System Output Research Articles

Evaluation of the Noninvasive Estimated Continuous Cardiac Output System for Pediatric Patients: A Prospective Observational Study.

The estimated continuous cardiac output (esCCO) system is a hemodynamic monitor that uses electrocardiograms and pulse oximeter waves to noninvasively estimate cardiac output. The coefficients for esCCO measurement have been established for adult patients, but the appropriate coefficients for pediatric patients are unclear. Therefore, this study determined esCCO coefficients for pediatric patients and validated the accuracy and tracking ability of a modified esCCO system. An initial study compared cardiac output measurements using transthoracic echocardiography and esCCO in 60 pediatric patients aged <15 years who underwent elective noncardiac surgery. Consequently, the coefficients for the esCCO measurements were redefined for pediatric patients. The main study compared cardiac output measurements between transthoracic echocardiography and modified esCCO in 80 pediatric patients. Measurements were performed pre- and postoperatively, and the accuracy and trending ability of the cardiac output measurements were evaluated using Bland-Altman analysis and a polar plot. The correlation coefficients between the modified esCCO and transthoracic echocardiography were 0.96 and 0.98 in the pre- and postoperative measurements, respectively. In Bland-Altman analysis, the bias (standard deviation [SD]), 95% limits of agreement, and percentage error were 0.03 (0.28), -0.53 to 0.60, and 18% in the preoperative measurement, and -0.04 (0.19), -0.42 to 0.35, and 15% in the postoperative measurement, respectively. The polar plot showed that the cardiac output changes were well tracked, with an angular bias (SD) of 2.9° (6.0°) and radial 95% limits of agreement ranging from -9.2° to 14.9°. Cardiac output measurement by esCCO with modified coefficients for pediatric patients showed high accuracy and tracking ability compared with cardiac output measurement by transthoracic echocardiography. This noninvasive cardiac output measurement could benefit perioperative hemodynamic monitoring in children.

An effective imaging based diagnosis of Solar Photovoltaic System and performance parameters for smart solar power grid

Another of the most attractive power generation sources for residential, corporate, and commercial operations is energy production. Due to this compelling qualities, photovoltaic systems (PV) electricity production have increasingly attracted the attention of researchers and professionals. Nonetheless, the greatest obstacle in generating energy from the sun is the predictable discontinuous output power from photovoltaic (PV) systems, which is largely caused by climate. The power differential of a photovoltaic panels may considerably reduce the financial profit of large scale solar fields. For everyday proper management of electricity supply production, distribution, and storing as well as for judgement on the electricity market, results were consistent prediction of the electricity output of solar PV systems is essential. The 5 subjects mentioned in this article are: The varied faults which might occur in PV panels; internet Photovoltaic panels monitoring; Use of machine learning approaches in Photovoltaic module damage detection; Advantages of fault diagnosis in PV panels and the various motion sensors used for this purpose are discussed. Recommendation for future possible research paths are given in view of the evaluated research.

Evaluation of imaging dose for the cone beam computed tomography system of the ring-shaped radiotherapy unit using the different International Electrotechnical Commission approaches

PurposeDaily imaging is mandatory for the Halcyon and Ethos, which are the new ring-shaped radiotherapy units. This study quantified the imaging dose of the cone beam computed tomography (CBCT) system of the new ring-shaped radiotherapy units using the latest International Electrotechnical Commission (IEC) measurement approach. Furthermore, we investigated the influence of collimation width on the imaging dose and compared the imaging dose values obtained using three IEC measurement approaches. MethodsThe IEC has proposed three approaches for measuring CTDI100: traditional, transition, and the latest measurement approach. In addition to the dose measured on the CTDI phantom, the latest IEC approach uses correction factors measured in free air for CTDI100 measurement. The imaging dose was measured for all scanning protocols using the latest IEC approach. To investigate the influence of the collimation width on the imaging dose, the associated dose metric values were evaluated and compared using different IEC approaches to increase the collimation width (2–28 cm). Furthermore, the imaging system output stability was evaluated by repeated measurements. ResultsIn the repeated measurements, the standard deviation of CTDIw value for the pelvis and head protocol was 0.05 mGy and 0.01 mGy, respectively. And the coefficient of variation was 0.21% and 0.23%, respectively. The measured CTDIw values of the clinical protocols were higher than the vendor-provided value, except for the pelvis protocol. The value of CTDIw changes little with increasing collimation width using the latest IEC approach. The traditional and transition approaches either underestimated or overestimated the value of CTDIw, as measured by the latest IEC approach, with increasing collimation width. ConclusionsThe new ring-shaped radiotherapy units imaging system has good output stability. The CTDIw values measured for clinical scanning protocols generally exceeded the vendor-provided values. Compared with the latest IEC CTDI100 measurement approach, the traditional and transition measurement approaches were more influenced by beam width. For wide CBCT scans, rationalizing the choice of imaging dose measurement approach is important.

