Different Sizes Of Windows Research Articles

The Investigation of Indoor Air Temperature Effects for Different Types of Window Dimensions Using CFD Simulation

Windows plays an important role in heat transfer and natural ventilation in buildings. In the field of building design, the challenge is to provide a thermally comfortable indoor environment that requires the least energy consumption to maintain. One of the energy reduction alternatives that can be incorporated into modern building design is the installation of efficient windows. The windows should allow thermally acceptable indoor air quality and have a pleasant design while being energy efficient. Good thermal conditions in school buildings, especially learning spaces, promote the educational process. Unfulfilling comfort levels can reduce the lecturers’ and students' physical and intellectual performance. One of the variables that affect the thermal comfort condition is indoor air temperature. The purpose of this study is to investigate the effect of window size on indoor air temperature by using Computational Fluid Dynamics (CFD) simulation. The selected classroom at Universiti Teknologi MARA (UiTM) Permatang Pauh, Pulau Pinang will be used as a case study. The field measurement result can be used to validate the simulation result. The simulation result will be compared to standard regulations set by the American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) Standard 55-2017. Three different sizes of windows have been chosen in this study which are double, triple, and quadruple-glazed windows. The validation result shows the percentage difference in indoor air temperature between the experimental and simulation is 1.04 %. The result found double and triple-glazed windows show the value of temperature under the range set by ASHRAE Standard 55-2017. The selection of triple-glazed windows is more suitable because the brightness of the classroom meets the criteria for the absence of direct solar radiation. In conclusion, the size of the window shows the performance in terms of heat transfer and indoor air temperature of the classroom.

Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches.

IMAGE ENHANCEMENT AND DE-NOISING TECHNIQUES OF MAGNETIC RESONANCE IMAGES

Medical images have noise, such as salt and pepper, speckle, and gaussian noise. So, getting the accuracy of magnetic resonance images is challenging always. The accuracy of brain images is essential for the clinical diagnosis process. The nonlinear method of median and morphological filters is used for enhancement, contrast adjustment, and histogram equalization for medical images and speech processing, to preserve them from noise and edge features. Because of its edge-preserving properties, this median spatial filter window is critically important and better for removing impulsive noise and speckle noise. Nine different shapes of windows and five different sizes of windows and five noise density injections are used for evaluating the performance of the median filter. The first person J.W.Tukey, did the smoothing process by median rank selection filter. The denoising using morphological process is also considered for analysis. These two nonlinear methods like median filtering and morphological methods are used for the restoration of noise from the original image. These two methods are applied in normal and seizure-affected images to calculate quality assessment metrics and also the performance evaluation metrics from magnetic resonance images. Any image needs to be denoised to get the accuracy, precision, and F-measure kind of common feature evaluation metrics. This paper evaluates two nonlinear filtering methods and their performance evaluated based on shapes and size of windows and for suppressing or de-noising to produce original noise-free images to assist clinically.

Application of Helbig integrals to magnetic gradient tensor multi-target detection

We extended the magnetic gradient tensor Helbig integrals (MGTHI) from classical Helbig integrals (HI), which can be perfectly adapted to the grid measurement of planar-cross magnetic gradient tensor system. With different sizes of windows, we can estimate the MGTHI by using the two-dimensional trapezoidal rule algorithm and calculate the total magnetization direction of grids, where the node with the smallest change in the magnetization direction is target horizontal position. In the targets recognition area determined by the invariant improved tilt angles, the source depth and dipole moment magnitude can be calculated with magnetic vector and tensor field data directly above the source. In experiments, the horizontal position deviation of multiple small magnets in the survey area of 2.1 m × 2.1 m and 1.2 m × 1.2 m is less than 0.08 m, and the buried depth deviation is less than 0.15 m, the positioning accuracy is improved by more than 5 times from the classical HI method.

Optimal window size selection for spectral information extraction of sampling points from UAV multispectral images for soil moisture content inversion

