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This paper studies the distribution network impacts of frequency containment services derived from domestic-scale devices. Risk metrics considering the likelihood and severity of violations are proposed, given uncertainty in the location of these devices. A novel linearization approach is proposed to enable detailed simulations of large-scale networks, capturing MV–LV coupling in European-style networks of over 100 000 nodes. The approach combines a novel factorization of the power flow Jacobian matrix and an efficient linearization update step. The first of these innovations improves the scalability and practicality of the linearization, reducing the memory and computational requirements by as much as twenty times, whilst the latter reduces the median linearization error by at least 50% in all networks studied. It is demonstrated that rapid voltage change (RVC) and overvoltage constraints could limit the uptake of these devices to just a fraction of one percent of customers if the response is not managed. Sensitivity analysis demonstrates that both the likelihood and severity of constraint violations increases rapidly with the size of the devices used for frequency response.

This paper demonstrates the feasibility of employing mode localized mass sensing in coupled MEMS resonators for ultra-fine particulate detection applications. The potential benefits of this approach include higher parametric sensitivity and intrinsic common mode rejection as compared to the resonant frequency shift-based detection approach. Weakly coupled MEMS resonators demonstrated in this paper demonstrate sensitivity to fine particulates and a significant improvement in mass sensitivity for particulate quantification by employing an amplitude ratio shift output metric.

Temperature measurement of an isopropanol-immersed microbottle resonator is demonstrated. The theoretical analysis indicates that the resonance wavelength blue shifts as the increase of temperature, and the temperature sensitivity increases with the decrease of resonator dimension, which are also demonstrated by experimental results. The temperature sensitivity enhancement of up to 4.6 times relative to the sensor in the air is achieved, which could be benefit from the high thermal refraction coefficient of isopropanol. Furthermore, a robust package has also been proposed to make the present sensor more practically.

Deep neural networks (DNNs) are widely used for fault classification using partial discharges (PDs) to evaluate various electrical apparatuses and achieve high classification accuracy pertaining to trained PD faults. However, there is a risk of false alarm in the case of untrained PD faults because it is difficult for DNNs to predict data that were not included in the training process. In this paper, we research classification problems of unknown classes using PDs in gas-insulated switchgears (GISs) and propose a deep ensemble model to obtain the confidence of output probability and determine thresholds to detect unknown fault classes. The proposed model was verified by real-world phase-resolved PD (PRPD) experiments using online ultra-high frequency (UHF) PD measurement systems. The experimental results show that the proposed model achieves better unknown detection performance for the untrained PD faults and retains the classification performance for the trained PD faults.

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem’s complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

This work proposes a learning model to predict the outcome of electrical defibrillation from ECG signals in ventricular fibrillation (VF) periods, which is a lethal situation happening when a patient is suffering cardiac arrest. An animal experiment of rats is conducted to obtain the ECG signals and necessary information for this study. This proposed model only extracts one feature from the ECG signals and enjoys low computational complexity at both training and testing stages. The statistics of this extracted single feature is further analyzed, and mathematical closed-form formulas for several interesting performance indices including the sensitivity, specificity, accuracy, precision and Area Under the Curve (AUC) are obtained to gain more insights of the proposed system. Moreover, the extracted feature can be treated as a linear combination of individual frequency components of the ECG signal, where the combining coefficients of the linear combination may show informative clinical inference. Frequencies corresponding to large trained combining coefficients imply that they contribute more in distinguishing the defibrillation outcome, and vice versa. As a result, important frequencies of the ECG signals can be identified and insignificant frequencies can also be filtered out by the proposed training. Simulation results corroborate the analytical results, and show that the proposed scheme greatly outperforms several competitive learning models and traditional methods in terms of testing accuracy and computational complexity.

This article proposes a voltage-to-frequency converter (VFC) design using unipolar metal-oxide thin film transistor (TFT) technology. The proposed VFC has an integrator and Schmitt trigger based structure. This structure has the advantages of constant power consumption, full-swing output, and low circuit complexity compared to the early designs. To verify the proposed design, SmartSpice simulation based on a Rensselaer Polytechnic Institute (RPI) model whose parameters are turned to fix the measured characteristics of our indium tin oxide- (ITO-) stabilized ZnO TFTs is carried out. The ITO-stabilized ZnO TFT has a single-gate staggered structure. Its typical field-effect mobility, threshold voltage, on/off current ratio, and subthreshold-slope are 14.5 cm2/Vs, 0.5 V, $1.2{\mathrm {\times }} 10^{8}$ , and 77 mV/decade, respectively. Simulation results show that the proposed VFC has maximum linearity error less than 1.8%, tuning sensitivity about 1 kHz/V, and power consumption less than $130~{\mu }\text{W}$ even under device variations. These performances are competitive compared to the state-of-the-arts. When configured to an analog-to-digital converter (ADC), 6 bit resolution and 14 S/s sampling rate can be realized. These results indicate that the VFC can find potential applications in flexible large-area low-voltage sensor interfaces for quasi-static signals.

In the light of Brazilian energy regulatory context, cluster strategies are required to classify groups of substations for voltage sag purposes. Tuning cluster algorithms is not a trivial task, due to the fact that these methods are sensitive to small errors. Therefore, this study proposes a new methodology based on principal components analysis (PCA), attribute agreement and analysis of covariance to verify the level of consistency and sensitivity of the linkage methods in the cluster formation for voltage sag studies. In order to prove this methodology, real data from power quality indices of distribution substations are used. Four distinct scenarios with disturbances are evaluated. PCA is applied for dimensionality reduction of the data. Then, grouping is performed for eight different linkage methods and agreement analysis is applied. Ward method was the only one that presented 100% consistency in all scenarios, considered as the most robust method whereas k-means showed consistency of 94.11%, with inversion of the clusters. However, when evaluating their groupings, it was found that k-means was unable to adequately separate the groups for this dataset. Finally, the proposed methodology is adequate for choose cluster methods for extensive data and it can be extended to applications in different areas.

