Calculation Process Research Articles

Uncertainty analysis for free-space three-dimensional measurement of electromagnetic pulse

As the polarization direction is unknown in free-space three-dimensional (3D) electromagnetic pulse (EMP) measurement, the components in three directions are usually measured first, and then the total vector is calculated. The waveform (magnitude) and polarization direction define the 3D process. Because of the uncertainty produced in the component measurement, there is also uncertainty in the calculated 3D process. This paper investigates the propagation of uncertainty during the total vector calculation process. The magnitude and polarization angle uncertainty propagation formulas are derived through analysis. The results show that the uncertainty of the calculated magnitude is less than the maximal measurement uncertainty of the three components, and the uncertainty of the polarization angle is less than sqrt(2) times the maximal uncertainty of the three components divided by the magnitude of the measured field. Finally, a Monte-Carlo (MC) simulation is run to validate the results of the analysis. The simulation results agree well with the analysis results.

Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms

It is known that artificial intelligence algorithms are based on calculations performed using various mathematical operations. In order for these calculation processes to be carried out correctly, some types of data cannot be fed directly into the algorithms. In other words, numerical data should be input to these algorithms, but not all data in datasets collected for artificial intelligence algorithms are always numerical. These data may not be quantitative but may be important for the study under consideration. That is, these data cannot be thrown away. In such a case, it is necessary to transfer categorical data to numeric type. In this research work, 14 encoding methods of transforming of categorical data were considered. At the same time, conclusions are given about the general conditions of using these methods. During the research, categorical data in the dataset that were collected in order to assess whether it is possible to give credit to customers will be transformed based on 14 methods. After applying each encoding method, experimental tests are conducted based on the classification algorithm, and they are evaluated. At the end of the study, the results of the experimental tests are discussed and research conclusions are presented.

Design and fabrication of a Fresnel zone plate with an enhanced depth of focus

A Fresnel zone plate (EFZP) with an extended depth of focus can maintain focused monochromatic light at different distances compared to a general Fresnel zone plate (FZP). The focal distances are determined by dividing the zone plate into multiple areas based on the desired order. The EFZP has potential applications in various research fields such as microscopy, direct laser lithography, and optical coherence tomography. However, manufacturing an EFZP is challenging due to the high precision requirements and difficulties associated with the calculation and simulation processes. In this research, a complete process is presented to design, simulate, and fabricate an EFZP using a Fourier optics design, simulations, and a direct laser lithographic machine. The resulting EFZP has an increased depth of focus of about nine times compared to a general Fresnel zone plate with similar parameters, while maintaining the focal spot diameter. The performance of this EFZP is evaluated through optical verification and mathematical simulation methods.

An Enhanced Numerical Calculation Method to Study the Anchorage Performance of Rebars.

When modelling the anchorage performance of rebars with the tri-linear law, the calculation process of the load-deformation relation is complicated. The reason is that when the rebar-grout interface entered the elastic-softening-debonding stage, the softening section length and debonding section length vary simultaneously. To solve this issue, this paper proposes an enhanced numerical calculation method. When the rebar-grout interface entered the elastic-softening-debonding stage, the softening section length was fixed to a specific value. One loop function was created to calculate the debonding section length. With this method, the number of iteration calculations significantly decreased. The credibility of this calculation method was confirmed with experimental results. Two case studies were conducted to compare the load-deformation relation obtained with the original calculation method and enhanced calculation method. The results showed that good consistency existed between the results obtained by those two methods. This finding can significantly improve the calculation efficiency when studying the anchorage performance of rebars. Moreover, this paper provides new insight for users to optimise the modelling process of rebars.

