Vertical Projection Research Articles

Breaking Free from Field Layers: The Interest of Post-excavation Stratigraphies (PES) for Producing Reliable Archaeological Interpretations and Increasing Chronological Resolution

In order to track diachronic changes in archaeological sequences, researchers typically partition time into stratigraphic layers defined during fieldwork, which serve as the framework for ensuing analyses. These analytical units have a significant impact on archaeological inference, defining its resolution, and influencing both the study of cultural assemblages and the reconstruction of past environments. However, field layers are seldom re-evaluated after excavation despite the fact that archaeological deposits are now commonly recognised as often containing material ‘mixed’ together by site formation processes, excavation techniques, or analytical practices. Although the analysis of intra-site spatial data clearly offers a means to overcome these issues, our literature review of 192 journal articles revealed the potential of this data (notably vertical projections of piece-plotted artefacts) to be under-exploited in prehistoric archaeology. Here, we advocate for the development of a more spatially informed framework for interpretation that we refer to as post-excavation stratigraphy or PES. After proposing a definition for PES, we attempt to develop a framework for theoretical considerations underlying their implication, importance, and potential. Three main benefits of PES are highlighted: ensuring assemblage reliability, increased chronological and spatial resolution, and more reliable interpretations based on a multi-stratigraphic approach. We contend that the stratigraphy defined during fieldwork is insufficient and potentially misleading. By providing a different “stratigraphic view” of the same sequence, each specialist can contribute data that, when combined, produces a better understanding of interactions between changes in, for example, technological or cultural traditions, subsistence strategies, or paleoenvironments.

Handwriting-Based Text Line Segmentation from Malayalam Documents

Optical character recognition systems for Malayalam handwritten documents have become an open research area. A major hindrance in this research is the unavailability of a benchmark database. Therefore, a new database of 402 Malayalam handwritten document images and ground truth images of 7535 text lines is developed for the implementation of the proposed technique. This paper proposes a technique for the extraction of text lines from handwritten documents in the Malayalam language, specifically based on the handwriting of the writer. Text lines are extracted based on horizontal and vertical projection values, the size of the handwritten characters, the height of the text lines and the curved nature of the Malayalam alphabet. The proposed technique is able to overcome incorrect segmentation due to the presence of characters written with spaces above or below other characters and the overlapping of lines because of ascenders and descenders. The performance of the proposed method for text line extraction is quantitatively evaluated using the MatchScore value metric and is found to be 85.507%. The recognition accuracy, detection rate and F-measure of the proposed method are found to be 99.39%, 85.5% and 91.92%, respectively. It is experimentally verified that the proposed method outperforms some of the existing language-independent text line extraction algorithms.

Effect of short duration plyometric training on vertical jump and sprint speed in volleyball players

INTRODUCTION: Volleyball is a fast-paced sport that involves vertical and horizontal projections of the ball by the player. The use of plyometric training is a popular method to enhance performance in players. OBJECTIVE: The present study aims to see the effect of short-duration plyometric training on vertical jump and sprint speed in volleyball players. METHOD: This study is a parallel group randomized controlled trial. Thirty-nine participants were screened for participation in the study, of which 30 were selected following the inclusion and exclusion criteria for the study. The inclusion criteria for the study comprised of volleyball players of age group 18 to 24 years, both males and females, with playing experience of at least one year, and willing to participate. The exclusion criteria for the study included players with a history of injury in the last six months, any reported neurological or musculoskeletal disorder, and any psychological condition. The selected participants were randomly allocated to Group A and Group B using the lottery method. Group A was given plyometric training for three days a week and Group B was asked to continue their regular routine exercises and playing schedule. The total duration of the intervention was four weeks. The outcome measure for the study was sprint speed and vertical jump height. Sprint speed was measured by the 20 m sprint test, and the vertical jump height was measured by the Sargent jump test. The assessment was done at the baseline and at the end of four weeks. An unpaired t-test was used to analyze between-group differences, and paired t-test was used to analyze the within-group differences in the outcome variables. The significance level was set as p<0.05. RESULTS: The result of the study showed a significant improvement in vertical jump height (MD= -7.133, 95% CI-12.657, -1.609) and no significant improvement in sprint speed when between-group comparisons were made (MD=0.084, 95%CI -0.177, -.345) with an effect size of 0.75 for vertical jump height. The within-group comparison was found significant only for vertical jump height in Group A, and there was no significant improvement was found in sprint speed for both Group A and B. FINAL CONSIDERATIONS: The short-duration plyometric training is effective in improving the vertical jump height and not the sprint speed in volleyball players. The specificity of plyometric training is important for optimal improvement in sports performance.

