Theoretical study of a thermoelectric-assisted vapor compression cycle for air-source heat pump applications

This paper proposes a thermoelectric-assisted vapor compression cycle (TVCC) for applications in air-source heat pump systems which could enhance the heating capacity of the system. Performances of TVCC are calculated and then compared with that of basic vapor compression cycle (BVCC). The simulation results show that when coefficients of performance (COPs) of the two cycles are almost equal, the TVCC under maximum COP condition of the thermoelectric modules still performs better than BVCC by 13.0% in heating capacity through selecting the appropriate intermediate temperature. In addition, the TVCC can also achieve an improvement of 16.4%–21.7% in both the heating COP and capacity when compared with the BVCC with an assistant electric heater that is provided with the equivalent power input of thermoelectric heat exchanger. Thus, the TVCC could be beneficial to the applications in small heat pumps if there is always need for auxiliary electric heat.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Efficiency and effectiveness enhancement of an intercooler of two‐stage air compressor by low‐cost Al2O3/water nanofluids

The present study was aimed to utilize low‐cost alumina (Al₂O₃) nanoparticles for improving the heat transfer behavior in an intercooler of two‐stage air compressor. Experimental investigation was carried out with three different volume concentrations of 0.5%, 0.75%, and 1.0% Al₂O₃/water nanofluids to assess the performance of the intercooler, that is, counterflow heat exchanger at different loads. Thermal properties such as thermal conductivity and overall heat transfer coefficient of nanofluid increased substantially with increasing concentration of Al₂O₃ nanoparticles. Specific heat capacity of nanofluids were lower than base water. The intercooler performance parameters such as effectiveness and efficiency improved appreciably with the employment of nanofluid. The efficiency increased by about 6.1% with maximum concentration of nanofluid, that is, 1% at 3‐bar compressor load. It is concluded from the study that high concentration of Al₂O₃ nanoparticles dispersion in water would offer better heat transfer performance of the intercooler.     

Chest X-ray enhancement to interpret pneumonia malformation based on fuzzy soft set and Dempster–Shafer theory of evidence

Image enhancement algorithms are commonly used to increase the contrast and visual quality of low-dose x-ray images. This paper proposes an automated enhancement method using soft fuzzy sets with a new decision-making scheme based on Dempster-Shafer theory of evidence for the visual interpretation of pneumonia malformation in low-dose x-ray images, called as XEFSDS. The XEFSDS model first generates an original source x-ray image into a complementary image, then each original and complement image is applied to the characterized image object and background areas of fuzzy space. The S-function is utilized to define fuzzy soft sets for the classification of gray level ambiguity in both images, and hence a decision criterion via Dempster-Shafer approach and fuzzy interval has been adapted to discriminate uncertainties on the pixel intensity and the spatial information. Modified membership grade operations have been performed on each object/background area, and Werner’s AND/OR operator (an aggregation operator) has been utilized to build a new membership function from two modified membership functions. Finally, an enhanced image is obtained from the new membership function via defuzzification. Experiments on different pneumonia X-ray images demonstrate that the XEFSDS scheme produces better results than the existing methods. To show the advantages of the XEFSDS scheme, we have executed a segmentation based examination on enhanced image for the detection of pneumonia malformation as well as abnormal lobe (lobar pneumonia) or bronchopneumonia.     

Broadband bowtie belt nanoantennas

In this article, we study a linear array of bowtie nanoantennas placed between two metallic strips that can work from 800 to 1420 nm (600 nm linewidth), with an electric field enhancement factor close to 20. We study the dynamical change of the position of the electric field enhancement amongst different elements in the array and, at the same time, the effects of dispersion on the scalability of the array elements. A systematic analysis and methodology to produce an array that can operate over a large bandwidth whilst maintaining the electric field enhancement without significant variation is provided.     

Stability and thermophysical properties of fly ash nanofluid for heat transfer applications

Improving the performance of heat transfer fluids is altogether significant. The best approach for improving the thermal conductivity is the addition of nanoparticles to the base fluid. In the present study, specific heat, dynamic viscosity, and thermal conductivity of water-based Indian coal fly ash stable nanofluid for 0.1% to 0.5% volume concentration in the temperature range of 30 to 60°C has been investigated. To evaluate an average particle diameter of 11.5 nm, the fly ash nanoparticles were characterized with scanning electron microscopy and dynamic light scattering. Using zeta potential, the stability of nanofluid in the presence of surfactant Triton X-100 was tested. Thermal conductivity and viscosity of fly ash nanofluid increased, while specific heat decreased as volume concentration increased. The effect of temperature on the fly ash nanofluid was directly proportional to its thermal conductivity and specific heat and inversely proportional to viscosity.     

Optimal Spectral-Galerkin Methods Using Generalized Jacobi Polynomials

We extend the definition of the classical Jacobi polynomials withindexes α, β>−1 to allow α and/or β to be negative integers. We show that the generalized Jacobi polynomials, with indexes corresponding to the number of boundary conditions in a given partial differential equation, are the natural basis functions for the spectral approximation of this partial differential equation. Moreover, the use of generalized Jacobi polynomials leads to much simplified analysis, more precise error estimates and well conditioned algorithms.Mathematics subject classification 1991. 65N35, 65N22, 65F05, 35J05     

Microstructures and Phase Transition Points of As-Cast Mg-Zn-Y System Alloys

As-east mierostruetures and their distribution of Mg-Zn-Y ternary alloy with high magnesium, low zinc and yttrium were examined using Nikon Epiphot optical microscopy （OM）, RigakuD/max-3C X- ray diffraetion （XRD）, and JEOL JSM-6700F scanning electron microscopy （SEM） equipped with an energydispersive X-ray spectroscopy （EDS）. In the as-east mierostructures, Yttrium and zinc tend to segregate at grain boundaries,     

差分跳频通信系统的特点及性能

文章介绍了一种新型的短波跳频通信技术——差分跳频,分析了差分跳频技术区别于常规跳频技术的主要特点。针对按序列检测的信号接收方法,对差分跳频通信系统在AWGN信道下的性能进行了理论分析,同时做出相应的计算机仿真,证实了差分跳频通信技术和按序列检测方法的结合,使通信系统在AWGN信道下的性能得到了比较显著的提升。