Diverse soliton solutions to the nonlinear partial differential equations related to electrical transmission line

This paper introduces novel traveling wave solutions for the (1+1)-dimensional nonlinear telegraph equation (NLTE) and the (2+1)-dimensional nonlinear electrical transmission line equation (NETLE). These equations are pivotal in the transmission and propagation of electrical signals, with applications in telegraph lines, digital image processing, telecommunications, and network engineering. We applied the improved tanh technique combined with the Riccati equation to derive new solutions, showcasing various solitary wave patterns through 3D surface and 2D contour plots. These results provide more comprehensive solutions than previous studies and offer practical applications in communication systems utilizing solitons for data transmission. The proposed method demonstrates an efficient calculation process, aiding researchers in analyzing nonlinear partial differential equations in applied mathematics, physics, and engineering

Application of C4.5 algorithm with PSO Feature Selection and Bagging Technique on Breast Cancer Classification

The second greatest cause of death for women worldwide is breast cancer. When abnormal cells in the body proliferate out of control, cancer is the result. The diagnosis of breast cancer was established using anthropometric data obtained from standard blood tests. From the UCI Machine Learning Repository, the Breast Cancer Coimbra Data Set was obtained and put to use. One popular classification decision tree method is the C4.5 approach. By selecting the appropriate features and applying the appropriate strategy to address class imbalance throughout the classification process, the performance of the C4.5 algorithm may be enhanced. To determine the accuracy of the classification, tests are conducted using a confusion matrix. Accuracy in this study is anticipated to increase with the application of the C4.5 Algorithm, the Bagging approach to address class imbalance, and the PSO feature selection method. The C4.5 Algorithm, PSO, Bagging Technique produce the best accuracy results, with an average of 86.36 percent. The C4.5 classification method has the second highest accuracy, with a PSO accuracy of 79.39 percent. Utilizing the Bagging Technique in conjunction with the C4.5 Algorithm, the accuracy of 75.0% is the third highest. Furthermore, it has a 65.71 percent accuracy with the C4.5 categorization. As a result, the increase in accuracy from before adding PSO and Bagging Technique was 20.65%, indicating that the inclusion of PSO and Bagging Technique had a substantial impact on the calculation process.

Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles

PurposeReading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library’s offerings, contributing to a holistic learning experience.Design/methodology/approachThis study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user’s rating.FindingsThe comparative results indicated that users are quite satisfied with the recommendations generated by W\\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.Originality/valueThe proposed W\\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.

TopicRefiner: Coherence-Guided Steerable LDA for Visual Topic Enhancement.

This article presents a new Human-steerable Topic Modeling (HSTM) technique. Unlike existing techniques commonly relying on matrix decomposition-based topic models, we extend LDA as the fundamental component for extracting topics. LDA's high popularity and technical characteristics, such as better topic quality and no need to cherry-pick terms to construct the document-term matrix, ensure better applicability. Our research revolves around two inherent limitations of LDA. First, the principle of LDA is complex. Its calculation process is stochastic and difficult to control. We thus give a weighting method to incorporate users' refinements into the Gibbs sampling to control LDA. Second, LDA often runs on a corpus with massive terms and documents, forming a vast search space for users to find semantically relevant or irrelevant objects. We thus design a visual editing framework based on the coherence metric, proven to be the most consistent with human perception in assessing topic quality, to guide users' interactive refinements. Cases on two open real-world datasets, participants' performance in a user study, and quantitative experiment results demonstrate the usability and effectiveness of the proposed technique.

Prediction of steady hydrodynamic performance of pump jet propulsion based on free wake vortex model

Due to mutual interference between various components, the transitional motion of fluid particles in the flow field of pump jet propulsion becomes more complex and variable. Additionally, the vortex structure at the wake can undergo severe contraction and deformation. To further improve the accuracy of predicting the hydrodynamic performance of pump jet propulsion, this paper proposes a time-varying, space-varying, and induced velocity-varying free wake numerical calculation model based on potential flow theory. A set of hydrodynamic performance prediction methods that consider the interference of the free wake model is also established. Furthermore, a velocity smoothing model based on time reversal is constructed based on the distribution pattern of numerical singularity points during the induced velocity calculation process, which enhances the robustness of the calculation program. By comparing and analyzing the numerical calculation results with experimental results, including pressure, circulation distribution, and hydrodynamic performance, the accuracy of the free wake model proposed in this paper is verified. The results also demonstrate that the free wake model proposed in this paper can effectively improve the prediction accuracy of the hydrodynamic performance of the pump jet propulsion.

