Column Direction Research Articles

Shear Force of Interior Beam–Column Joints under Symmetrical Loading with Two Transverse Forces on the Beam †

The beam-to-column connection is a particularly vulnerable element in frame structures under seismic action and is often responsible for building damages. Experimental investigations carried out over the past six decades on shear strength in frame joints have not led to the establishment of a uniform procedure in the design codes of different countries. The reason lies probably in the varied nature of the investigated parameters and in the varied configurations of beam–column connections. A good knowledge of the forces passing through the frame joints in the beam–beam and column–column direction would allow both their adequate computation in new buildings and the verification of existing ones without requiring experimental studies. In the design codes of the leading countries in seismic engineering, the shear force is determined by the capacitive method, considering only the area of the longitudinal reinforcement of the beam passing through the column. This method shows us how much shear force the beam reinforcement can take, but not what the magnitude of the resulting forces actually is as a result of the acting loads. In addition, the method of the codes does not indicate the contribution of the concrete to the total magnitude of the shear force in the beam–column connection. In the proposed mathematical model for calculating the forces that leave the beam, the full dimensions of the cross-section of the beam were taken into account. The material properties and cross-sectional shape were also taken into account. A determining factor for the magnitude of forces entering the beam–column joint is the acting load on the beam. In this paper, the load of two transverse forces was considered. The forces are applied in different possible positions, while remaining symmetrically located on the beam. The calculations are based on Menabrea’s theorem to determine the hyperstatic unknowns. The results of the proposed method for the considered beam show that the magnitude of the shear force differs from that accepted in the literature and the norms by 2% to 27%, depending on the stage of development of the crack. In comparison, the Eurocode-recommended method shows differences in the order of 27% to 40% for the adopted beam under static loads.

Programmable thick-panel Miura-ori for ultra-large planar antennas with one-degree-of-freedom

Planar antennas are large spacecraft primarily used in various fields such as communication, Earth observation, astronomical observation, and scientific experiments. They require high surface accuracy, with the array needing to have uniform thickness and flat surfaces. An ultra-large planar antenna means higher receiving sensitivity and greater coverage area, which is the trend for the future. During launch, the planar antenna needs to be folded to fit into the payload capsule. In this study, a programmable thick-panel Miura-ori unit that has uniform thickness and satisfies the Bennett linkage condition is developed. After special design, this unit can be infinitely expanded in the row and column directions, allowing for the folding of planar antennas of any size (rows × columns). For each vertex inside the planar antenna, there are four panels around it, and their connection relationships all satisfy the Bennett linkage condition, resulting in the entire planar antenna having only one degree of freedom. The sector angle of each parallelogram can approach 90°, compacting the folding form of the planar antenna. Furthermore, a layout scheme for an ultra-large planar antenna is provided, the deployment ratio, envelope diameter and envelope height are derived, and the complete unfolding process of the ultra-large planar antenna is demonstrated. Finally, the effectiveness of the proposed method for satellite planar antennas is verified through an experiment.

47‐5: Analyzing and Enhancing Display Quality in FRC Algorithm

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform.

An efficient full-size convolutional computing method based on memristor crossbar

Modern artificial intelligence systems based on neural networks need to perform a large number of repeated parallel operations quickly. Without hardware acceleration, they cannot achieve effectiveness and availability. Memristor-based neuromorphic computing systems are one of the promising hardware acceleration strategies. In this paper, we propose a full-size convolution algorithm (FSCA) for the memristor crossbar, which can store both the input matrix and the convolution kernel and map the convolution kernel to the entire input matrix in a full parallel method during the computation. This method dramatically increases the convolutional kernel computations in a single operation, and the number of operations no longer increases with the input matrix size. Then a bidirectional pulse control switch integrated with two extra memristors into CMOS devices is designed to effectively suppress the leakage current problem in the row and column directions of the existing memristor crossbar. The spice circuit simulation system is built to verify that the design convolutional computation algorithm can extract the feature map of the entire input matrix after only a few operations in the memristor crossbar-based computational circuit. System-level simulations based on the MNIST classification task verify that the designed algorithm and circuit can effectively implement Gabor filtering, allowing the multilayer neural network to improve the classification task recognition accuracy to 98.25% with a 26.2% reduction in network parameters. In comparison, the network can even effectively immunize various non-idealities of the memristive synaptic within 30%.