Neural Adaptive Intermittent Output Feedback Control for Autonomous Underwater Vehicles With Full-State Quantitative Designs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

Hierarchical Distributed Adaptive Fault-Tolerant Control of Nonlinear Fractional-Order Multiagent Systems With Faults and Periodic Disturbances Using Event-Triggered Communication.

This article presents a distributed fault-tolerant control (FTC) scheme for nonlinear fractional-order (FO) multiagent systems (MASs) with the order lying in (0, 1], such that the proposed control architecture can be directly applied to both FO and integer-order (IO) systems without any modifications. To handle the unexpected actuator faults encountered by the FO MASs, a hierarchical FTC mechanism is developed for each system by constructing an event-triggered distributed FO estimator at the upper layer to estimate the leader system's output via conditionally triggered neighboring information, and an FTC unit at the lower layer to counteract the loss-of-effectiveness faults via Nussbaum function with FO criteria. To further address the unknown nonlinear functions involving bias faults and periodic disturbances, the Fourier series expansion technique is used to construct the input variables of fuzzy neural networks (FNNs), such that the FNNs with dynamically adjusted weight matrices, centers, and widths can be developed for each FO system to act as the learning module. It is shown by FO Lyapunov stability analysis that all follower systems can track the leader system against faults and periodic disturbances. Simulation results on FO systems and hardware-in-the-loop experiment results on IO fixed-wing unmanned aerial vehicles show the extensive feasibility of the developed scheme.

State Space Model Reference Adaptive Control for a Class of Second-Degree Fractional Order Systems with Different Eigenvalues

This study proposes an adaptive control synthesis for a class of second-degree fractional order systems with different eigenvalues in the state-space domain. The proposed fractional order adaptive controller is a generalization of the MRAC controller for the class of scalar fractional order systems. In order to control the fractional order plant, an adaptive state space feedback controller is applied based on the error between the system output and a chosen reference model using a fractional adaptation law to make the fractional order plant track the fractional order reference model. We show that the resulting adaptive regulator is able to stabilize the fractional order second degree system with a satisfying performance. A simulation example illustrating these performance properties is provided along with a comparison with a fractional order sliding mode control (FOSMC) to demonstrate the superiority of the proposed control scheme.

Comparison of entransy loss analysis and the first law of thermodynamics in the optimization of organic Rankine cycle coupled with parabolic trough collector

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss (Ġloss) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, (Ġloss) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the Ġloss). In addition, the optimization parameters included turbine inlet temperature (Ttur), boiler pressure (Pboil), condenser pressure (Pcond), and the temperature of the collector fluid at the boiler outlet (Thf,out). This optimization was performed for the temperature of the collector fluid at the boiler inlet (Thf,in) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and Thf,in, scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1.

Low voltage distribution intelligent control system based on distributed photovoltaic grid connection

Abstract This article mainly studied the use of distributed photovoltaic power generation technology to achieve intelligent control of low-voltage distribution networks. With the rapid development of new energy, distributed photovoltaic power generation technology has become an important means to alleviate the current energy crisis and environmental problems. This article first elaborated on the theory and research status of distributed photovoltaic power generation systems. Based on this, artificial intelligence technology was applied to distributed photovoltaic power generation systems for real-time monitoring to enhance their stable and reliable performance. When the simulated grid voltage was set to 220V, the system output voltage was also 220V, showing a deviation of 0.0%, proving that the system has excellent regulation accuracy at this voltage level. This article is an innovative research that integrates distributed photovoltaic power generation with intelligent control, opening up a practical and feasible technical path for the large-scale utilization of clean energy. Therefore, this study has significant academic value and practical significance.

Logic-Based Fixed-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions.