Visual analysis of pulmonary blood flow in pulmonary circulation assessment: differences between two variant algorithms for processing dynamic images.

In the quantitative assessment of pulmonary blood flow, two different processing algorithms [cross-correlation calculation processing (CCC-pro) and reference frame subtraction processing (RFS-pro)] within dynamic imaging systems have been reported to exhibit high correlations with conventional measurement methods. However, reports still need to evaluate these two processing algorithms regarding the different aspects of pulmonary blood flow. This study aimed to analyze the differences in pulmonary circulation. We conducted a cross-sectional study to evaluate patients with lung cancer who underwent radical surgery, simultaneous dynamic chest radiography (DCR), and pulmonary perfusion scintigraphy (PPS). We assessed the correlation between PPS and two algorithms (CCC-pro and RFS-pro) regarding calculated blood flow ratio (BFR) using Pearson's correlation and linear regression analysis. Additionally, we evaluated consistency using the Bland-Altman analysis. We compared the pulmonary blood flow distributions across six-division lung fields and evaluated each method's blood flow images and histograms of pixel values. From May 2018 to December 2020, we consecutively enrolled 46 patients with lung cancer who met the inclusion criteria (40 male patients, with a mean age of 72.91 years). In these patients, CCC-pro and RFS-pro were correlated (R=0.718, P<0.01); however, CCC-pro was more strongly correlated with PPS than RFS-pro (R=0.859, P<0.01 vs. R=0.549, P<0.01). The Bland-Altman analysis showed high agreement, although systematic errors were observed in relationships other than RFS-pro to PPS. CCC-pro and RFS-pro showed similar blood flow distributions in the upper and lower lung fields, with RFS-pro being dominant in the middle. RFS-pro showed higher pixel values in the hilar region and a histogram shape similar to PPS; however, posture affected the right upper lung field gradient. RFS-pro showed no difference in the BFR when the pulmonary artery region was symmetric; however, potential inaccuracies existed when it overlapped with the cardiovascular shadow. The CCC-pro algorithm was useful for quantifying BFRs, whereas the RFS-pro algorithm accurately evaluated blood flow distribution in lung fields. Further algorithm development is required to enable versatile pulmonary blood flow analysis.

A multi-image encryption scheme based on a new [formula omitted]-dimensional chaotic model and eight-base DNA

At present, multi-image encryption schemes usually require the type, quantity and size of images to be fixed, and there are many restrictions. Therefore, this paper proposes a multi-image encryption scheme based on a new n-dimensional(nD) chaotic model and eight-base DNA, which can encrypt any type, quantity and size of images. Firstly, an nD chaotic model is designed, which can generate different chaotic systems according to individual requirements, simplify the complexity of chaotic system design, and solve the problems of uneven chaotic trajectory and limited range. Secondly, some new eight-base algebraic operations are proposed, which opens up a new calculation method for DNA calculation and information processing. These algebraic operations are applied to the bidirectional diffusion process to achieve bidirectional DNA diffusion. Finally, multiple images are fused, and different keys are generated by the SHA-512 algorithm to ensure that each fused image has a unique key. Moreover, SCAN is utilized to scramble the fused image, which provides more flexibility and selectivity for image encryption and processing. Experimental simulation and security analysis verify that the proposed scheme has a superior effect and good anti-attack when encrypting multiple images with different types, quantities and sizes.