A multi-channel high-throughput chiral molecule determination method based on weak value amplification (WVA) is proposed for the first time. This method can be used for high-throughput and high-precision determination of chiral molecule. Through the design of multi-channel weak measurement system, we realized the real-time, high-throughput, high-sensitivity and high-resolution detection of chiral molecules by selecting proper measurement states. In this work, we have realized a sensitivity of 11.8 nm/° and a resolution of 3.13 × 10-4° for measuring optical rotation. Moreover, the proposed method is successfully applied for detection of lactose content in milk samples. Furthermore, the system has the advantages of simple and robust real-time detection settings, strong stability, high measurement efficiency and good scalability. This method has important significance in clinical diagnosis, food safety and biological system.

With the rapid development of power-electronics-enabled power systems, the new converter-based generators are deteriorating the small-signal stability of the power system. Although the numerical differentiation method has been widely used for approximately calculating the eigenvalue sensitivities, its accuracy has not been carefully investigated. Besides, the ele-ment-based formulation for computing closed-form eigenvalue sensitivities has not been used in any commercial software due to the average efficiency, complicated formulation, and error-prone characteristics. Based on the matrix calculus, this paper proposes an easily manipulated formulation of the closed-form eigenvalue sensitivities with respect to the power generation. The distinguishing feature of the formulation is that all the formulas consist of vector and matrix operations, which can be performed by developed numerical algorithms to take full advantages of architectural features of the modern computer. The tests on WSCC 3-machine 9-bus system, New England to-ma-chine 39-bus system, and IEEE 54-machine 118-bus system show that the accuracy of the proposed formulation is superior to the numerical differentiation method and the efficiency is also greatly improved compared to the element-based closed-form formulation. The proposed formulation will be helpful to perform a more accurate and faster stability analysis of a power grid with converter-based devices.

For antenna modeling and optimization design, high-fidelity full-wave electromagnetic simulation software can generally be used to obtain training samples. However, this process takes a long time. To solve this problem, a two-stage Gaussian process (GP) considering electromagnetic sensitivity information is proposed. Based on the coarse grid and fine grid of electromagnetic simulation software, the first stage learns the mapping relationships of antenna performance and sensitivity of each parameter, simultaneously. In the second stage, the accurate surrogates are established based on the mapping relationships above obtained. Because the method in this study takes sensitivity information into consideration during the modeling process, it can better reflect the mapping relationship between antenna input and output, which can develop a more accurate surrogate model. The proposed two-stage GP surrogate model considering sensitivity information can significantly reduce the demand of high-fidelity training samples for modeling, and greatly save the simulation time of calling electromagnetic simulation software. Therefore, this method is more suitable for the problems of insufficient electromagnetic simulation samples. The proposed approach is evaluated by modeling and optimization of an inverted F antenna and an ultra-wideband planar monopole antenna. The results show that the method has high modeling accuracy with limited training samples given by high-fidelity electromagnetic simulation, which further verify its effectiveness and efficiency.

This research paper represents the analysis and simulation of semi-active suspension using non-linear modeling of the Magneto-Rheological (MR) suspension with consideration of the hysteresis behavior for a quarter car model. The research is based on the assumption that each wheel experiences the same disturbance excitation. Hysteresis is analyzed using Bingham, Dahl’s, and Bouc-Wen models. This research focuses on simulation of passive, Bingham, Dahl, and Bouc-Wen models and analysis for the five road profiles. The desired damping force determines the optimum working conditions based on optimized critical design parameters. An integrated approach towards the numeric design optimization by computational methods has been used to find the optimum working conditions. The critical parameters of MR damper are determined, and multi-objective optimization is performed considering the pole length, piston radius, gap thickness, piston internal radius, piston velocity and coil current. Sensitivity analysis also is performed to identify the sensitive parameter in the MR damper geometry towards the damping force. Analysis shows that gap thickness is the most sensitive geometric parameter of the MR damper. Furthermore, the comparative study of the models for the highest comfort with less overshoot and settling time are executed. The Bouc-Wen model is 36.91% more accurate than passive suspension in terms of damping force requirements, has a 26.16% less overshoot, and 88.31% less settling time. The simulation of the Bouc-Wen model yields the damping force requirement to be 2150N which is 91% of the analytical results.

As one of the most essential parts of the military operation, the ability of weapon systems to achieve the operation mission is vital to the success of a military operation. Focusing on the problem of weapon system effectiveness assessment (WSEA) under interval uncertainty, this paper proposed WSEA method that utilized the interval-valued evidential reasoning (ER) algorithm, the analytical hierarchy process (AHP), and the two-grade interval ranking method to properly deal with interval uncertainty in the assessment as well as provide reliable assessment results. Firstly, the AHP is used to determine the weight of different attributes of the weapon system based on experts’ knowledge, which could enhance the subjectiveness of the assessment result. Then, the interval-valued ER algorithm is applied to combine the assessment of different attributes under interval uncertainty and provide the overall assessment result. Finally, the two-grade interval ranking method is utilized to compare and rank the assessment results of different weapons. Case study shows that the proposed method could provide reliable and accurate results for weapon effectiveness assessment. Comparison results and sensitivity analysis results further confirm that the proposed method is not only as effective as existing methods with precise data, but also has the ability to provide reliable results under uncertainty. In conclusion, by utilizing the interval-valued ER algorithm, the AHP and the two-grade interval ranking method, the proposed method provides a novel and effective way for weapon effectiveness assessment under uncertainty, especially interval uncertainty.