The fixed-time control problem is investigated for a class of uncertain nonlinear systems subjected to multiple unknown control directions. The control coefficients of nonlinear systems under consideration are time varying and their signs are not required to be identical. To tackle this challenge, a switching mechanism along with a novel dynamic boundary function is proposed. Utilizing the devised dynamic boundary function, adaptive parameters are introduced into the controller to effectively handle system uncertainties. It is proved that the system output converges to a small neighborhood of the origin in fixed time and the boundedness of all system signals is maintained. Finally, two simulation examples are used to show the validity of the presented switching control strategy.

Design and analysis of a novel rotary inertance hydraulic converter with the continuous controlling turntable

The inertance hydraulic converter is an energy-efficient hydraulic component, its output efficiency and dynamic performance are seriously affected by the switched valve. This paper proposed an innovative a rotary inertance hydraulic converter (RIHC), which uses a continuous controlling turntable to determine the ratio of the high pressure oil and the low pressure oil to change the output energy, instead of the discrete controlling of the switched valve. The working principle and mechanical design of the RIHC are described, and mathematical models are established. The effects of the key variables on energy efficiency are evaluated using numerical analysis. The operating performance is analyzed based on simulation, and a control strategy to achieve stable system output is proposed. The results provide a foundation for theoretical research and practical application of the novel RIHC.

Control of Thermal Uniformity in Microwave Heating Process by BPNN and Adaptive Particle Swarm Optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.

New energy urban rail vehicle hybrid energy storage control system

Abstract With the increasing demand for urban transportation and the gradual strengthening of environmental protection awareness, new energy urban rail transportation has gradually become the future trend of urban development. The hybrid energy storage system is one of the key technologies to achieve the goals of energy saving and emission reduction of new urban rail vehicles, improving operation efficiency and comfort. In the traction stage of urban rail vehicles, the energy storage unit composed of lithium batteries and supercapacitors is jointly supplied with the power grid. In the braking stage, the energy storage unit composed of lithium batteries and supercapacitors is a common way for urban rail vehicles to recover energy. This study validates the efficacy of employing fuzzy control methodology to enhance the performance of the energy storage system. The experimental results show that the fuzzy control method can better balance the load distribution between lithium batteries and supercapacitors, thus extending their service life and improving the overall system performance. Based on the SOC energy management strategy of supercapacitors, urban rail vehicle power is allocated to supercapacitors and lithium-ion batteries via a low-pass filter. System power is adjusted based on supercapacitor SOC, determining capacitor charging/discharging states and suppressing system output power.

A Review of Factors Affecting the Efficiency and Output of PV Systems Applied in the Tropical Climate of South-South Nigeria

The increasing adoption of renewable energy for electricity generation has led to a growing application of photovoltaic (PV) systems in residential and commercial settings across Nigeria. This study aims to provide a comprehensive review of the factors affecting the efficiency and output of PV systems applied in the tropical climate of South-South Nigeria. The environmental benefits of PV systems further enhance their appeal as a direct method of converting solar energy into electricity. For remote communities in South-South Nigeria, often disconnected from the national grid, PV systems offer a viable alternative electricity source. However, the efficiency and output of PV systems are significantly influenced by various environmental conditions prevalent in the tropical climate of South-South Nigeria. The methodology employed in this study includes an extensive literature review and analysis of PV system installations in the region. Data was collected from various sources, including journal articles, conference proceedings, and reports, to identify the key factors impacting PV system performance in South-South Nigeria. The findings highlight that high temperatures, humidity levels, frequent dust accumulation, and potential sea salt effects due to coastal proximity are among the critical environmental factors affecting PV system efficiency in this tropical zone. The study concludes that while the region's climate provides advantages for solar energy harvesting, it also presents unique challenges that need to be addressed when implementing PV systems in South-South Nigeria. The authors recommend strategies to mitigate the environmental challenges faced by PV systems in this tropical climate, contributing to the optimization of solar energy utilization in the region.

