Journal of Materials Science - La0.7Pb0.3MnO3(LPMO) nanoparticles (NPs) were prepared by sol–gel auto-combustion method. These were embedded in P(VDF-TrFE) to form (0–3) nanocomposite... 相似文献
The paper presents modeling and simulation of ion-sensitive field-effect transistor (ISFET)-based pH sensor with temperature-dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal-oxide-semiconductor field-effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant-voltage, constant-current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state-of-the-art ML-based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature-independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance. 相似文献
Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance. 相似文献
A Fabry pérot antenna with a multilayer superstrate having nonuniform unit cells has been investigated as a receiving antenna for radio frequency (RF) energy harvesting applications. Here, the primary radiator is selected as a dual‐polarized aperture coupled microstrip antenna with a double‐layer superstrate. This antenna excites orthogonal polarizations, vertical (V) and horizontal (H) in the frequency band of 6.2 and 5.8 GHz, respectively, due to the presence of two orthogonal H‐shaped slots in its ground plane. The proposed antenna provides a gain enhancement of 9.8 and 10.1 dBi at the respective frequencies. The rectifying circuit is designed for a frequency of 5.8 GHz using a voltage doubler topology. The circuit provides a power conversion efficiency of 41% at 0 dBm input power. 相似文献
The major challenges faced by candidate electrode materials in lithium‐ion batteries (LIBs) include their low electronic and ionic conductivities. 2D van der Waals materials with good electronic conductivity and weak interlayer interaction have been intensively studied in the electrochemical processes involving ion migrations. In particular, molybdenum ditelluride (MoTe2) has emerged as a new material for energy storage applications. Though 2H‐MoTe2 with hexagonal semiconducting phase is expected to facilitate more efficient ion insertion/deinsertion than the monoclinic semi‐metallic phase, its application as an anode in LIB has been elusive. Here, 2H‐MoTe2, prepared by a solid‐state synthesis route, has been employed as an efficient anode with remarkable Li+ storage capacity. The as‐prepared 2H‐MoTe2 electrodes exhibit an initial specific capacity of 432 mAh g?1 and retain a high reversible specific capacity of 291 mAh g?1 after 260 cycles at 1.0 A g?1. Further, a full‐cell prototype is demonstrated by using 2H‐MoTe2 anode with lithium cobalt oxide cathode, showing a high energy density of 454 Wh kg?1 (based on the MoTe2 mass) and capacity retention of 80% over 100 cycles. Synchrotron‐based in situ X‐ray absorption near‐edge structures have revealed the unique lithium reaction pathway and storage mechanism, which is supported by density functional theory based calculations. 相似文献
The Journal of Supercomputing - An efficient integration of Internet of Things (IoT) and cloud computing techniques accelerates the evolution of next-generation smart environments (e.g., smart... 相似文献
Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (Ton), pulse-off time (Toff) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.
It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models. 相似文献
Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments. 相似文献