The phenomenal growth of the Electric Vehicle (EV) technology demands efficient and intelligent control strategies for the propulsion system. In this work, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively. The controller parameters and the fuzzy logic controller’s membership functions are tuned and updated in real-time using the multi-objective ACO technique. The proposed controller’s speed tracking performance is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform. The proposed controller outperforms the ACO-based fuzzy integer-order PID (IOPID), FOPID, and traditional IOPID controllers. The sensitivity analysis confirms the robustness of the proposed controller for varying parameters of the EV model. The stabilization of EV speed in the presence of external disturbance is also confirmed. In the proposed work, an attempt is made to analyze the system’s stability using Matignon’s theorem, considering the linearized EV model. The proposed controller gives optimum speed tracking performance compared to the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) based fuzzy FOPID controllers. Additionally, the optimized fuzzy FOPID controller is realized using a second-generation current conveyor with extra inputs (EX-CCII) and fractional-order capacitors with electronic tunability. The controller circuit’s performance evaluation is carried out in the Cadence Analog Design Environment using GPDK 180 nm CMOS process.

Multivariate memory-type control charts that use information from both the current and previous process observations have been proposed. They are designed to detect shifts in both upper and downward directions with equal precision when monitoring the process mean vector. The absence of directional sensitivity can limit the charts' application, particularly when users are interested in detecting variations in one direction than the other. This article proposes one-sided and two one-sided multivariate control charts for monitoring shifts in the process mean vector. The proposed charts are presented in the form of the multivariate homogeneously weighted moving average approach that yields efficient detection of shifts in the mean vector. We provide simulation studies under different shift sizes in the process mean vector and evaluate the performance of the proposed charts in terms of their run length properties. We compare the average run length (ARL) results of the charts with the conventional charts as well as the one-sided and two one-sided multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) charts. Our simulation results show that the proposed charts outperform the existing charts used for the same purpose, particularly when interest lies in detecting small shifts in the mean vector. We show how the charts can be designed to be robust to non-normal distributions and give a step-by-step implementation efficient application of the charts when their parameters are unknown and need to be estimated. Finally, an illustrative example is provided to show the application of the proposed charts. 2013 IEEE. This work was supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM) under Grant SB191043. Scopus

The working life evaluation of metal oxide varistor (MOV) is always expected by MOV manufacturers and users. In Oct. 2019, the International Electrotechnical Commission (IEC) SC37B established a project team PT 61643–333 led by the author of this paper to draft a technical report about MOV life assessment. As an early researching achievement of PT 61643-333, this paper provides an evaluation method for the impulse life of MOV with the median rank regression (MRR) method based on the Weibull distribution, which provides an effective means for evaluating the impulse life of MOV. Firstly, the theory of Weibull distribution and impulse life characteristics of MOV are introduced. Then the evaluation method and procedure of MOV impulse life is derived from the combination of the MRR method and the impulse characteristics of MOV. Finally, an application example of the proposed evaluation method is given with the test results from real MOV samples. By establishing three basic impulse life characteristics equations, the life evaluation of MOV under arbitrary impulse current can be achieved. In addition, the fitting validity of six groups of samples is checked to prove the effectiveness of the proposed parameter model. And further sensitivity analysis of the variables has been done to examine the effect of changes in the values of the parameters on the propose model.

High-resolution radar imaging has to be accomplished within the constraints of aperture size, frequency, and acquisition time. To achieve effective and reliable imaging, we introduce a compressed sensing (CS) technology to spread spectrum through-the-wall radar imaging. Using the sparseness of the spread spectrum through-the-wall radar echo in the correlation domain and the sparsity of the target information in the imaging region, the feasibility of CS technology for reducing the number of observation points and the number of samples per observation point is verified. Moreover, considering the high sensitivity of CS technology to clutter, receiver cancellation is adopted to remove the strong clutter information present in echo signals. The results of numerical simulations show that CS technology can provide very efficient sampling, thereby significantly reducing the amount of data required. Compared to back-projection imaging based on traditional sampling technology, the proposed CS technique can obtain almost the same satisfactory resolution and quality imaging results using only 0.72% of the total data. This will reduce the hardware costs and significantly shorten the data-acquisition and data-transfer times.

As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.

Many systems are usually subjected to the combined effects of degradation and random shocks at the same time. Their failures are the competitive result of soft failure caused by degradation and hard failure caused by shocks. For operating machinery, wear failure is the main failure mechanism, and the machine is also subject to shock during the wear process. This paper proposes a new generalized surface wear model in combination with dependent competing failure processes; this proposed model is different from the other wear model with independent wear increments. As a typical mechanical structure, worm gears and worms are subjected to the combined effect of two failure mechanisms: soft failure caused by performance degradation, and hard failure caused by shocks. Meanwhile, it is necessary to consider the competitiveness and correlation of these two failure mechanisms. The interdependent competitive failure model is used to describe the failure of operating machinery. In this study, the extended Archard model is used to calculate the wear depth of the tooth surface, and the wear model is established through the wear threshold. The relationship between tooth surface wear depth and duty cycle, sliding speed, and contact stress is analyzed. An iterative algorithm is used to derive a nonlinear time-varying wear degradation model considering contact stress and sliding velocity. Comparing the calculation results with the Monte Carlo simulation method, the model has high accuracy and describes the mechanism of soft and hard failures, and the mutual dependence of the two failure mechanisms has an important effect on reliability. Numerical examples are presented to illustrate the developed reliability models, along with sensitivity analysis.