Vertical projections in the Heisenberg group via cinematic functions and point-plate incidences

Let {πe:H→We:e∈S1} be the family of vertical projections in the first Heisenberg group H. We prove that if K⊂H is a Borel set with Hausdorff dimension dimH⁡K∈[0,2]∪{3}, thendimH⁡πe(K)≥dimH⁡K for H1 almost every e∈S1. This was known earlier if dimH⁡K∈[0,1].The proofs for dimH⁡K∈[0,2] and dimH⁡K=3 are based on different techniques. For dimH⁡K∈[0,2], we reduce matters to a Euclidean problem, and apply the method of cinematic functions due to Pramanik, Yang, and Zahl.To handle the case dimH⁡K=3, we introduce a point-line duality between horizontal lines and conical lines in R3. This allows us to transform the Heisenberg problem into a point-plate incidence question in R3. To solve the latter, we apply a Kakeya inequality for plates in R3, due to Guth, Wang, and Zhang. This method also yields partial results for Borel sets K⊂H with dimH⁡K∈(5/2,3).

Nitrogen addition changes the canopy biological characteristics of dominant tree species in an evergreen broad-leaved forest

Many studies have focused on the impact of nitrogen deposition on plants, but due to technical limitations, research on the responses of forest canopy to manipulated nitrogen deposition is relatively scarce. Based on a canopy nitrogen addition (CN) platform, this study used laboratory analysis and unmanned aerial vehicle (UAV) observations to assess the impact of CN on the canopy traits of dominant tree species (Engelhardia roxburghiana, Schima superba, and Castanea henryi) in an evergreen broad-leaved forest in China. The results showed that nitrogen application at 25 kg N ha−1 y−1 (CN25) and 50 kg N ha−1 y−1 (CN50) significantly increased the actual net photosynthetic rate (An) of all the three tree species. CN25 significantly increased superoxide dismutase (SOD), catalase (CAT), and ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activities in C. henryi. CN50 significantly increased the leaf area of all the three tree species and significantly reduced the leaf thickness of C. henryi, and significantly increased the POD and Rubisco activities in S. superba and C. henryi. CN significantly changed the number of forest gaps, but did not significantly change the area of forest gaps within the sample plots. CN25 significantly decreased the vertical projection area but increased the canopy flowering coverage of S. superba in dominant directions. CN25 and CN50 significantly increased the flowering coverage of C. henryi in favorable directions. It is found that under long-term (10-year) nitrogen addition, the balance between carbon fixation and antioxidant defense functions of E. roxburghiana may be broken down, but the carbon assimilation, antioxidant capacity and reproduction potential of S. superba and C. henryi may be well coordinated, which will have a potential impact on the species composition and ecological functions of the evergreen broad-leaved forest. This study may also provide scientific basis for forest management in the context of enhanced atmospheric nitrogen deposition.

Navigation Line Extraction Method for Broad-Leaved Plants in the Multi-Period Environments of the High-Ridge Cultivation Mode

Navigation line extraction is critical for precision agriculture and automatic navigation. A novel method for extracting navigation lines based on machine vision is proposed herein using a straight line detected based on a high-ridge crop row. Aiming at the low-level automation of machines in field environments of a high-ridge cultivation mode for broad-leaved plants, a navigation line extraction method suitable for multiple periods and with high timeliness is designed. The method comprises four sequentially linked phases: image segmentation, feature point extraction, navigation line calculation, and dynamic segmentation horizontal strip number feedback. The a* component of the CIE-Lab colour space is extracted to preliminarily extract the crop row features. The OTSU algorithm is combined with morphological processing to completely separate the crop rows and backgrounds. The crop row feature points are extracted using an improved isometric segmented vertical projection method. While calculating the navigation lines, an adaptive clustering method is used to cluster the adjacent feature points. A dynamic segmentation point clustering method is used to determine the final clustering feature point sets, and the feature point sets are optimised using lateral distance and point line distance methods. In the optimisation process, a linear regression method based on the Huber loss function is used to fit the optimised feature point set to obtain the crop row centreline, and the navigation line is calculated according to the two crop lines. Finally, before entering the next frame processing process, a feedback mechanism to calculate a number of horizontal strips for the next frame is introduced to improve the ability of the algorithm to adapt to multiple periods. The experimental results show that the proposed method can meet the efficiency requirements for visual navigation. The average time for the image processing of four samples is 38.53 ms. Compared with the least squares method, the proposed method can adapt to a longer growth period of crops.