A texture feature extraction method considering spatial continuity and gray diversity

Texture features play an important role in the field of remote sensing classification. However, most existing methods lack a comprehensive consideration of spatial continuity, which makes them either destroy the spatial integrity of regular ground objects or fail to quantify the fragmentation degrees of irregular ground objects. These problems weak the ability of existing methods to distinguish ground objects with different fragmentation degrees. Therefore, this study proposed a new texture feature extraction method considering spatial continuity and gray diversity (SCGD). SCGD first connected all pixels in a neighborhood in series from end to end according to the row and column directions, and the diversities of the spatial continuity encoding in different directions were calculated by the Shannon index. Then, the Shannon index was used to calculate the gray diversity. Finally, SCGD calculated the weighted average of spatial continuity diversity and gray diversity to obtain the final texture feature values. Validation results indicated that SCGD can effectively distinguish ground objects with different fragmentation degrees, and its performance is better than that of traditional methods. As the spatial resolution decreases, its performance advantage becomes more obvious. Moreover, SCGD has great application potential in the field of ground object classification, and combining it with deep learning models will contribute to achieving the fine recognition of ground objects.

Robust two-dimensional PCANet with F-norm distance metric

The principal component analysis network (PCANet) is a simplified convolutional neural network (CNN) implemented using a deep subspace learning model. Since its typical PCA filters utilize the squared Frobenius-norm (F-norm) as the distance metric, it exhibits extreme sensitivity to outliers hidden within the data. Furthermore, PCANet faces challenges in extracting structural information in the row and column directions of images as the filters need to be pre-vectorized. To address these issues, we propose a robust bi-directional two-dimensional PCANet (RBDPCANet), wherein the filters utilize the F-norm instead of the squared F-norm to mitigate the impact of outliers. Additionally, the relationship between reconstruction errors and projection distances is simultaneously considered in the objective function. After a concise analytical description of the PCANet concept and related work, we present the methodology for generating three convolutional kernels in the form of RBDPCANet-1, RBDPCANet-2, and RBDPCANet-3 algorithms. These convolution kernels are iteratively obtained and are suitable for numerical implementation. Each of the three algorithms uses another optimization objective function. It has been shown that in image recognition tasks, the developed methods are characterized by better resistance to noise compared to the classic PCANet approach. The rotation invariance and convergence of RBDPCANet are demonstrated according to theoretical principles. The efficacy of the proposed method is validated using images from publicly available datasets. Extensive experimental results on these datasets demonstrate that the proposed RBDPCANet not only significantly boosts the efficiency of image classification but also has strong resistance to occlusions, changes in lighting, changes in orientation, etc. Our implementation code is available at https://github.com/Wesley-li1/RBDPCANet-.

Dynamic Behavior Assessment of OC4 Semi-submersible FOWT Platform Through Morison Equation

This paper proposes an effective inertia coefficient (EIC) in the Morison equation for better wave-force calculations. The OC4 semi-submersible floating offshore wind turbine (FOWT) platform was considered to test the feasibility. Large diffraction at large Keulegan– Carpenter (KC) numbers and the interaction between columns can result in errors in estimating the wave force using the Morison equation with a theoretical inertia coefficient, which can be corrected by the EIC as a function of the wave period and direction. The horizontal and vertical wave forces were calculated using the Morison equation and potential theory at each column, wave period, and wave direction. The EICs of each column were then obtained, resulting in a minimal difference between the Morison inertia force and the wave excitation force by the potential theory. The EICs, wave forces, phase angles, and dynamic motions were compared to confirm the feasibility of an EIC concept under regular and random waves.