Precision time protocol (PTP) is one of the most widely used protocols for clock synchronization in packet-switched networks, on which, among others, the transaction synchronization of the stock markets relies. PTP was not standardized with security as a core requirement and is therefore vulnerable and attractive to manifold kinds of malicious attacks, such as time-delay attacks (TDAs). TDAs, in short, corrupt the exchange of timestamped messages and thus cause an incorrect synchronization process. The annex P of the IEEE 1588-2019 standard has defined a number of security mechanisms for clock synchronization, but, however, none of these can protect a PTP-based system completely against TDAs. In this work, we enhance existing approaches by introducing a so-called observation task and analytically deriving attack parameters of an ongoing TDA. Following the recommendation of the annex P of the IEEE 1588-2019 standard, these attack parameters can serve as an additional input for intrusion detection systems and allow for a more reliable and sensitive detection of TDAs. In a comprehensive evaluation, we experimentally investigate the impact different attack parameter combinations can have on a system.

The traditional fiber Bragg grating (FBG) acceleration has a low operating frequency, which limits its application in transmission line galloping monitoring. We develop a slotting optimization technique to realize the accurate tracking of transmission line galloping in this work. Furthermore, this technique is applied to the development of a high-frequency FBG acceleration sensor. The width, position, and length of the slot are optimized to minimize the slot’s effect on the FBG acceleration sensor. The vibration experiment in our work studies the frequency response and the sensing property of this FBG acceleration sensor. The experimental results indicate that our high-frequency FBG acceleration sensor has not only high sensitivity but also accurate monitoring results.

This study introduces an interferometric synthetic aperture radar (InSAR) altimeter for sea-level elevation measurement. To evaluate the system capability and demonstrate that the InSAR altimeter can meet the application requirements of sea-surface elevation measurement, the main factors affecting relative elevation accuracy are analyzed. The mathematical expressions for each elevation measurement parameter and sea-surface elevation are derived along with the error transfer coefficients between each elevation measurement parameter and relative sea-surface elevation accuracy. Using the single-control variable method, the sensitivity of each elevation measurement parameter to relative elevation accuracy is obtained. By introducing the parameter values into the derivation formula of theoretical analysis, theoretical results are obtained. The theoretical results are compared with the simulation results, which helps to verify the validity of the error analysis. Results reveal that the interference phase error is the main factor affecting the accuracy of relative ocean height measurements. This work could significantly guide the subsequent design of the InSAR altimeter system.

An effective approach for general measurement based on weak value amplification using non-Gaussian broadband light sources is demonstrated. This scheme can reduce the difficulty of preparing the measurement apparatus and correcting the systematic error caused by the imperfection of device's wave function, thus making the weak-value amplification scheme more convenient and robust for practical field applications. The influence of several common noise sources in a general application based on weak value amplification is analyzed theoretically and examined experimentally. A purely imaginary weak-value phase measurement system is considered for the noise model verification experimentally. In this we show how to optimize the precision of measurement in a unique solution that takes into account the interplay between precision and uncertainty, and explicate the ramifications of such a compromising and pragmatic approach.

This article describes the preparation of a clinical study on a connected medical device dedicated to facilitate the prevention of lymphedema for patients treated for breast cancer after axillary lymph node dissection (ALND), and to alert them of any manifestation or resurgence in order to prevent its aggravation. A simulator of the entire physical process called digital twin has been developed for this purpose, in a hardware-in-the-loop framework. Statistical calibration of its input parameters, by stochastic inversion and using sensitivity studies, led to establish one or more measurement protocols allowing to capture the signal on a mobile device (phone or tablet) and to detect signal breaks that are physically significant. The measured signal makes it possible to report quickly on the worsening of the patient’s condition and to warn the therapists within a very reasonable period of time. The general methodology of this work seems to us to be easily reproducible in the preparation of clinical studies of other types for connected devices, which aim to develop measurement protocols limiting the often significant cost of such studies. One of the immediate prospects of this work is the initiation of a clinical study on different patients who have been treated by surgery for breast cancer, after improving the robustness of the design of the prototype to take into account the uncertainties affecting the measurements.

In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

Machine tools are complex structures consisting of several parts connected through different types of joints. Mechanical joints affect dynamics of the machine tools significantly, and any virtual model of the structure should include joint properties. It is desirable to use an interfacial sensor inside the joint to directly identify joint dynamic properties without changing the joint’s design and dynamics. In this study, a polymeric nanocomposite sensor with high sensitivity and a wide frequency bandwidth is implemented inside a bolted lap joint to identify the joint dynamic properties. The sensor implementation does not require any modifications to the joint design which makes the proposed approach suitable for many applications. An identification procedure is developed to find the micro-slip regime and the stick-slip boundaries in the joint interface using the acquired data of the nanocomposite sensor. Lab scale experiments are then conducted on a structure that consists of two beams attached to each other using a bolted lap joint. The proposed method is then used to identify the joint dynamics and the results are then compared with an existing approach named hysteresis loop technique. The experimental results show that the proposed method can predict the joint properties effectively with maximum deviations of 17% compared to the hysteresis loop results. Furthermore, effects of the contact normal load and the excitation load on the joint properties are investigated.

We propose a second harmonic phase angle method (WMS-$\theta _{2f}$) based on wavelength modulation spectroscopy for trace gas detection. WMS-$\theta _{2f}$ exhibits the advantages of background-free and immunity to light intensity fluctuations. The detection sensitivity is 1-2 orders of magnitude higher than that of the first harmonic phase angle detection method. Moreover, there is room for further improvement in sensitivity by optimizing the linearity of the light intensity modulation of the laser. We verified the feasibility of the WMS-$\theta _{2f}$ method experimentally and theoretically, respectively.

