E-field signal amplitude), g (the ratio of Emax to Erms), K3 (the third harmonic coefficient), and THD as inputs to the BP neural network model, an ESDD prediction with a relative error lower than 10.63 % is realized. This research can offer novel technical support for predicting pollution degrees.

4D Electrical Resistivity Imaging of Stress Perturbations Induced During High‐Pressure Shear Stimulation Tests

AbstractFluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory‐scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time‐lapse 3‐dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear‐stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation.

STATISTICAL ESTIMATES OF VARIABLES DETERMINING THE INTERVAL OF ELECTRIC POWER MONITORING

It is demonstrated that digital processing of information on electricity consumption is possible when transforming the studied process into a discrete form. The correct choice of the monitoring interval allows for the accurate reconstruction of the values of the studied function in the intervals between measurements. It is shown that when organizing the monitoring of average power, it is necessary to select a discretization interval that provides adequate representation of the current process. The proposed procedure for choosing the interval of electric power monitoring is based on determining the duration of the deviation of the random function of electric power change, subject to certain constraints. The justified number of monitoring studies is established to determine the optimal interval of electric power monitoring. It is shown that the most rational method of processing experimental data will be the method of joint estimation for correlation and standard deviation.