Experimental and numerical simulation study of mechanical properties of inner sleeve T-joint in modular gymnasia

In response to the contradiction between the insufficient number and unreasonable distribution of stadiums in cities and the increasing demand of people for fitness, the authors propose movable modular gymnasiums. This gymnasium takes advantage of the modular building, which can be quickly built, dismantled, and reassembled to fully utilize the temporary empty land in the city to meet the fitness demand of the people. However, the box modules bear a huge roof load, which puts higher demands on the performance of the modular units and their connecting joints. This study proposes a novel joint using an inner sleeve and bolted connections. The proposed connection uses an inner sleeve and bolts to connect the upper and lower columns of the modular unit to give it a higher bearing capacity. Bolts connect the lower unit's ceiling beam and the upper unit's floor beam to provide a better overall performance of the modular unit. The authors conducted experimental studies of 2 full-scale models and 24 finite-element parametric analyses. The results show that the welding quality determines the beam-ward bearing capacity of the joints, while it has little effect on the column-ward bearing capacity. In practical engineering, the authors suggest that the initial stiffness of the joints does not consider the contribution of the inner sleeve. The setting of stiffening ribs forces the inner sleeve to deform and force, improving the joints' mechanical properties, with the initial stiffness in the column direction increase of 25 %, the initial stiffness in the beam direction increase of 31 %, and a bearing capacity in the beam direction increase of 38 %. The design thickness of the inner sleeve and stiffening ribs should be close to the wall thickness of the modular beam and column. The results of this paper contribute to the development of fitness for the whole population and the expansion of the application of modular buildings.

A Row Displacement Correction Algorithm for High-speed and Accurate Terahertz Raster Scanning Imaging

Terahertz time-domain spectroscopic imaging has become a common and useful technique for various interdisciplinary studies. Raster scan is still the most general and reliable imaging modality due to the lack of matrix detectors. In terahertz raster scanning imaging, row displacements occur due to the use of a two-way continuous scanning mode, the lack of delay line triggering, or other technical factors. The displacements result in image distortion which significantly affects the subsequent data analysis. In this article, a row displacement correction algorithm based on a genetic algorithm is proposed. The algorithm corrects images based on the principle that row displacements result in an increase of high-frequency components of the Fourier spectrum transformed along the image column direction. The algorithm performance is comprehensively verified by both numerical simulation and experimental data, showing a promising effectiveness and robustness on different sample types, shapes, orientations, and spatial distributions. The algorithm corrects the displacements numerically regardless of the causing factors, offering a wide adaptability for various THz systems to improve the scanning efficiency and image quality.

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.

A Modified 2D-Checksum Error Detecting Method for Data Transmission in Noisy Media

In data transmission a change in single bit in the received data may lead to miss understanding or a disaster. Each bit in the sent information has high priority especially with information such as the address of the receiver. The importance of error detection with each single change is a key issue in data transmission field.The ordinary single parity detection method can detect odd number of errors efficiently, but fails with even number of errors. Other detection methods such as two-dimensional and checksum showed better results and failed to cope with the increasing number of errors.Two novel methods were suggested to detect the binary bit change errors when transmitting data in a noisy media.Those methods were: 2D-Checksum method and Modified 2D-Checksum. In 2D-checksum method, summing process was done for 7×7 patterns in row direction and then in column direction to result 8×8 patterns. While in modified method, an additional parity diagonal vector was added to the pattern to be 8×9. By combining the benefits of using single parity (detecting odd number of error bits) and the benefits of checksum (reducing the effect of 4-bit errors) and combining them in 2D shape, the detection process was improved. By contaminating any sample of data with up to 33% of noise (change 0 to 1 and vice versa), the detecting process in first method was improved by approximately 50% compared to the ordinary traditional two dimensional-parity method and gives best detection results in second novel method

Forced oscillation experiments for each vibration mode of closely spaced parallel pipes in steady flow

