Optimal Orientations of the Generalized Cycles

Optimal orientations of the generalized cycles are studied. For a graph G, let D（G） be the family of the strong orientations of G,d（G）=min {d（D） D∈D（G） and ρ（G）=d（G）-d（G）, whered（G） and d （D） are the diameters of G and D respectively. Evaluate the value of ρ（G） is evaluated by reduction to absurdity when G is a generalized cycle Cn [Km], and a complete result is obtained.

The optimal performance of a generalized Carnot cycle for a generalized heat transfer law

The finite time thermodynamic performance of a generalized Carnot cycle, in which the heat transfer between the working fluid and the heat reservoirs obeys the generalized law Q∝( Δ T) m , is studied. The optimal configuration and the fundamental optimal relation between power and efficiency of the cycle are derived. Some special examples are discussed. The results can provide some theoretical guidance for the design a practical engine.

Performance characteristics of low-dissipative generalized Carnot cycles with external leakage losses

Under the assumption of low-dissipation, a unified model of generalized Carnot cycles with external leakage losses is established. Analytical expressions for the power output and efficiency are derived. The general performance characteristics between the power output and the efficiency are revealed. The maximum power output and efficiency are calculated. The lower and upper bounds of the efficiency at the maximum power output are determined. The results obtained here are universal and can be directly used to reveal the performance characteristics of different Carnot cycles, such as Carnot heat engines, Carnot-like heat engines, flux flow engines, gravitational engines, chemical engines, two-level quantum engines, etc.

Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.

Improving Brain Tumor Classification with Deep Learning Using Synthetic Data

Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising results.On the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL model.In this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with DL.This study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers.By comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of DL.One model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original images.Synthetic data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,respectively.The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature.Additionally,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.

The Generalized Thermodynamic Temperature and the New Expressions of the First and the Second Law of Thermodynamics

The classical thermodynamics reflects the significant relationship between the heat and the temperature. On the basis of the relationships, according to the mathematical derivation, this paper structures the conceptions of generalized heat, generalized thermodynamic temperature, generalized entropy and so on. The series of conceptions in the classical thermodynamics is merely a special case of the generalized thermodynamics. Based on these conceptions of generalized thermodynamics, this paper presents the new expressions of the first law and the second law of thermodynamics. In other words, these expressions are endued with new explanations. The Eq. LZ = kTS given by this paper provides theoretical basis for these new expressions.

A Demand Response System for Wind Power Integration: Greenhouse Gas Mitigation and Reduction of Generator Cycling

A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side,and a generic generation mix of nuclear,hydro,coal,gas and oil-based generators representing the supply side,is investigated using agent-based simulations.The simulation includes a transactive load control in a real-time pricing electricity market.The study investigates the impacts of adding wind power and demand response(DR)on both greenhouse gas(GHG)emissions and generator cycling requirements.The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation.The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors.It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation.However,the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators,and especially on the capacity factor at which they operate.Therefore,if a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes,it will result in higher GHG emissions.The simulations show that DR can yield a small reduction in GHG emissions,but also lead to a smaller increase in emissions in circumstances when,for example,a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes.Nonetheless,DR is shown to enhance overall system operation,particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability.DR reduces the amount of generator cycling by an increased order of magnitude,thereby reducing wear and tear,improving generator efficiency,and avoiding the need for additional operating reserves.The effectiveness of DR for these uses depends on the participation of responsive loads,and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.