Comparative assessment of different rose cultivars under environmental conditions in central Transylvania, Romania

Despite the abundance of rose varieties, it is necessary to create and use new genotypes to improve the appearance of green spaces and meet the demands of society, as well as to ensure their response to environmental variables and climate change. To assess the overall value and flower quality of several Romanian and foreign varieties under the ecological circumstances of Northwest Romania, 14 rose varieties from the shrub and floribunda groups were examined. The study identified notable variations among the cultivars for plant growth, foliage, flowers, and their susceptibility to the three primary fungal diseases (black spot, powdery mildew, and rust) prevalent in the study area. Additionally, the study identified the cultivars with proper resilience to frost during the five-year investigation period, particularly after the cold winters. New varieties obtained at HRS Cluj-Napoca, such as Cristiana, Pasiune mov, Maria-Cristina and Splendid, and international varieties such as Fisherman's Friend, Bonica and Mirato, stood out for their decorative quality and attractive floral elements. Some of these varieties have also been considered potential parents for disease and frost resistance in new hybridization projects. Broad-sense heritability data for the quantitative variables under investigation, as well as multivariate analyses, provided useful information for new rose breeding programs. The comparison of quantitative and qualitative assessments of important decorative traits for the roses, together with proper statistical calculation processes to assure their validity and trustworthiness, enabled the development of hypotheses or working models that can improve rose breeding efficiency.

Nonlinear Dynamics and Meshing State Transition Analysis of Spalling Defect Gears with Multi-Lubrication Conditions

The purpose of this paper is to analyze the dynamic characteristics and the meshing state transition characteristics of spalling defect gears under various lubrication conditions. The time-varying geometric parameters, motion parameters and load distribution of the gears in the state of positive meshing and reverse impact are analyzed. The time-varying meshing stiffness considering the time-varying friction coefficient under elastohydrodynamic lubrication (EHL), mixed lubrication and boundary lubrication are calculated, and the spalling defect is introduced in the stiffness calculation process. The equivalent lubrication model of the gear is established, the switching condition of the lubrication state is derived, and the distribution of the lubrication state in the torque-speed plane is discussed. The multimeshing state dynamic model of the spalling defect gear is established, and the evolution characteristics of the coexistence response and the transition of the meshing state under different lubrication conditions are simulated by using the attraction domain, phase trajectory, meshing force and bifurcation diagram. The results show that the phase trajectory of the gear with the spalling defect is deformed compared with that of the healthy gear. The change of lubrication state has little effect on the phase trajectory and attraction domain of the gear with spalling defect, but has a great influence on the meshing force. At the spalling boundary, the meshing force is changed abruptly, and the worse the lubrication state, the more obvious the mutation. Due to the influence of spalling on tooth stiffness, it will also affect the meshing force in the nonspalling area.

Analysis of Student Errors in Solving Word Problems using Polya Steps

This research was motivated by the many difficulties experienced by students in the mathematics learning process which had an impact on the occurrence of errors in working on mathematics story problems. These errors need to be known so that it is easier to find solutions so that these errors can be minimized. This research aims to determine the types and causes of errors made by students in solving mathematics story problems on single interest and compound interest materials using Polya's steps. This research method is qualitative with descriptive research type. The research subjects consisted of 2 students from 13 class XI TKP SMKN 1 Pariaman students, with the criteria being 1 student each from the medium group and 1 student from the low group. Data collection techniques include tests and interviews. The results of the research show that the percentage of errors made by students in solving mathematics story problems on single interest and compound interest material using Polya steps is: in question number 1, the biggest error made in the step of understanding the problem was 23.08%. Question number 2, the biggest error made during the rechecking step was 15.38%. In question number 3, the biggest mistake made in the step of understanding the problem was 19.23%. The factors that cause students to make mistakes include, not being used to writing information on questions using their own words, not writing down what they know and what is asked in the question correctly, not focusing when working on questions, not being careful in carrying out the calculation process, not being used to checking again, and rushing through the settlement process.