A semi-empirical model was applied to estimate the frequency shift of a shear-horizontal surface-acoustic-wave (SH-SAW) biosensor for detecting a disease-related biomarker antigen transferrin receptor (CD71) in the sample. In the simulation to investigate its sensitivity, a shift of the SH-SAW resonant frequency occurred by applying an incremental surface mass density change on the surface. The semi-empirical model was proposed and developed by using the experimental and numerical results to relate the concentration of the biomarker to the frequency shift. Results indicated that the thickness of the SiO2 guiding layer affects the sensitivity of SH-SAW sensing, and the dependence is non-monotonically. The SH-SAW sensor was used for specific detection of biotin at a varied concentration. With the concentration of the targeted antigen in the range 0.4 ∼4.2 μg/mL, a typical exponential relation was found between the quantitative target and the frequency shift. Measurement results showed that the mass-loading effect of the antibody-antigen has a reliable response with a sensitivity of 0.94 kHz/(μg/mL). Effects of the sample flow rate on the antigen- antibody interaction and thus the frequency shift of the SH-SAW sensor were also evaluated. It is demonstrated that the proposed model provides a useful approach to analyze effectively the frequency shift dependence on the concentration and the flow rates of sensed molecules in a flow-type SH-SAW sensor.

Parameter adjustment through numerical optimization has become a commonplace of contemporary microwave engineering. Although circuit theory methods are ubiquitous in the development of microwave components, the initial designs obtained with such tools have to be further tuned to improve the system performance. This is particularly pertinent to miniaturized structures, where the cross-coupling effects cannot be adequately accounted for using equivalent networks. For the sake of reliability, design closure is normally performed using full-wave electromagnetic (EM) simulation models, which entails considerable computational expenses, often impractically excessive. Available mitigation techniques include acceleration of the conventional (e.g., gradient-based) routines using adjoint sensitivities or sparse sensitivity updates, surrogate-assisted and machine learning algorithms, the latter often combined with nature-inspired procedures. Another alternative is the employment of variable-fidelity simulations (e.g., space mapping, co-kriging), which is most often limited to two levels of accuracy (coarse/fine). This work discusses an EM model management approach coupled with trust-region gradient-based routine, which exploits problem-specific knowledge for continuous (multi-level) modification of the discretization density of the microwave structure at hand in the course of the optimization run. The optimization process is launched at the lowest discretization level, thereby allowing for low-cost exploitation of the knowledge about the device under study. Subsequently, based on the convergence indicators, the model fidelity is gradually increased to ensure reliability. The simulation fidelity selection is governed by the algorithm convergence indicators. Computational speedup (i.e., reduction in the number of EM simulations required by the optimization process to converge) is achieved by maintaining low resolution in the initial stages of the optimization run, whereas design quality is secured by eventually switching to the high-fidelity model when close to concluding the process. Numerical verification is carried out using two microstrip circuits, a dual-band power divider and a dual-band branch-line coupler, with the average savings of almost sixty percent when compared to single-fidelity optimization.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

The success achieved by deep learning techniques in image labeling has triggered a growing interest in applying deep learning for three-dimensional point cloud classification. To provide better insights into different deep learning architectures and their applications to ALS point cloud classification, this article presents a comprehensive comparison among three state-of-the-art deep learning networks: PointNet++, SparseCNN, and KPConv, on two different ALS datasets. The performances of these three deep learning networks are compared w.r.t. classification accuracy, computation time, generalization ability as well as the sensitivity to the choices of hyper-parameters. Overall, we observed that PointNet++, SparseCNN, and KPConv all outperform Random Forest on the classification results. Moreover, SparseCNN leads to a slightly better classification result compared to PointNet++ and KPConv, while requiring less computation time and memory. At the same time, it shows a better ability to generalize and is less impacted by the different choices of hyper-parameters.

Deadbeat predictive control (DBPC) requires accurate model parameters in its application, therefore it is difficult to use DBPC for surface mounted permanent magnet synchronous motor (SPMSM) with uncertain motor parameters. As a solution, this paper proposes a strategy to obtain the needed parameters, and then uses them to achieve DBPC on SPMSMs whose parameters are unknown. A detailed investigation on parameter sensitivity of DBPC is presented, and attention is paid to different influences between the incremental inductance and the apparent inductance. On this basis, parameter configurations in the DBPC are suggested. And novel current-controller-free self-commissioning schemes are proposed to identify the stator resistance together with the stator inductances under different magnetic saturation levels. Compared with existing commissioning schemes that do not depend on current controllers, solutions for commissioning voltage auto-tuning is provided. So the methods are able to automatically decide the amplitudes of the injected voltage for a given motor, and achieve a controllable current feedback during the commissioning. This brings significant convenience for the inductance identification at a specific saturation level. Moreover, the inverter nonlinearity compensation is not needed but still, identification accuracy can be guaranteed. The feasibility and effectiveness of the proposed method are confirmed by the achieved DBPC and the corresponding current tracking performances on two different SPMSMs.

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

This paper focuses on a modified Multi-Depot Unmanned Aerial Vehicle Routing Problem (MMDUAVRP). Comparing to classic multi-depot vehicle routing problem, our studied problem has no constraints to restrict the depot where the Unmanned Aerial Vehicle (UAV) departs and returns. This work aims to minimize the number of UAVs and total distance traveled by all UAVs. This problem is mathematically formulated in this paper and a heuristic-assignment based hybrid large neighborhood search(HLNS) is proposed to solve it. Extensive computational experiments are conducted to verify the performance of HLNS. The HLNS algorithm was first tested on Multi Traveling Salesman Problem which is a simplified version of the MMDUAVRP, and high quality solutions have been obtained. Experimental results by compared with CPLEX and other well-known algorithms suggest that our proposed algorithm provides better solutions within a comparatively shorter period of time. In addition, we also conduct sensitivity analysis on the location of depots and task points that may affect the total cost of solution.