In the industrially widespread use of multiple bundled pipes, the effects of mutual interference, in addition to the vibrations generated in single columns, must be considered, and estimating their behavior is critical to the design. In this study, based on previous research that identified the vibration modes occurring in two bundled pipes placed in a steady flow, such as (1) translational motion in the crossflow direction, (2) approaching and moving away, and (3) torsional motion, we developed four types of oscillators that can emulate the oscillation patterns (mode (2) uses different mechanisms for in-line and crossflow direction oscillation.), conducted forced oscillation experiments, and measured fluid forces. The mechanism of generation of the behavior patterns, measured in the towing and vibration measurement experiments of the elastic body model, was examined from the viewpoint of fluid force. A database of added mass and linear damping coefficients with parameters of distance between the center of the column and flow direction, in addition to oscillation patterns, was compiled for the development of a future two-tube behavior estimation program.A comparison of the measured fluid force of two columns with that of a single column, obtained by forced oscillation experiments for three vibration modes, showed that two closely spaced columns may vibrate over a wider frequency band and have larger amplitudes than a single column because of the expanded region of negative linear damping coefficients. Based on the results of the fluid force measurements for each vibration mode, rotational motion is more likely to occur in the tandem configuration, whereas approaching and moving away motion is more likely to occur in the side-by-side configuration because of the interchanged positive and negative added mass coefficients and strength of the negative linear damping coefficient.

Error budget analysis of geocoding and geometric correction for KOMPSAT-5 SAR imagery

ABSTRACT Geocoding geometrically rectifies a remote sensing image according to a specific map projection, and is an essential process for utilizing synthetic aperture radar (SAR) satellite images. The accuracy of geocoding is affected by various intercorrelated error sources. In this study, we propose a framework for improving the geocoding accuracy of SAR images. Our framework consists of two major theoretical and computational steps: 1) calculating and setting the error budget of the SAR image geocoding accuracy and checking their quality and 2) applying a geometric correction model if the quality is lower than the predefined threshold. Error budget analysis was performed by utilizing the law of variance propagation, considering the correlations among the primary error sources. During the second (geometric correction) step, the non-multicollinearity (N-MC) model, a ground control point (GCP)-based geometric correction model without multicollinearity, was proposed. Experiments were conducted using two KOMPSAT-5 SAR images from Daejeon City, Korea to verify the framework. The geocoding accuracy of SAR images #1 and #2 exceeded the error budgets of all confidence levels, except for the row direction of SAR image #2, despite the vendor’s internal calibration. In the second step, two SAR images were geometrically corrected by applying the N-MC model. The use of geometric correction improved the geocoding accuracy of the two SAR images by approximately two to five pixels in the row and column directions. The final geocoding accuracy of the SAR images was within the error budget.

Pixel Centroid Characterization with Laser Speckle and Application to the Nancy Grace Roman Space Telescope Detector Arrays

The Nancy Grace Roman Space Telescope will use its wide-field instrument to carry out a suite of sky surveys in the near-infrared. Several of the science objectives of these surveys, such as the measurement of the growth of cosmic structure using weak gravitational lensing, require exquisite control of instrument-related distortions of the images of astronomical objects. Roman will fly new large-format (4 × 4 k) Teledyne H4RG-10 infrared detector arrays. This paper investigates whether the pixel centroids are located on a regular grid by projecting laser speckle patterns through a double slit aperture onto a non-flight detector array. We develop a method to reconstruct the pixel centroid offsets from the stochastic speckle pattern. Due to the orientation of the test setup, only x-offsets are measured here. We test the method both on simulations, and by injecting artificial offsets into the real images. We use cross-correlations of the reconstructions from different speckle realizations to determine how much of the variance in the pixel offset maps is signal (fixed to the detector) and how much is noise. After performing this reconstruction on 64 × 64 pixel patches, and fitting out the best-fit linear mapping from pixel index to position, we find that there are residual centroid offsets in the x (column) direction from a regular grid of 0.0107 pixels rms (excluding shifts of an entire row relative to another, which our speckle patterns cannot constrain). This decreases to 0.0097 pix rms if we consider residuals from a quadratic rather than linear mapping. These rms offsets include both the physical pixel offsets, as well as any apparent offsets due to crosstalk and remaining systematic errors in the reconstruction. We comment on the advantages and disadvantages of speckle scene measurements as a tool for characterizing the pixel-level behavior in astronomical detectors.