Integrating Temporal and Feedforward Models for Solar Energy Prediction: LSTM and ANN Hybrid Approaches

The increasing reliance on renewable energy, particularly solar power, necessitates accurate models for predicting energy output to optimize storage and distribution systems. Traditional methods such as Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANNs) offer unique strengths in forecasting photovoltaic (PV) system outputs. LSTM excels in capturing temporal dependencies in time-series data, while ANNs effectively model nonlinear relationships between variables. This study aims to develop and evaluate a hybrid LSTM-ANN model for improving the accuracy of PV energy output predictions, focusing on voltage, power, and irradiance. Using data collected from a solar-powered greenhouse in Talang Kemang, Indonesia, the model was trained and validated. The hybrid model demonstrated significant improvements in prediction accuracy. For voltage, the model achieved a Mean Absolute Error (MAE) of 0.1016 and a Root Mean Squared Error (RMSE) of 0.1417, while irradiance predictions resulted in an MAE of 0.0895 and RMSE of 0.1149. Power predictions also yielded strong results, with an MAE of 0.1506 and RMSE of 0.1954. These results highlight the hybrid LSTM-ANN model's effectiveness in combining temporal and nonlinear data processing capabilities, leading to superior accuracy in predicting PV system outputs. This approach can enhance the reliability of energy forecasting models, enabling better integration of solar power into electrical grids. The model holds promise for broader applications in renewable energy systems, improving their efficiency and sustainability

Optically Delaying a Radio Frequency–Linear Frequency-Modulated (RF-LFM) Pulse Using Kerr Comb Carriers and Off-the-Shelf Concatenation of a Linearly Chirped Fiber Bragg Grating and a Chirped-and-Sampled Fiber Bragg Grating

We demonstrate a low latency delay of a radio frequency (RF)–linear frequency-modulated (LFM) pulse by modulating it onto optical carriers from a Kerr comb and sending the signal through a concatenation of off-the-shelf linearly chirped fiber Bragg gratings (LC-FBGs) and chirped-and-sampled FBG (CS-FBG). We characterize the frequency response and latency of the LC-FBG and CS-FBG. Then, experimentally, the LFM pulse performance is characterized by measuring the peak sidelobe level (PSL) at the output of the tunable delay system. The experiment, performed with an LFM pulse of 1 GHz bandwidth at a 10 GHz center frequency, shows a PSL better than 34.4 dB, attesting to the high quality of the buffer RF transfer function. Thus, the proposed optical memory buffer architecture, utilizing compact devices based on a Kerr comb and FBGs, offers several benefits for delaying LFM pulses, including (i) a larger tunable delay range, (ii) low latency, (iii) wide bandwidth, and (iv) high PSL.

Entropy-Driven Catalytic G-Quadruple Cycle Amplification Integrated with Ligases for Label-Free Detection of Single Nucleotide Polymorphisms.

G-Quadruplex/thioflavin (G4/THT) has become a very promising label-free fluorescent luminescent element for nucleic acid detection due to its good programmability and compatibility. However, the weak fluorescence efficiency of single-molecule G4/THT limits its potential applications. Here, we developed an entropy-driven catalytic (EDC) G4 (EDC-G4) cycle amplification technology as a universal label-free signal amplification and output system by properly programming classical EDC and G4 backbone sequences, preintegrated ligase chain reaction (LCR) for label-free sensitive detection of single nucleotide polymorphisms (SNPs). First, the positive strand LCR enabled specific transduction and preliminary signal amplification from single-base mutation information to single-strand information. Subsequently, the EDC-G4 cycle amplification reaction was activated, accompanied by the production of a large number of G4/THT luminophores to output fluorescent signals. The EDC-G4 system was proposed to address the weak fluorescence of G4/THT and obtain a label-free fluorescence signal amplification. The dual-signal amplification effect enabled the LCR-EDC-G4 detection system to accurately detect mutant target (MT) at concentrations as low as 22.39 fM and specifically identify 0.01% MT in a mixed detection pool. Moreover, the LCR-EDC-G4 system was further demonstrated for its potential application in real biological samples. Therefore, this study not only contributes ideas for the development of label-free fluorescent biosensing strategies but also provides a high-performance and practical SNP detection tool in parallel.

Motion planning in underactuated systems with impulsive phenomenon via dynamic shaping of virtual holonomic constraints

Rhythmic motions are traditionally achieved by developing predetermined paths for the states of the space system to follow. Since these paths are obtained offline, the dynamic behavior fails to adapt to changes in environmental conditions or user command desires. The solution we propose is a new strategy called dynamic shaping, in which the paths are formed online to allow the system to create an orbit with the characteristics we need. Hereupon, this paper focuses on applying this strategy to Dynamics with One Degree of Under-actuation and Impulsive Phenomenon (DSODUIP) to adapt the characteristics of outcomes to be in line with the demands.This research was conducted by leveraging the advantages of virtual holonomic constraints (VHCs) to establish these paths. Therefore, a novel two-level hierarchical control method is designed considering a stability criterion to avoid divergence. At the Low-Level, the controllers stabilize the output of system to follow the VHCs on the system. At the High-Level, the VHCs are modified to shape an orbit with our desired characteristic in the motion. As an illustrative example, the algorithm is implemented to adjust the average angular velocity of a devil stick and the hip velocity of a Three-Link biped robot. Their results vividly demonstrate smooth adjustments and efficient performance in achieving our desired outcomes.