Delineating the electrocardiogram (ECG) waveform is an important step with high significance in cardiology diagnosis. It refers to extract the ECG morphology in start, peak, end points of waveform. Due to various shapes and abnormalities presented in ECG signals, several conventional computer algorithms always fail to extract the essential feature of heart information. Thus, it is critical to investigate an automated ECG signal delineation with its result accuracy. In this study, we propose the delineation process by using bidirectional long short-term memory (BiLSTM) classifier. Such process was conducted as one beat to the next (beat-to-beat), that means the ECG waveform classification is start of P-wave1 to start of P-wave2. However, such classifier lack of feature extraction process, reducing the classification accuracy result. To improve the classifier performance, convolutional layers as facture extraction are stacked with BiLSTM named ConvBiLSTM. We conducted the experimental based on seven-class ECG waveform using a publicly available QT Database with annotation of the main waveforms to produce high accurate classifier, i.e., Pstart–Pend, Pend – QRSstart, QRSstart – Rpeak, Rpeak – QRSend, QRSend – Tstart, Tstart–Tend, and Tend–Pstart. It was found that the proposed model showed remarkable results with overall average performances of 99.83% accuracy, 98.82% sensitivity, 99.90% specificity, 98.86% precision, and 98.84% F1 score. Based on these promising results, the efficacy of the proposed stacked ConvBiLSTM model in classifying ECG waveform provides a great opportunity to help cardiologists in diagnosis decision-making for faster assessment.

With the proliferation of renewable energy and power electronic converters in power systems, the reliability issue has raised more research attention than ever before. This paper proposes a comprehensive framework to assess the reliability of a power system considering the effect from various power converter uncertainties. For the converter stage, we formulate a reliability model for each power converter based on several semiconductor devices, for which ambient uncertainties and converter topologies are considered. For the system stage, we estimate system reliability indicators through a non-sequential Monte Carlo simulation and calculate their variances. Afterward, we leverage machine learning regression algorithms between two stages to establish a nonlinear reliability relation. Moreover, a variance-based sensitivity analysis (SA) is conducted to rank and identify the most influential converter uncertainties with respect to the variance of system EENS. Based on the SA conclusions, system operators can take proactive actions to mitigate the potential risk of the system.

Imbalanced class has been a common problem encountered in the modeling process, and has attracted more and more attention from scholars. Biased classifiers, which limit the classifiers’ performance for minority classes, will be produced if the imbalanced ratio between the number of positive labels and negative labels is ignored. The synthetic minority over-sampling technique (SMOTE) is a very classic and popular over-sampling method, which is widely used to address this problem. However, SMOTE increases label noise and the training time during the over-sampling process. To improve the detection rate of minority classes while ensuring efficiency, we propose a cost-sensitive XGBoost (CS-XGB) for the imbalanced data problem. The CS-XGB method can reduce the classifiers’ preference for most classes without changing the distribution of the original data. 600000 Uniform Resource Locators (URLs) were collected to validate the CS-XGB method. We compare XGBoost (XGB), SMOTE+XGB and CS-XGB, and the experimental results confirm that the CS-XGB is robust and efficient for imbalanced cases.

Commonly used variants of the proportional-integral, PI, and the integral-proportional, IP, compensator gains selections, merged with the $\mathfrak {D}$ -decomposition technique are presented in this paper. The motivation to this work is the willingness to check whether such a combination can lead to unified and perhaps simplified approach in this matter. Therefore, the $\mathfrak {D}$ -decomposition technique has been effectively combined with the frequency- and the time-domain driven requirements regarding the control dynamics. Criteria such as the gain- and phase-margins, $(GM, PM)$ , the sensitivity, $M_{\text {S}}$ , the pole placements by means of the $(\sigma, \omega _{\text {d}})$ and the $(\xi, \omega _{\text {n}})$ , and the overshoot with the rise time $(\delta, t_{\text {r}})$ are considered. It has been shown that the control design effort can be reduced by the means of the $\mathfrak {D}$ -decomposition to intuitive judgments in the proportional and integral gains coordinates $(K_{\text {P}}, K_{\text {I}})$ with parametric curves. As such it can be thought of as a promising scenario in a case considered. The analyses are presented and discussed in details. They are conducted basing on example of output voltage closed loop control of the Dual Active Bridge, DAB, converter. The circuit operates under the phase shift control scheme. The control-to-output transfer function identification and the control circuit delays are included in the analyses. The case analysis have shown that the time domain requirements are less effectively met with the PI regulator when compared to its IP configuration. This is due to the commonly used simplifications during conversion between the $(\xi, \omega _{\text {n}})$ and the $(\delta, t_{\text {r}})$ in presence of uncompensated zero of the closed loop transfer function. The paper contains complete and intelligible approach to dedicated mathematical investigations verified experimentally.

Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation accuracy while combining with advanced remote sensing technology. Airborne light detection and ranging (LiDAR) has the preferable capability to characterize 3-D canopy structure and estimate forest structural parameters. In this study, we developed a deep learning-based algorithm (Deep-RBN) that combined the fully connected network (FCN) deep learning algorithm with the optimized radial basis neural network (RBN) algorithm for forest structural parameter estimation using airborne LiDAR data. The multiple iterations were used to constantly update the internal weights to achieve the optimized accuracy of model fitting, and the optimized RBN algorithm was developed for the limited training sets. We assessed the efficiency and capability of the Deep-RBN in the estimation of forest structural parameters in a subtropical planted forest of southern China, by comparing the traditional FCN algorithm and multiple linear regression. We found that Deep-RBN had the strongest capability in estimates of forest structural parameters (R2 = 0.67-0.86, rRMSE = 6.95%-20.34%). The sensitivity analysis of the key hyperparameters of Deep-RBN algorithm showed that the learning rate is one of the most important parameters that influence the performance of predictive models, and while its value equal is to 0.001, the predictive models had the highest accuracy (mean DBH: RMSE = 1.01, mean height: RMSE = 1.45, volume: RMSE = 26.49, stem density: RMSE = 121.06). With the increase of training samples added in Deep-RBN model, the predictive models performed better; however, no significant improvements of accuracy were observed while the number of training set is larger than 80. This study demonstrates the benefits of jointly using the Deep-RBN algorithm and airborne LiDAR data to improve the accuracy of forest structural parameter estimation and mapping, which provides a promising methodology for sustainable forest resources monitoring.

Muscle weakness owing to stroke, spinal cord injuries, or aging can make a person’s life sedentary, temporarily as well as permanently. Such persons need to be motivated to break their sedentary postures and attempt independent motion. A key motivator in this aspect is the ability to easily transition from seated to standing posture. If this sit-to-stand transition (STSt) is easy, it will encourage further mobility. A soft wearable device that can assist the STSt, would fill this need perfectly. Such a device should be able to seamlessly assist during STSt and be unobtrusive during sitting. A major limitation that is currently holding back the development of soft exosuits in STSt-assist is the lack of low-profile soft actuators with high strain rate and force-to-weight ratio. Hence, we propose a novel low-profile vacuum actuator (LPVAc) with an integrated inductive displacement sensor that, can be rapidly fabricated, is lightweight (14g), and can provide high strain (65%) and a high force-to-weight ratio (285 times self-weight). The proposed actuator comprises a low-profile spring encased within a low-density polyethylene film with rapid vacuum actuation and passive quick return. The proposed inductive sensor has a sensitivity of $0.0022~\mu H/mm$ and the hysteresis is below 1.5% with an overall absolute average error percentage of 5.24%. The performance of the proposed integrated sensor in displacement control of the LPVAc is experimentally evaluated. The proposed actuator is integrated into a novel mono-articular STSt-assist exosuit for preliminary testing. Surface electromyography measurements of the gluteus maximus muscles during STSt indicate a mean muscle activity reduction of 45%. This supports the potential use of the proposed actuator in STSt-assist.

Compared with general chaotic systems, laser chaotic systems have more complex dynamic behaviors and extremely high sensitivity to parameters and initial values. So far, there are few studies on the application of laser chaotic systems to image encryption. Therefore, this paper proposes a new image encryption scheme based on an optical injection semiconductor laser chaotic system. The encryption scheme mainly includes two processes of scrambling and diffusion. The scrambling process converts the pseudo-random sequences generated by the laser chaotic system into chaotic matrixes and scrambling each pixel through a random point scrambling algorithm. In the diffusion process, the chaotic sequences generated by the laser chaotic system are quantized into random numbers and applied to the improved gravitational model algorithm to achieve the effect of pixel diffusion. The experimental results and security analyses indicate that the proposed algorithm has good image encryption performance and can effectively resist various common attacks on image encryption systems.

This paper analyzes the yaw data of the 2.5MW wind turbine of XEMC Windpower Company, and the real wind information collected by the wind farm, and proposes a two-level economic model predictive control (TL-EMPC) yaw strategy based on ideal wind measurement by light detection and ranging (LiDAR). This strategy comprehensively considers the power loss caused by yaw misalignment and the structural loads of the yaw bearing during the yaw process, making the yaw system more efficient and economical: In the high wind speed range, the yaw system has a higher sensitivity by setting the threshold of the yaw error angle, so as to fully capture wind energy; in the low wind speed range, fully consider the fatigue load of the critical parts of the wind turbine during the yaw process, thus Improve the economy of the wind turbine yaw system. The fatigue load and limit load of the yaw actuator at different yaw speeds are analyzed, and the best yaw speed is obtained. The finite control set of the yaw speed is established, which is used as the constraint set of the second-level minimum objective function. Finally, an external controller was used to simulate the 2.5MW wind turbine model in Bladed, and the effectiveness of the control strategy was verified.

The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dual-antenna InSAR data processing are performed to verify the effectiveness of this method.

Grid forming converters are a promising solution to enhance the stability of electrical networks with a high penetration of renewable generation. Virtual Synchronous Machines (VSM) are a particular type of grid forming converter, which can be implemented without an inner Current Control (CC) loop. This VSM implementation can provide a better inertial response than other grid forming structures with an inner CC. However, it cannot limit the current through the converter during a fault. A standard solution uses a VSM as the primary controller with a conventional inner CC acting as a backup controller during fault conditions, referred to here as a Dual VSM control structure. The switching action between the primary and the backup controllers is dependent on an accurate Fault Detection Algorithm (FDA). The conventional FDA mentioned in the literature has limitations in particular but not uncommon scenarios. The article shows the limitations of the conventional FDA in weak grids and unbalanced conditions and introduces a new FDA with improved performance. Two types of sensitivity analysis are used to study the dynamics of the proposed control approach. The First is the sensitivity of the proposed control structure to different fault locations in strong and weak grids. The Second is the sensitivity of the controller response to changing controller parameters. The second analysis is introduced as a reference for tuning and improvement of the control structure introduced.