Soil moisture content monitoring with UAV remote sensing always involves the selection of an appropriate window size for spectral information extraction, but research on the effect of window size on the accuracy of soil moisture content monitoring models and the selection of the optimal window size has been rarely reported. To solve these problems, an experiment was conducted on three typical bare plots in Shahaoqu Experimental Station at Hetao Irrigation District, Inner Mongolia, China. First, remote sensing images were obtained from the three bare plots from April 15 through 17, 2019, with a six-rotor UAV equipped with a six-channel multispectral camera. Synchronously, the moisture content at 0–10 cm of the surface soil was measured using the drying method. Then, the spectral information was extracted through windows of 16 different sizes (ranging from 1 * 1 to 31 * 31). Followed was the construction of thirty spectral indices using the ratio and normalized ratio methods, and the processing of the constructed indices using principal component analysis. The principal components accounting for 95% of the cumulative contribution rate were selected as the input variables for the construction of the monitoring models based on BP neural network. Finally, the model accuracy was tested using ANOVA, and the local variogram of the spectrum was used to explore the optimal window size selection. The results demonstrated: (1) There are differences in the spectral information extracted from different sizes of windows, which affects the accuracy of soil moisture monitoring model; (2) The spatial autocorrelation threshold of the plots at the local variogram was 13 * 13, resembling the window size with the highest accuracy, so it is feasible to select the optimal window size with the local variogram; (3) As the window size of spectral information increased, R2 first increased and then decreased, reaching the maximum value of 0.261 at the size of 13 * 13, and RMSE first decreased and then increased, reaching the minimum value of 0.017 when at the size of 7 * 7. These results can provide some reference for window size selection in spectral information extraction to monitor soil moisture content.

A sliding window method to identify defects in 3D printing lattice structure based on the difference principle

Structural defects are inevitably produced by residual stress in the 3D printing process, which reduce the structural-functional properties. For a typical 3D printing lattice structure, a new sliding window approach to intelligently identify defects, based on the difference principle, in the inspection process was proposed. Industrial computed tomography (CT) was used for image information acquisition. The grayscale standard deviation in the local range of the CT slice is calculated to characterize the grayscale variation of the defect location. On this basis, a sliding window method is proposed for traversing CT slices. Three different sizes of window and the corresponding calculation methods were established, and then the appropriate window was selected through comparison. After differential processing is performed on the traversed image to extract the defect feature, automatic defect recognition rules are created. The experimental results show that the recognition rate of the proposed method is 98.5% for typical internal defects of metal 3D lattice structures and the validity of the method is verified. Compared with a manual marking method, this method can effectively improve the efficiency of defect detection.

Single Image Super-Resolution Using Asynchronous Multi-Scale Network

An existing multi-scale residual network (MSRN) has demonstrated its success on conducting the single image super-resolution (SISR) task. The MSRN consists of a number of multi-scale residual blocks (MSRBs), and each MSRB performs convolutions by exploiting two different sizes of windows for conducting multi-scale feature extraction. The smaller window is used to extract image features at a low scale, while the larger one is used for a high scale. To significantly reduce the number of parameters involved in the MSRB, a new feature extraction module, called the asynchronous multi-scale block (AMB), is proposed in this paper. It is based on the fact that the larger window used in the MSRB can be replaced by two smaller windows without affecting the original MSRB's function. Consequently, by replacing each MSRB with our AMB, an asynchronous multi-scale network (AMNet) is then constructed, which can yield a significant reduction on computational complexity. This means that more AMBs can be used in our AMNet to deliver superior SISR performance, while maintaining the same or comparable computational complexity to that of the MSRN. To consolidate all image features generated from all scales, a new fusion scheme, called the adaptive feature fusion block (AFFB), is proposed that weights the extracted features according to their importance for further increasing SISR's performance. Extensive experimental results have clearly shown the superiority of our proposed AMNet when compared with multiple state-of-the-arts.

Selection of Optimal Building Facade Texture Images From UAV-Based Multiple Oblique Image Flows

Oblique photogrammetry with multiple cameras onboard unmanned aerial vehicle (UAV) has been widely applied in the construction of photorealistic three-dimensional (3-D) urban models, but how to obtain the optimal building facade texture images (BFTIs) from the abundant oblique images has been a challenging problem. This article presents an optimization method for selection of BFTIs from the image flows acquired by five oblique cameras onboard UAV. The proposed method uses multiobjective functions, which consists of the smallest occlusion of the BFTI and the largest façade texture area, to select the optimal BFTIs. Geometric correction, color equalization, and texture repairment are also considered for correction of BFTI's distortions, uneven color, and occlusion by other objects such as trees. Visual C++ and OpenGL under the Windows Operating System are used to implement the proposed methods and algorithms. The proposed method is verified using 49 800 oblique images collected by five cameras onboard the Matrice 600 Pro (M600 Pro) UAV system over Dongguan Street, in the City of Ji'nan, Shandong, China. To restore the partially occluded textures, different thresholds and different sizes of windows are experimented, and a template window of $200\times200$ pixels <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is recommended. With the proposed method, 2740 BFTIs are extracted from 49 800 oblique images. As compared with the Pix4Dmapper and Smart 3-D method, it can be concluded that the optimal texture can be selected from the image flow acquired by multiple cameras onboard UAV and the approximately 95% memory occupied by the original BFTIs is reduced.