A laminar model for the joint development of ocular dominance columns and CO blobs in the primary visual cortex

In this paper, we present a multi-layer, activity-dependent model for the joint development of ocular dominance (OD) columns and cytochrome oxidase (CO) blobs in primate V1. For simplicity, we focus on layers 4C and 2/3 with both layers receiving direct thalamic inputs and layer 4C sending vertical projections to layer 2/3. Both the thalamic and the vertical connections are taken to be modifiable by activity. Using a correlation-based Hebbian learning rule with subtractive normalization, we show how the formation of an OD map in layer 4C is inherited by layer 2/3 via the vertical projections. Competition between these feedforward projections and the direct thalamic input to layer 2/3 then results in the formation of CO blobs superimposed upon the ocular dominance map. The spacing of the OD columns is determined by the spatial profile of the intralaminar connections within layer 4, while the spacing of CO blobs depends both on the width of the OD columns inherited from layer 4 and the spatial distribution of intralaminar connections within the superficial layer. The resulting CO blob distribution is shown to be consistent with experimental data. In addition, we numerically simulate monocular deprivation and find that while the CO blob distribution is unaltered, the OD pattern undergoes modification. The OD stripes of the deprived eye narrow, whereas the OD stripes for the remaining open eye widen.

Automatic localization and classification of intervertebral disc herniation using hybrid classifier

Lumbar disc herniation detection is challenging due to numerous problems, including a complex background, and small joints. Deep learning-based methods have set new benchmarks for much computer vision and pattern recognition research throughout the last five years. In this proposed work, a novel method for the localization of horizontal and vertical projection of Intervertebral Discs (IVDs) in lumbar spine MRI images based on YOLOv2-IVD (You only look once v2-Intervertebral Disc) is suggested, which includes Enhanced Visual Geometry Group 16 (EVGG16) as the YOLOv2 model's backbone. In this framework, features are taken from the ReLU 11, which is the output layer of EVGG16 modeland fed into the YOLOv2 model. Following that, the exact intervertebral discs are detected by finding the intersection of the IVDs localized horizontal and vertical projections, from which IVDs features are learned using Enhanced AlexNet (EAlexNet). It includes 8 weight layers and kernels 5×5,3×3 for classifying IVDs as normal or herniated, a process also known as disc herniation classification. Finally, the severity of the herniation, such as prolapse (mild) and extrusion (moderate), is determined by estimating the length of the intervertebral herniation disc, which has been achieved using basic image processing techniques such as thresholding and morphological segmentation methods. The proposed method is trained and validated using a real-world spine MRI image dataset obtained from a hospital. Also the results reveal that the proposed approach, attains a high accuracy of 93.59% for herniation detection, comfortably outperformed other state-of-the-art methodologies.

Analysis of Using Multi-Angle Conventional Ultrasound Scanning for Efficient 3-D Object Imaging

The purpose of this work is to examine the possibility of using multi-angle conventional ultrasound B-mode scanning in efficient 3-D imaging. In the paper, the volume of an object is reconstructed from vertical projections registered at fixed angular positions of the multi-element linear ultrasonic probe rotated in relation to the object submerged in water. The possible configurations are: vertical lateral, vertical top or vertical bottom. In the vertical lateral configuration, the ultrasonic probe acquires 2-D images of object’s vertical cross-sections, turning around its lateral surface. In the vertical top or bottom configuration, the ultrasonic probe acquires 2-D images of the object’s vertical cross-sections, turning on the horizontal plane over the top or under the bottom surface of the object. The method of recording 3-D volume of an object’s structure and reconstruction algorithm have been designed. Studies show the method in the vertical top or bottom configuration could be successfully applied to the effective 3-D visualisation of the structure of the female breast in vivo as the new complement ultrasonic imaging modality in the prototype of the developed ultrasound tomography scanner.