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building’s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models are typically voltage-invariant. This paper proposes a novel framework to transform these voltage-invariant building load models into fully time- and voltage-dependent load profiles using available data on the voltage sensitivity of individual load components. While a voltage-dependent building model could theoretically be generated from static load models of every component in a building, this approach faces two challenges: first, load models representing all load components are impractical to develop for all possible load component types; second, building energy consumption is never measured or modeled at the individual component level. The proposed framework compiles available component data in the form of static ZIP load model parameters, and maps them into the end use categories utilized by standard building modeling programs. The voltage sensitivity of each end use category is then bounded by the extrema of the component models within it. This framework is applied to a load profile case study representing the aggregate U.S. residential building stock. In addition to the minimum/maximum conditions, a load profile based on typical load composition and weighted ZIP parameters is generated for the same building stock. The results show that for a 10% drop in voltage, using the least sensitive ZIP parameters, active power is expected to be 3% to 14% lower than nominal, depending on the season and time of day. Using the most sensitive ZIP parameters, the active power is expected to be 9% to 20% lower than nominal, also depending on the season and time of day.

Microgrids with installation of converter controlled-based generations (CCGs), i.e. renewable energy source (RES) and battery energy storage system (BESS), may lead to a lack of system inertia. Moreover, CCG modes dominated by the CCGs may result in new control issues. Accordingly, optimal-fixed controllers designed at an operating point may not be sufficient to deal with the nonlinearity of such microgrids. This paper presents an adaptive robust control strategy of a future microgrid considering the CCG modes. Without requiring any microgrid parameters, the proposed control using a subspace-based state-space identification is used to 1) monitor microgrid changes along with moving window, 2) identify microgrid model, 3) assess stability indices, and 4) robustly design controllers of RES and BESS. Characteristic and sensitivity of the new CCG modes are analyzed. Effectiveness of the proposed control method is verified in a microgrid with 100% CCGs under various RES outputs and load patterns.

Epiretinal membrane (ERM) is an eye disease that affects 7% of the world population, with a higher incidence in people over 75 years old. If left untreated, it can lead to complications in the central vision, resulting in severe vision loss. Early detection is important for progress follow-up, treatment monitoring, and to avoid total vision loss. Optical coherence tomography, a non-invasive retina imaging technique, can be used for effective detection and monitoring of this condition. To date, automatic methods to detect ERM have received little attention in the research literature. This article describes the application of deep learning to the automatic detection of ERM. The proposed solution is based on four widely used convolutional neural network architectures adapted to the task using transfer learning, and fine-tuned with a proprietary dataset. The architectures were specialized by optimizing the network hyperparameters and two loss functions, cross-entropy and focal loss. A detailed description of the methods is provided, complemented with an exhaustive evaluation of their performance. Overall, the methods reached an accuracy of 99.7%, with sensitivity and specificity of 99.47% and 99.93%, respectively. The results showed that transfer learning enabled a successful use of deep learning to detect ERM in optical coherence tomography retinal images, even when only relatively small training datasets are available.

In this article, an Improved Frequency Regulation (IFR) for an interconnected hybrid power system under a deregulated scenario is proposed. The analyzed test system comprises of a thermal power system, biogas plant, and Distributed Generation (DG). The impact of the integration of Renewable Energy Sources (RES) has been examined by considering DG system with solar and wind power generation. In order to make the system more realistic, appropriate non-linearities are incorporated into the thermal and biogas system. Electric Vehicle (EV) is also employed to take care of some portion of uncontracted demand. A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR. Also, a new Quasi-Opposition-based Artificial Electric Field Algorithm (QO-AEFA) is proposed to acquire the optimal controller gain parameters of the tested system. A pertinent performance analysis is also examined for other employed algorithm in this study, to figure out the eminence of the proposed control algorithm. A comprehensive examination and comparison of the proposed controller with other controllers shows its effectiveness for the proposed test system. In order to ensure the reliability of the proposed controller, sensitivity analysis is also carried out for system parameters that reveal its robust nature. Further, case studies for random load variations and comparison with the works in previous literature also manifest the puissance of the current research work. Moreover, the improved results under random weather conditions illustrate the effectiveness of the proposed IFR under deregulation.

Fine particulate matter (PM2.5) is a major environmental health risk. Several instruments based on the quartz crystal microbalance (QCM) have been developed for PM2.5 measurement because of their accurate, sensitive, real-time, and low-cost mass measurements. However, prolonged or non-uniform deposition on the quartz crystal can cause nonlinear responses between frequency shifts and mass deposition, and its frequent manual cleaning with wet sponges is required. These disable long-term measurements of the instruments, thus limiting their applications in remote areas. Herein, we present a new PM2.5 instrument called qEPC-Snow. This instrument consists of a QCM crystal embedded in an electrostatic particle concentrator (EPC) for collection and sensing of PM2.5 and a carbon dioxide aerosol (snow) jet unit for residue-free, rapid, effective, and non-destructive cleaning of the crystal. Laboratory tests were conducted with aerosolized 100-nm and 2- $\mu \text{m}$ polystyrene latex microspheres as PM2.5 representatives to evaluate (i) frequency responses and (ii) mass sensitiveness of qEPC-Snow, (iii) particle removal efficiencies, and (iv) reuse of the used crystals. Experimental results demonstrated high removal efficiencies (approximately 99.9% for both particle sizes) and statistical similarity between the initial and cleaned QCM crystals in the frequency shift-mass deposition relationship, thereby enabling measurement for more than one month without demounting the crystals. The mass sensitivity was 57.34 (Hz/ $\mu \text{g}$ ) with R2 = 0.9904, corresponding to 0.05667 [(Hz/min)/( $\mu \text{g}/\text{m}^{3})$ ] in mass concentration sensitivity for the PM2.5 representatives. The influence of particle sizes on qEPC-Snow’s frequency behaviors will also be discussed in detail.