Synthesis of expert matrices in inductive system-analytical research based on fuzzy logic algorithm

The object of research is the process of inductive modeling of complex systems. The studies that were conducted related to the application of algorithms of the fuzzy logic theory were to coordinate the conclusions of top-level experts in system information-analytical research (SIAR) in the tasks of innovative design. The possibilities of constructing elements of expert matrices of results, as well as evaluating the effectiveness of such applications, are defined. Thanks to this, obtaining formal expert evaluations in numerical form became possible. Experimental studies have confirmed that the proposed approach to the application of fuzzy logic algorithms to the construction of matrices of expert evaluations of SIAR results is quite effective and simple to implement. In addition, this approach fits well into the general paradigm of the Group Method of Data Handling (GMDH). In particular, it was established that the possibility of «retraining» such a block without significant efforts of professional experts can have a positive result, as well as have a good effect on the economic and time parameters of the research project. The main calculation formulas for the algorithm for building a fuzzy system using a neural network in a system with two rules are given. The construction of a fuzzy information output system trained on expert evaluations in the Matlab system is shown. As a result, a technologically acceptable standard deviation of 0.28268 mg/l was obtained. It has been established that by accumulating a database (knowledge) and/or using an information monitoring system, it is possible to «additionally train» a fuzzy system periodically or according to the established quality criterion in the program mode, without involving experts in this process. Thus, there are reasons to assert the importance of using a fuzzy system as one of the tools in inductive SIAR procedures

An Experimental Study of Odometer as a Navigation aid for Land Vehicle Application

Strap-down inertial navigation system (SINS) shall provide position, velocity, and orientation information with reference to a pre-defined reference frame. The SINS shall have excellent accuracy over a short duration and is highly self-contained. However, the errors in navigation solutions build up exponentially with time and make the system output very unstable. In order to mitigate these errors, there is a need for a damping mechanism through external aiding sensors. In this article, one such approach is proposed to improve navigation accuracy for land vehicles in the GNSS (Global Navigation Satellite Systems) denied environment. The design and implementation of an odometer are carried out with the use of inductive proximity sensors and a mounting assembly to attach the odometer to one of the non-steering wheels of a vehicle. The odometer gives the pulse-high and pulse-low output with reference to the rotation of the wheel to which it is attached. Vehicle ground velocity is derived through sensed output pulses processed through quadrature decoder circuitry. Odometer measurements along with non-holonomic constraints (NHC) are used for minimizing velocity and position errors in SINS using an extended Kalman Filter (EKF) technique. Field trials are carried out to validate the proposed scheme of hybrid navigation with odometer design and experimental results are presented with a positioning accuracy of 0.05 % of distance travelled (DT).

Capacitive voltage effect at a resistive sintering system container and its electrical model

Abstract The electric current activated/assisted sintering (ECAS) method enables various kinds of materials to be produced much faster and environmentally friendly compared to conventional sample production systems. The main handicap of this system is that the heating regime varies according to the material type even the chemical composition of the same type of material and causes partial melting due to the sudden current flow. Previously, the ECAS output equivalent circuit is modeled as a temperature-dependent resistor in the literature. This study shows that it is insufficient to model the ECAS output consisting of a container and two stiffs as a resistor considering experimental waveforms. We report the discovery of a capacitive effect at the output of the ECAS system that has not been reported before. We have given an equivalent electrical circuit for the ECAS system output and examined the effect’s temperature dependence. The circuit model, which consists of a parallel resistor-capacitor (R p-C) circuit in series with another resistor (R s), is suggested for the container and the stiffs. By using the experimental data, the equivalent circuit parameters are calculated by curve-fitting. The temperature dependence of the equivalent circuit parameters is also examined. Possible explanations for the capacitive effect are given. Such a model and further examining the effect may help design better ECAS systems.