The Application of Image Processing for Conversion of Handwritten Mathematical Expression

Abstract Optical character recognition (OCR) is the conversion of printed or written text from a scanned document or image file into a machine-readable form to be used for data processing like editing. Handwriting has been a way of communication for centuries, but modern technology has made it easier with the introduction of modern computers. While people have adapted to typing out words using a keyboard, formulas and mathematical expressions requires additional add-ons installed in the word processor. The process can be time consuming and tedious. Therefore, an alternative method is proposed in this paper in which handwritten mathematical formulas are converted into computer readable text. Horizontal and vertical projection is used for segmentation while convolutional neural network for character recognition is used to increase the recognition accuracy. The proposed method was able to segment out handwritten mathematical equations from lined papers as well as extract out and identify each character written. The handwritten equation was then successfully converted to a digital format.

Corn Row Navigation Line Extraction Method Based on the Adaptive Edge Detection Algorithm

Aiming at the problems of long detection time and large detection error when agricultural machinery extracts corn row navigation lines, a method of corn row navigation line extraction based on the adaptive edge detection algorithm is proposed. First, the improved super-green feature algorithm and the maximum inter-class variance method are used to automatically obtain the green feature binary image, and the morphological processing is used to improve the image quality, determining dynamic regions of interest by constraining pixel thresholds, extraction of corn edge contour using adaptive edge detection algorithm, finally, the feature points are fitted by the Theil-Sen estimation method. Experimental results show: the super-green feature algorithm reflects the green content in the image more realistically, using the adaptive edge detection algorithm to extract corn row features, the accuracy rate is 94%, and the processing time of a single frame image is 104ms. Compared with the Hough algorithm extraction and the vertical projection algorithm, the navigation line extraction accuracy is increased by 15% and 8% respectively, and the time-consuming is reduced by 258ms and 150ms respectively. In addition, the stability of the algorithm is analyzed in different environments, all with good timeliness.

DIFFERENCES IN KINEMATIC PARAMETERS BETWEEN FIRST AND SECOND SERVES IN TENNIS BY ELITE PLAYERS OF THE GERMAN LEAGUE

The primary goal of this research was to determine the differences in kinematic parameters between the first and second serves in tennis among the elite players of the German league. The sample of respondents in this research consisted of 30 elite male senior players of the German League who have "Leistungklasse" from 1 to 7. The kinematic parameters that were analyzed are: Height of ball impact (KPVUL), Racket speed (KPBRE), Ball speed (KPBLO), The angle at the elbow joint of the arm that hits the ball (KPULZ), Oscillation of the vertical projection of the center of gravity of the body (KPOVP), Jump height (KPVSK), Ball launch height (KPVIL), The point of contact between the ball and the racket "sweetspot" (KPMLR), Ball rotation "Top spin" (KPRLO). The video recording required for kinematic analysis was acquired with two Casio EX-F1 digital cameras at a frequency of 200 images per second. The camera was placed at an angle of 900 in relation to the recording plane, and they were at a distance of 10 m. Space calibration was performed with the help of a calibration frame (200 x 200 cm). We have installed Zepp Tennis Smart Sensor 2.0 in the handle of the Wilson Pro Staff RF97 Autograph racket, using the Zepp Tennis application software. "Kinovea" software was used for service analysis and obtaining kinematic parameters from video recordings. By analyzing the results of the T-test for independent samples, it can be seen that there are statistically significant differences in five of the nine analyzed kinematic parameters : KPBRE – kinematic parameter of racket speed (Sig.=.000), KPBLO – kinematic parameter of ball speed (Sig.=.000), KPULZ – kinematic parameter angle in the elbow joint (Sig.=.000), KPMLR – kinematic parameter point of contact between the ball and the racket (Sig.=.000), as well as a variable KPRLO – kinematic parameter of ball rotation (Sig.=.000). Larger numeric values in variables: KPVUL – kinematic parameter ball impact height, KPOVP – kinematic parameter of oscillation of the vertical projection of the center of gravity of the body, KPVSK – kinematic parameter jump height and KPVIL – kinematic parameter ball impact height, show a difference but it is not enough to be statistically different. Key words: tennis, kinematic analysis, racket speed, Zepp sensor

