HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

Class Title:Radiological imaging method a comprehensive overview purpose.This GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the field.Materials and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed tomography.Additionally recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis systems.Results:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical conditions.Conclusion:Radiological imaging is an extremely important tool in modern medicine to assist in medical diagnosis.This work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.

Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

In this paper,two crossover hybrid variable-order derivatives of the cancer model are developed.Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators.The existence,uniqueness,and stability of the proposed model are discussed.Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models.Comparative studies with generalized fifth-order Runge-Kutta method are given.Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented.We have showcased the efficiency of the proposed method and garnered robust empirical support for our theoretical findings.

Generating Squeezed States of Nanomechanical Resonator via a Flux Qubit in a Hybrid System

We propose a scheme for generating squeezed states based on a superconducting hybrid system. Our system consists of a nanomeehanical resonator, a superconducting flux qubit, and a superconducting transmission line resonator. Using our proposal, one can easily generate the squeezed states of the nanomechanical resonator. In our scheme, the nonlinear interaction between the nanomechanical resonator and the superconducting transmission line resonator can be implemented by the flux qubit as ＇nonlinear media＇ with a tunable Josephson energy. The realization of the nonlinearity does not need any operations on the flux qubit and just needs to adiabatically keep it at the ground state, which can greatly decrease the effect of the decoherenee of the flux qubit on the squeezed ef^ciency.

Triboelectric–Electromagnetic Hybrid Generator for Harvesting Blue Energy

Progress has been developed in harvesting lowfrequency and irregular blue energy using a triboelectric–electromagnetic hybrid generator in recent years. However,the design of the high-efficiency, mechanically durable hybrid structure is still challenging. In this study, we report a fully packaged triboelectric–electromagnetic hybrid generator(TEHG), in which magnets were utilized as the trigger to drive contact–separation-mode triboelectric nanogenerators(CS-TENGs) and coupled with copper coils to operate rotary freestanding-mode electromagnetic generators(RF-EMGs). The magnet pairs that produce attraction were used to transfer the external mechanical energy to the CS-TENGs, and packaging of the CS-TENGpart was achieved to protect it from the ambient environment. Under a rotatory speed of 100 rpm, the CS-TENGs enabled the TEHG to deliver an output voltage, current,and average power of 315.8 V, 44.6 μA, and ~ 90.7 μW,and the output of the RF-EMGs was 0.59 V, 1.78 m A, and 79.6 μW, respectively. The cylinder-like structure made the TEHG more easily driven by water flow and demonstrated to work as a practical power source to charge commercial capacitors. It can charge a 33μF capacitor from 0 to 2.1 V in 84 s, and the stored energy in the capacitor can drive an electronic thermometer and form a self-powered water-temperature sensing system.

Inference for dependence competing risks from bivariate exponential model under generalized progressive hybrid censoring with partially observed failure causes

Inference are considered for the dependence competing risks model by using the Marshal-Olkin bivariate exponential distribution. Under generalized progressively hybrid censoring with partially observed failure causes, the maximum likelihood estimators are established, and the approximate confidence intervals are also constructed via the observed Fisher information matrix.Moreover, Bayes estimates and highest probability density credible intervals are presented and the importance sampling technique is used to compute corresponding results. Finally, the numerical analysis is proposed for illustration.

THEORETICAL BASIS AND GENERAL OPTIMAL FORMULATIONS OF ISOPARAMETRIC GENERALIZED HYBRID/MIXED ELEMENT MODEL FOR IMPROVED STRESS ANALYSIS

By the modified three-field Hu-Washizu principle, this paper establishes a theoretical founda- tion and general convenient formulations to generate convergent stable generalized hybrid/mixed cle- ment (GH/ME) model which is invariant with respect to coordinate, insensitive to geometric distortion and suitable for improved stress computation. In the two proposed formulations, the stress equilibrium and orthogonality constraints are imposed through incompatible displacement and internal strain modes respectively. The proposed model by the general formulations in this paper is characterized by including as- sumed stress/strain, assumed stress, variable-node, singular, compatible and incompatible GH/ME models. When using regular meshes or the constant values of the isoparametric Jacobian Det in the assumed strain in- terpolation, the incompatible GH/ME model degenerates to the hybrid/mixed element model. Both general and concrete guidelines for the optimal selection of element shape functions are suggested. By means of the GH/ME theory in this paper, a family of new GH/ME can be and have been easily constructed. The software can also be developed conveniently because all the standard subroutines for the corresponding isoparametric displacement elements can be utilized directly.

A Numerical Convex Lens for the State-Discretized Modeling and Simulation of Megawatt Power Electronics Systems as Generalized Hybrid Systems

Modeling and simulation have emerged as an indispensable approach to create numerical experiment platforms and study engineering systems.However,the increasingly complicated systems that engineers face today dramatically challenge state-of-the-art modeling and simulation approaches.Such complicated systems,which are composed of not only continuous states but also discrete events,and which contain complex dynamics across multiple timescales,are defined as generalized hybrid systems(GHSs)in this paper.As a representative GHS,megawatt power electronics(MPE)systems have been largely integrated into the modern power grid,but MPE simulation remains a bottleneck due to its unacceptable time cost and poor convergence.To address this challenge,this paper proposes the numerical convex lens approach to achieve state-discretized modeling and simulation of GHSs.This approach transforms conventional time-discretized passive simulations designed for pure-continuous systems into state-discretized selective simulations designed for GHSs.When this approach was applied to a largescale MPE-based renewable energy system,a 1000-fold increase in simulation speed was achieved,in comparison with existing software.Furthermore,the proposed approach uniquely enables the switching transient simulation of a largescale megawatt system with high accuracy,compared with experimental results,and with no convergence concerns.The numerical convex lens approach leads to the highly efficient simulation of intricate GHSs across multiple timescales,and thus significantly extends engineers’capability to study systems with numerical experiments.