Consistent Distribution of Irrigation Water on Vertical Gardens (With Water As Content Sample)

As the population increases and infrastructure development continues to increase, the need for green open space is decreasing. A vertical garden is an efficient solution with a vertically modern garden concept that the functions of green open spaces can be fulfilled. The irrigation system in a vertical garden gives a major role because plants can be grown well if they have a great irrigation system. Drip irrigation is one type of irrigation system that is widely applied and the best method that has been used in the world because it is considered more efficient with a small discharge, can reduce runoff, and can reduce water loss due to evaporation. This study aims to analyze the uniform of water distribution in the drip irrigation system and the average variation of water distribution in the row and column direction using a statistical test with Kruskal-Wallis. Based on the statistical analysis, it is obtained the coefficient of variation (Cv) of 0.27, the uniformity statistic (Us) of 73.2%, and the coefficient of uniformity (Uc) of 71.7% which means that the three parameters are categorized as good and acceptable while for statistical tests Kruskal-Wallis has a difference in the average variation of water distribution for the row and column groups.

RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.

Multiple-image encryption algorithm based on the dice-rotation model and improved chaos

In the digital age, network transmission of batch images is increasingly frequent. To prevent the information leak of image content and improve the security of encryption during the network transmission, we design an improved chaotic system, establish a dice-rotation model, and propose a multiple-image encryption algorithm. First, k plain images are combined into an image cube; second, the image cube is divided into dice blocks (i.e., small image cube); third, the dice blocks are rotated and scrambled according to the designed rules, which are controlled by chaotic sequences; finally, the forward addition and modular operations in the row and column directions are performed by the improved chaos. Experimental results and algorithm analyses indicate that the proposed algorithm can encrypt multiple interactive images at once with desirable encryption effect and have strong security.

LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network’s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network’s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images.

Covariance Estimation for Matrix-valued Data

Covariance estimation for matrix-valued data has received an increasing interest in applications. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, we propose a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure. Under these conditions, the original covariance matrix is decomposed into a Kronecker product of two bandable small covariance matrices representing the variability over row and column directions. We formulate a unified framework for estimating bandable covariance, and introduce an efficient algorithm based on rank one unconstrained Kronecker product approximation. The convergence rates of the proposed estimators are established, and the derived minimax lower bound shows our proposed estimator is rate-optimal under certain divergence regimes of matrix size. We further introduce a class of robust covariance estimators and provide theoretical guarantees to deal with heavy-tailed data. We demonstrate the superior finite-sample performance of our methods using simulations and real applications from a gridded temperature anomalies dataset and an S&P 500 stock data analysis. Supplementary materials for this article are available online.

Shank length and circumference measurement algorithm of breeder chickens based on extraction of regional key points

The phenotypic parameters of breeder chickens are the external expression of their genetic genes. Accurate and efficient acquisition of chicken phenotypic parameters is a key technology in poultry breeding. However, the traditional manual measurement method is time-consuming and laborious, and the measurement error is large, which seriously affects the effect and efficiency of poultry breeding. Therefore, in this paper, Xinghua chicken as the research object, combining computer vision technology and deep learning algorithm, proposed the shank length and circumference measurement algorithm (SLCM) of breeder chickens. The algorithm firstly detected and extracted the region of chicken claw and shank bottom base by YOLOV5 algorithm. And then extracted the key points of shank length and circumference measurement based on the pixel distribution features of row direction and column direction in this region, and finally calculated the values of shank length and circumference. The test results of SLCM algorithm show that compared with the traditional manual measurement method, the Pearson correlation coefficient of shank length and circumference measured by algorithm is 0.8722 and 0.8869. Besides, this algorithm not only has smaller standard deviation (standard deviation of shank length is 1.35 mm, and shank circumference is 0.25 mm) and coefficient of variation (the mean coefficient of variation of shank length is 0.02, maximum is 0.06; and the mean coefficient of variation of shank circumference is 0.01, maximum is 0.03), but also has higher measurement efficiency. This research provided reference and basis for the accurate measurement of phenotypic parameters of breeder chickens and had important significance for poultry breeding.