Maize crop row recognition algorithm based on improved UNet network

Aiming at the problem that it is difficult to identify maize crop row centerlines in complex farmland environments such as high weeds, row breaking, and leaf adhesion under different growth periods, this study proposes a centerline detection algorithm based on a improved UNet network. The UNet network – a traditional semantic segmentation network – was enhanced to create the Atrous Spatial Pyramid Pooling UNet (ASPP-UNet) network for maize crop row and background segmentation, and the improved vertical projection method was subsequently employed to measure the crop rows’ feature points. Finally, the least squares method was used to fit the centerlines. Experimental results yielded the Mean Intersection Over Union, Mean Pixel Accuracy, Mean Precision, and Mean Recall metrics of ASPP-UNet network to be 83.23%, 90.18%, 91.79%, and 90.18% respectively. These figures represent respective increases of 10.03%, 11.86%, 9.43% and 11.24% compared to the Fully Convolutional Network (FCN), and 7.80%, 5.52%, 2.71%, and 5.52% compared to the UNet. Furthermore, the average fitting time and angle error of the improved vertical projection method combined with the least square method were reduced to 66 ms and 4.37°, compared to 80 ms and 6.12°in the traditional vertical projection method, and 86 ms and 5.67°in the left and right edge centerline method. Likewise, the accuracy of proposed method increased to 92.59%, compared to 87.21% and 90.16% of the two aforementioned methods, respectively. Therefore, the proposed method successfully meets the accuracy and real-time demands of agricultural robot vision navigation, and can function effectively under varying environmental pressures.

Geophysical Characterization and Attenuation Correction Applied for Hydrate Bearing Sediments in Japan Sea

Estimation of rock properties from seismic data is important for exploration and production activities in the petroleum industry. Considering the compressional velocity—the speed of propagating body waves in formations—and the quality factor (Q)—a measure of the frequency-selective energy losses of waves propagating through formations—both properties are usually estimated from multichannel seismic data. Velocity is estimated during multichannel processing of seismic reflection data in either the time or depth domain. In marine seismic acquisition, Q can be estimated from the following sources: Vertical Seismic Profile (VSP) surveys, where sources are located near the sea surface and geophones are distributed at depth along a borehole; and multichannel reflection data, where sources are also located near the sea surface and receivers are distributed either at the sea surface (conventional seismic survey with streamers) or on the sea floor (use of nodes or Ocean Bottom Cables (OBC)). The aforementioned acquisition devices, VSP, conventional streamers, nodes, and OBCs are much more expensive than single-channel acquisition with one receiver per shot due to the cost of operation. There are numerous old and new datasets from academia and the oil industry that have been acquired with single-channel acquisition devices. However, there is a paucity of work addressing the estimation of velocity and Q from this type of equipment. We investigate the estimation of Q and velocity from single-channel seismic data. Using the windowed discrete Fourier transform for a single seismic trace, we calculate the peak and dominant frequency that changes with time. In the geologic environment, higher frequencies are attenuated at shallow depths (time), while lower frequencies remain at deeper positions. From the rate at which higher frequencies are attenuated with time, we estimate the effective quality factor (Qeff). However, when using Kirchhoff migration to process single-channel seismic data, events far from the vertical projection of the receiver contribute to the trace at a given time. Then, an underestimation of the effective quality factor occurs. To compensate for the effects of more distant events with lower-frequency content contaminating the shorter events, we propose a linear equation to correct the effective quality factor estimated from migrated seismic data. Effective Q and its correction are estimated in five single-channel seismic lines surveyed along the Joetsu Knoll, a SW-NE anticline structure on the eastern margin of the Sea of Japan. These results are linked to geomorphological and geological features and the velocity field. Joetsu Knoll is a known site of massive gas hydrates (GH), which occur in the first hundred metres of Neogene sediments and, together with gas chimneys, play an important role in seismic wave absorption. Qeff estimated from migrated seismic data maintains the spatial relationship between high and low Q regions. The region of low Q, which is below 124 and has an average value of 57, occurs near the anticlinal hinge and tends to coincide with the region in which the Bottom Simulating Reflector (BSR) resides. The coexistence of GH and free gas coincides with the very low P velocity gradient of 0.225 s−1. BSR occurrence, Qeff and the geometry of the Joetsu anticline testify to progressive gas hydrate depletion northward along the dome.