Development of Amine-Functionalized Silica Foams with Hierarchical Pore Structure for CO2 Capture

A novel mesocellular silica foam (MCF) support were synthesized, which has different pore volumes and two different sizes of windows on the spherical cells. Tetraethylenepentamine (TEPA)-functionalized MCF adsorbents were then prepared for CO2 capture. The effects of support structure, capture temperature, SO2, and NO2 on the CO2 capture were investigated. The results show that the hierarchical window structure can improve the dispersion of amine species in the silica foams. In situ diffuse reflectance infrared Fourier transform spectroscopy results indicate that more adsorption sites can be exposed in the pores at high temperature for CO2 capture. The MCF-TEPA60% has the optimal capture amount of 4.75 mmol/g with 15 vol % CO2 at 75 °C. The SO2 and NO2 have a negative influence on the adsorption and regeneration properties of adsorbent due to the production of byproducts. However, the MCF-0.8-TEPA60% can maintain outstanding carbon dioxide adsorption capacity during 10 regeneration cycles.

International equity market integration: evidence from ETFs

We examine international equity market integration using VAR-based rolling cointegration analysis and coefficients of the error correction terms. Applying different sizes of window for the rolling cointegration analysis, we reveal that 500-day or less windows might not be long enough to study the cointegration analysis and are more likely to have biased results. We also find three sub-periods showing relatively strong cointegrating relationships, but results for the rest of the sample periods display weak integration among international equity markets. Our findings from the error correction model show that major markets with greater coefficients are more efficient than markets with relatively smaller coefficients.

Different Sizes of Windows for Energy Spectrum of a Photon Counting X-ray Imaging System

In relation to the radiation imaging sensor Timepix, the energy spectrum generated from the sensor data has been studied and for this, different sizes of windows have been selected in order to observe their effect on energy spectrum generation. It was found that removing the charge sharing effect significantly improves the means of discovering the centre peak. Additionally, it has been observed that a 4x4 window is best suited with regards to energy spectrum generation. Different fluorescent materials were used as samples in order to generate test images and the analysis was conducted using MATLAB.

Time-Varying International Equity Market Integration: Evidence from Market Price of Exchange-Traded Funds

We examine time-varying international equity market integration using the VAR-based rolling cointegration analysis and coefficients of the error correction terms. Using Exchange-traded funds (ETFs) as proxies for international equity markets allows us to take advantages of avoiding discrepancy of trading time, exchange rate volatility and lack of liquidity. Applying different sizes of window for the rolling cointegration analysis, we reveal that 500-day (or less) windows may not be long enough and more likely to have results biased. We also find three sub-periods showing relatively strong cointegrating relationships, but results for the rest of sample period display weak integration among international equity markets. Then, we conclude that investors can benefit by diversifying their investments to major equity markets and others. Our findings from the error correction model exhibit that major markets with greater coefficients are more efficient than the markets with relatively smaller coefficients. After all, there might be an arbitrage opportunity due to a different response speed of short-run dynamics to the deviations from long-run equilibrium. We can easily apply our findings to actual investment strategies by simply buying stocks of ETFs, and enjoy the benefits from diversification and arbitrage opportunities.

Face recognition method based on relative gradient

Based on the original relative gradient operator,the authors proposed a new relative gradient operator and combined it with 2DPCA or 2DFLD. A face recognition algorithm based on this new relative gradient operator was also put forward. Experimental results on AR and Yale_B face database show that,the method has robustness to the complex change like various expression and lighting condition. The recognition accuracy of the method is much higher than 2DPCA,2DFLD and face recognition based on the original relative gradient operator. What is more,experiments were done on three different sizes of windows,which confirms that,when the window size is 3×3,the recognition result is relatively the best.

A multi-scale analysis of continental relief in Southern France

Abstract A statistical analysis of topography is performed on a square domain using different sizes of window applied to a Digital Elevation Model covering Southern France. For a given window, a two-dimensional linear drift is defined by fitting a plane through the topography, the direction and magnitude of its slope and the explained variance is then computed. After removing the previously defined linear drift, two-dimensional statistics of the residual topography is used to define the direction of lengthening of the topography called the direction of polarization. The data are evenlydistributed on a space grid of one kilometre. We detect correlations between the mean plane and statistics orientations and the mean drainage pattern. According to this approach, two scale thresholds are detected around 6km and 2 km.