Design of Engine-Generator Work Mode for Hybrid Electric Vehicle

From electric circuit theory view, a system model of series hybrid electric vehicle was built which uses engine-generator and battery pack as its on-board energy source in this paper. Based on the analysis for the constant power work mode and constant bus voltage work mode of engine-generator, a third work mode was put forward which combined the advantages of constant power and constant bus voltage work modes. The new work mode is reasonable to keep the battery in good working conditions and to extend its life. Also the working conditions of engine can be bettered to get low pollution and high efficiency.

Development and Analysis of the Magnetic Circuit on Double-Radial Permanent Magnet and Salient-Pole Electromagnetic Hybrid Excitation Generator for Vehicles

With the improvement of vehicles electrical equipment, the existing silicon rectification generator and permanent magnet generator cannot meet the requirement of the electric power consumption of the modern vehicles electrical equipment. It is di cult to adjust the air gap magnetic field of the permanent magnet generator. Consequently, the output voltage is not stable. The silicon rectifying generator has the problems of low e ciency and high failure rate.In order to solve these problems, a new type of hybrid excitation generator is developed in this paper. The developed hybrid excitation generator has a double-radial permanent magnet, a salient-pole electromagnetic combined rotor,and a fractional slot winding stator, where each rotor pole corresponds to 4.5 stator teeth. The equivalent magnetic circuit diagram of permanent magnet rotor and magnetic rotor is established. Magnetic field finite element analysis(FEA) software is used to conduct the modeling and simulation analysis on double-radial permanent magnet magnetic field, salient-pole electro-magnetic magnetic field and hybrid magnetic field. The magnetic flux density mold value diagram and vector diagram are obtained. The diagrams are used to verify the feasibility of this design. The designed electromagnetic coupling regulator controller can ensure the stable voltage export by changing the magnitude and direction of the excitation current to adjust the size of the air gap magnetic field. Therefore, the problem of output voltage instability in the wide speed range and wide load range of the hybrid excitation generator is solved.

Torque Distribution Strategy for Integrated Starter/ Generator Hybrid Bus Implemented by Fuzzy Algorithm

A torque distribution strategy was designed by using fuzzy logic to realize the optimal control. The vehicle load zones were dynamically divided into several zones by several torque lines to indicate the drivers demand and the high or low efficient operating areas of the diesel engine. The fuzzy logic controller with trapezoid membership function and Mamdani rule reference mechanism was utilized. There are over 100 rules used in this fuzzy-based torque distribution strategy which are sorted into four rule-bases. The fuel economy and acceleration tests were designed to test and validate the integrated starter/generator (ISG) bus perfor-mance using fuzzy-based torque distribution strategy. The fuel economy is improved 7.7% compared with the rule-based strategy. Finally the road test results reveal that there is about 15% improvement of fuel economy. And the 0-50 km/h acceleration time is 9.5% shorter than the original bus.

ON THE EQUIVALENCE OF THE WILSON ELEMENT AND THE GENERALIZED HYBRID ELEMENT

In this paper the equivalence of the generalized hybrid element and the modified Wilson element, which is derived by the generalized hybrid method, is proved.

Likelihood Inference under Generalized Hybrid Censoring Scheme with Comp eting Risks

Statistical inference is developed for the analysis of generalized type-Ⅱ hybrid censoring data under exponential competing risks model. In order to solve the problem that approximate methods make unsatisfactory performances in the case of small sample size,we establish the exact conditional distributions of estimators for parameters by conditional moment generating function(CMGF). Furthermore, confidence intervals(CIs) are constructed by exact distributions, approximate distributions as well as bootstrap method respectively,and their performances are evaluated by Monte Carlo simulations. And finally, a real data set is analyzed to illustrate all the methods developed here.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

A ROBUST PHASE-ONLY DIRECT DATA DOMAIN ALGORITHM BASED ON GENERALIZED RAYLEIGH QUOTIENT OPTIMIZATION USING HYBRID GENETIC ALGORITHM

A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.

Design of Digital Hybrid Chaotic Sequence Generator

The feasibility of the hybrid chaotic sequences as the spreading codes in code divided multiple access(CDMA) system is analyzed. The design and realization of the digital hybrid chaotic sequence generator by very high speed integrated circuit hardware description language(VHDL) are described. A valid hazard canceledl method is presented. Computer simulations show that the stable digital sequence waveforms can be produced. The correlations of the digital hybrid chaotic sequences are compared with those of m-sequences. The results show that the correlations of the digital hybrid chaotic sequences are almost as good as those of m-sequences. The works in this paper explored a road for the practical applications of chaos.

Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

State-of-the-art review of MPPT techniques for hybrid PV-TEG systems:modeling,methodologies,and perspectives

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation.Of the currently available technologies,solar energy has promising application prospects owing to its merits of being clean,safe,and sustainable.Solar energy is converted into electricity through photovoltaic(PV)cells;however,the overall conversion efficiency of PV modules is relatively low,and most of the captured solar energy is dissipated in the form of heat.This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells.To overcome the shortcomings,an efficient approach involves combining a PV cell with a thermoelectric generator(TEG)to form hybrid PV-TEG systems,which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs.Based on a thorough examination of the literature,this study comprehensively reviews 14 maximum power point tracking(MPPT)algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion,namely conventional,mathematics-based,metaheuristic,artificial intelligence,and other algorithms.This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.