From 2D to 3D

Mid-air text entry on virtual keyboards suffers from the lack of tactile feedback, which brings challenges to both tap detection and input prediction. In this paper, we explored the feasibility of single-finger typing on virtual QWERTY keyboards in mid-air. We first conducted a study to examine users' 3D typing behavior on different sizes of virtual keyboards. Results showed that the participants perceived the vertical projection of the lowest point on the keyboard during a tap as the target location and inferring taps based on the intersection between the finger and the keyboard was not applicable. Aiming at this challenge, we derived a novel input prediction algorithm that took the uncertainty in tap detection into a calculation as probability, and performed probabilistic decoding that could tolerate false detection. We analyzed the performance of the algorithm through a full-factorial simulation. Results showed that the SVM-based probabilistic touch detection together with a 2D elastic probabilistic decoding algorithm (elasticity = 2) could achieve the optimal top-5 accuracy of 94.2%. In the evaluation user study, the participants reached a single-finger typing speed of 26.1 WPM with 3.2% uncorrected word-level error rate, which was significantly better than both tap-based and gesture-based baseline techniques. Also, the proposed technique received the highest preference score from the users, proving its usability in real text entry tasks.

Improving the Size of the Propellers of the Parrot Mini-Drone and an Impact Study on its Flight Controller System

Unmanned Aerial Vehicles (UAVs) are widely used in transportation, delivery, surveillance and surveillance applications. The development of stable, resilient, and accurate flight based on turbulence and turbulence will likely become a key feature in the development of unique flight control systems. In this research, we studied the control system of a small Parrot mini drone, the Mambo drone, which was designed using the MATLAB program, while we added turbulence to the drone by changing the weight of the original plane in the design, where we increased the weight and calculated the vertical projection area of the propellers of the plane several times until we got the best space for the propellers able to carry more extra weight. We imposed an increase in the drone's weight due to bad conditions that the plane experienced during its flight, such as snow or dust falling on it. In order to make the aircraft bear these weather conditions without falling and colliding, we calculated an appropriate increase in the area of the aircraft wing, and we actually applied it in the MATLAB-R2021a Simulink program, and we got good results using simulation as well as in real-time inside the laboratory, turbulence was added in the simulation program. The new design of the propellers demonstrated the aircraft's ability to carry an additional payload of approximately one-third of the aircraft's weight, as shown in the roads chapter. In future work, we propose to use this design on larger aircraft with fixed propellers and to study the effects of other weather conditions on UAVs, such as the effect of temperature, humidity, and others.

Design and Experiment of an Automatic Row-Oriented Spraying System Based on Machine Vision for Early-Stage Maize Corps

Spraying pesticides using row alignment in the maize seedling stage can effectively improve pesticide utilization and protect the ecological environment. Therefore, this study extracts a guidance line for maize crops using machine vision and develops an automatic row-oriented control system based on a high-clearance sprayer. First, the feature points of crop rows are extracted using a vertical projection method. Second, the candidate crop rows are obtained using a Hough transform, and two auxiliary line extraction methods for crop rows based on the slope feature outlier algorithm are proposed. Then, the guidance line of the crop rows is fitted using a tangent formula. To greatly improve the robustness of the vision algorithm, a Kalman filter is used to estimate and optimize the guidance line to obtain the guidance parameters. Finally, a visual row-oriented spraying platform based on autonomous navigation is built, and the row alignment accuracy and spraying performance are tested. The experimental results showed that, when autonomous navigation is turned on, the average algorithm time consumption of guidance line detection is 42 ms, the optimal recognition accuracy is 93.3%, the average deviation error of simulated crop rows is 3.2 cm and that of field crop rows is 4.36 cm. The test results meet the requirements of an automatic row-oriented control system, and it was found that the accuracy of row alignment decreased with increasing vehicle speed. The innovative spray performance test found that compared with the traditional spray, the inter-row pesticide savings were 20.4% and 11.4% overall, and the application performance was significantly improved.