Flight Attitude Estimation with Radar for Remote Sensing Applications.

Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor.

VIRTUAL POLE METHOD - ALTERNATIVE METHOD FOR PROFILING RACK TOOLS

This paper presents a variant for determining the enwrapping condition, in the case of rack gear type tools. This variant was called the “virtual pole method”. The position of the virtual pole Pv is defined by the movement angle of the blank together with the centrode associated with the generated profile. The virtual pole method allows translating in the centrodes’ absolute movements, a point on the generated profile, on to the rack tool profile, thus determining the geometric locus representing the generating tool profile. The new method is easier to apply, while remaining scientifically rigorous. By applying the proposed algorithm, it is no longer necessary to write explicitly the relative movements between the tool and the piece, which simplifies the calculation process and eliminates some of the possibilities for errors.

Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm

Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.

Prediction of strong coupling in resonant perovskite metasurfaces by deep learning.

Resonant metasurfaces are often used to achieve strong coupling, and numerical simulations are the common method for designing and optimizing structural parameters of metasurfaces, while their calculation process takes a lot of time and occupies more computing resources. In this work, the deep learning strategy is proposed to simulate the strong coupling phenomenon in resonant perovskite metasurfaces. The designed fully connected neural network is constructed based on the deep learning algorithm that is used to predict transmission spectra, multipole decomposition spectral lines, and anti-cross phenomena of a perovskite metasurface. Through comparison of numerical simulation results, it can be seen that the neural network can efficiently and accurately predict the strong coupling phenomenon. Compared with the traditional design process, the proposed deep learning model can guide the design of the resonant metasurface more quickly, which significantly improves the feasibility of the design in complex metasurface structures.

Research on image saliency detection based on deep neural network

AbstractAs a hot research field at present, computer vision is devoted to the rapid acquisition and application of target information from images or videos by simulating human visual mechanism. In order to improve the accuracy and efficiency of image detection, image saliency region detection technology has received more and more attention in the field of computer vision research; an important research content in the field, the core part of which lies in the research on algorithms related to feature extraction and saliency calculation of targets. This paper analyzes the multi‐feature fusion saliency detection model and visual saliency calculation process, and based on the existing algorithm, by improving the VGG16 network, a fully convolutional network saliency detection algorithm is proposed. The qualitative and quantitative experimental results show that compared with the four mainstream methods of BL, GS, SF, and RFCN, our algorithm not only improves the accuracy of salient object detection, but also effectively solves the problem of target edge blur. Therefore, this study has improved the accuracy and efficiency of saliency detection, which can not only promote the development of computer vision technology, but also provide support for research in the field of image processing.

Analysis of the Tenth Grade Students’ Numeracy Literacy Ability in Solving Contextual-Based Story Problems

The aim of this study was to describe the tenth grade students’ numeracy literacy ability in solving contextual-based story problems at SMK Negeri 4 Klaten in the 2022/2023 academic year. Taking research subjects using purposive sampling technique. The data collection techniques are documentation, tests and interviews. Based on the results of the study, it was obtained: students' numeracy literacy ability in identifying and understanding problems for students in the upper group had very good criteria (100%), students in the middle group had less criteria (50%) and students in the lower group had very poor criteria (0%); students' numeracy literacy ability in understanding and using symbols and numbers for group students with very good criteria (91.7%), middle and lower group students with very good criteria (87.5%); students' numeracy literacy ability in selecting and applying problem solving methods for students in the upper group had very good criteria (87.5%), students in the middle group had good criteria (79.2%), students in the lower group had good criteria (70.8%); as well as the ability of students' numeracy literacy in drawing and explaining the conclusions of the final results obtained from solving problems for students in the top group with very good criteria (91.7%), students in the middle and lower groups with good criteria (66.7%). In solving contextual-based word problems, students made several mistakes due to students' lack of accuracy and thoroughness both in reading the questions, writing answers and the calculation process.