Application of machine learning (ML) and genetic algorithm (GA) to optimize window wing wall design for natural ventilation

The recent global pandemic has shown the importance of natural ventilation not only in reducing energy consumption but also in reducing airborne contamination. Long-established studies have demonstrated the advantages of window wing walls – vertical projections attached to windows that create a pressure change near the openings - to increase indoor wind flow. However, these conventional strategies to improve natural ventilation rarely take into account site-specific conditions such as dynamic wind conditions throughout the year.This research aims to find a design configuration for window wing walls to improve air circulation through careful interventions for the whole year in classrooms with conventional one-sided openings. With data-driven design, natural ventilation can be maximized to reduce the risk of contamination by insufficient fresh air.The paper utilizes the Computational Fluid Dynamics (CFD) simulations and Artificial Neural Networks (ANN) to predict indoor air movement with less computational time and load. Coupled with Genetic Algorithm (GA), the paper develops an approach that would enable increased natural ventilation in rooms with one-sided windows. Resultsshow an increase in indoor wind speed in the optimized case compared to the baseline conditions. The main contribution of this study is to demonstrate the use of advanced CFD simulation to provide designers and users with a site-specific configuration for installing fixed wing walls that can maximize indoor wind flow from different wind directions over the whole year, not just for one wind direction.

Comprehensive Evaluation and Comparative Analysis of the Green Development Level of Provinces in Eastern and Western China

Considering the green development initiatives vigorously promoted by China, this paper constructs an evaluation index system that covers six areas, including resource utilization, pollution control, living environment, ecological protection, circular economy, and quality of economic growth. This paper also establishes an improved comprehensive evaluation model by using the method of Vertical Projection Distance-Set Pair Analysis in order to optimize the traditional method of Technique for Order of Preference by Similarity to Ideal Solution. Based on the official data released by China, this paper quantitatively analyzes the green development level of 21 provinces in eastern and western China in the aforementioned six areas from 2005 to 2020, and makes a regional comparison between eastern and western China. The results show that the level of green development in eastern China is significantly higher than that in western China. In 2020, when the research period ends, the comprehensive evaluation values of green development in all the eastern provinces, except Hebei, are higher than 4.0; meanwhile, no province in the western region has a comprehensive evaluation value exceeding 4.0 in 2020, and there is a large gap between the eastern region and the western region in areas such as economic growth quality and pollution control. On this basis, this paper puts forward relevant suggestions in terms of the coordinated green development of the eastern and western regions of China in the future.

Sugarcane Node Detection Method Based on Photoelectric Sensor Vertical Projection Signal Processing

Highlights A linear array CCD sensor is utilized to obtain the contour signal of the vertical projection of the sugarcane. A method is provided for continuously identifying and locating sugarcane nodes. Examines the impact of scan speed and illumination on the accuracy of identification. The method performs well regarding identification rate, precision, and efficiency. Abstract. In order to achieve continuous and dynamic detection of sugarcane nodes, improve the automatic production efficiency of pre-cut sugarcane seed, and lower the cost of mechanized sugarcane production, a detection method based on linear array charge-coupled device (CCD) photoelectric sensor signal processing was developed. Firstly, the mechanical drive unit was controlled to drive the photoelectric detection system to acquire the signal of the vertical projection of the sugarcane profile. The projection information was then binarized into profile information using the Otsu algorithm. The profile signal was then decomposed using a variable mode decomposition algorithm optimized based on the sparrow search algorithm, and the component reflecting the node content was regarded as the feature signal. Finally, the position of the wave peaks above the judgment threshold in the normalized feature signal was considered the position of the sugarcane nodes. One-way and two-way experiments were conducted to investigate the effects of scan speed and illuminance on identification precision. The results showed that the identification rate, average response time, and average error values were 98.40%, 0.13 s, and 1.36 mm at a scan speed of 75 mm/s and an illuminance of 91.91 lx. Compared to other node identification methods discussed in this article, the proposed method has a high identification rate and accuracy with a high response speed, which can improve the automation efficiency of sugarcane seed production. Keywords: Identification accuracy, Non-contact detection, Photoelectric sensor, Precision agriculture, Seed production, Signal processing, Sugarcane node, Variational mode decomposition.