Set Of Points Research Articles

Analysis of numerical modeling of steady-state modes of methane–hydrogen mixture transportation through a compressor station to reduce CO2 emissions

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane–hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

Feature matching based on Gaussian kernel convolution and minimum relative motion

Feature matching is a necessary and important step for remote sensing image registration, intended to establish reliable point correspondences between two sets of features. In this paper, we propose a feature registration model based on local relative motion, which combines Gaussian kernel convolution with relative motion (GRM) vector to obtain better results by removing wrong matches and improving the inlier point accuracy. We first establish putative matching based on the similarity between local descriptors. Then, the preliminary hypothetical matching point set is filtered using consistency with nearest neighbors among the inlier points to obtain a more accurate motion vector, and to fit the real motion vector through the Gaussian convolution kernel. Finally, we find the displacement between the fitted motion vector and the matching generated motion vector. And combine the displacement with the optimization model to find the inlier point set. Experimental results show that our GRM method outperforms related work, achieving better matching results.

Populist Storytelling and Negative Affective Polarization: Social Media Evidence from Mexico

ABSTRACT The ideational definition of populism proposes that a narrative is populist if it is characterized by a Manichean cosmology that divides the political community between a “people,” conceived as a homogeneously virtuous entity, and an “elite,” conceived as a homogeneously corrupt entity. Departing from that conceptualization, this work first investigates the specific stories that Andrés Manuel López Obrador uses to spread his populist worldview, which we call “storytelling.” We define the idea of storytelling as the art of telling a story where emotions, characters and other details are applied in order to promote a particular point of view or set of values. Second, we explore whether some of those stories produce greater negative affective polarization, here defined as the extent to which rival sociopolitical camps view each other as a disliked out-group. Findings suggest that some specific stories—in particular, what we call “stories of conspiracy” and “stories of ostracism”—indeed tend to induce more polarized attitudes among citizens.

Real-time Welding Machine Status Recognition using Embedded Perceptron Color Classification Model

This paper aims to utilize the perceptron model in machine learning to achieve precise classification of the working status of welding machines by analyzing image data. We collected static images of welding and non-welding operations, extracted the R, G, B color values of the images, and labeled them accordingly (+1 for welding status, -1 for idle status). By employing the stochastic gradient descent method for training the perceptron model, we obtained the weights w and bias b of the model under the condition that all data points in the training set are correctly classified. The model's recognition accuracy was validated on the test set. Ultimately, we applied this model to the STM32f103zet6 microcontroller chip and successfully achieved real-time recognition of the welding machine's operational status during the welding process via a camera. Through testing, the classification accuracy reached 83.6%. Further optimization of classification criteria can be combined with relevant expert suggestions, providing a reference solution for monitoring and identifying the working status of welding machines in steel pipe production.

Use of Total Least Squares Adjustment in Geodetic Applications

This article discusses the method of computing the values of the unknowns under the condition of the minimum sum of the squares of the residuals of the observations, also known as the least squares method, with the additional condition of taking into account the errors in the unknowns. The problem has already been treated by many authors, especially in the field of regression analysis and the computation of transformation parameters. We give an overview of the theoretical foundations of the least squares method and extensions of this method by considering the errors in the unknowns in the model matrix. So, the total least squares method is presented in this paper, fitting the regression line to a set of points and computing transformation parameters for the transition between the old and the new Slovenian national coordinate systems. Furthermore, for the first time, the method is also presented and tested in the S-transformation between different geodetic datum-dependent solutions. Also, for the first time, we systematically compare the results of the approach with conventional approaches in all three considered tasks. With the results based on relevant statistics, we confirm the suitability of the described method for dealing with the considered computational tasks.

BIM-based task planning method for wheeled-legged rebar binding robot

ABSTRACT Building Information Modeling (BIM) data has the advantages of high quality, operability, and parametric capabilities. In recent years, the application of BIM in construction planning for building robots has been increasing. Task planning for rebar binding is an important aspect of construction, and there is a growing trend of using robots to improve the accuracy and efficiency of binding operations. However, the complex and dynamic construction site environment poses significant challenges for task planning in rebar binding. Therefore, this paper proposes a new BIM-based task planning method for a wheeled-legged rebar binding robot, which can quickly generate an optimal task sequence. Firstly, an initial set of rebar intersection points is generated based on BIM data. Then, considering the execution dead zone of the robot and the variable workspace of the binding mechanism, the rebar intersection points set is decomposed and filtered using morphological opening operations. Finally, a constrained genetic algorithm is employed to sort the set of task points. Simulation and real-world experiments were conducted using a wheeled-legged rebar binding robot as the research subject. The results demonstrate that, compared to manual planning, the average binding efficiency is approximately 1500 points per hour, which is 1.97 times higher than manual operation. This validates the applicability and effectiveness of the proposed method.

TWO-CARDINAL DERIVED TOPOLOGIES, INDESCRIBABILITY AND RAMSEYNESS

Abstract We introduce a natural two-cardinal version of Bagaria’s sequence of derived topologies on ordinals. We prove that for our sequence of two-cardinal derived topologies, limit points of sets can be characterized in terms of a new iterated form of pairwise simultaneous reflection of certain kinds of stationary sets, the first few instances of which are often equivalent to notions related to strong stationarity, which has been studied previously in the context of strongly normal ideals. The non-discreteness of these two-cardinal derived topologies can be obtained from certain two-cardinal indescribability hypotheses, which follow from local instances of supercompactness. Additionally, we answer several questions posed by the first author, Holy and White on the relationship between Ramseyness and indescribability in both the cardinal context and in the two-cardinal context.

INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY

In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.

An Industrial Control System for Cement Sulfates Content Using a Feedforward and Feedback Mechanism

This study examines the design and long-term implementation of a feedforward and feedback (FF–FB) mechanism in a control system for cement sulfates applied to all types of cement produced in two mills at a production facility. We compared the results with those of a previous controller (SC) that operated in the same unit. The Shewhart charts of the annual SO3 mean values and the nonparametric Mann–Whitney test demonstrate that, for the FF–FB controller, the mean values more effectively approach the SO3 target than the older controller in two out of the three cement types. The s-charts for the annual standard deviation of all cement types and mills indicate that the ratio of the central lines of FF–FB to SC ranges from 0.39 to 0.59, representing a significant improvement. The application of the error propagation technique validates and explains these improvements. The effectiveness of the installed system is due to two main factors. The feedforward (FF) component tracks the set point of SO3 when the mill begins grinding a different type of cement, while the feedback (FB) component effectively attenuates the fluctuations in the sulfates of the raw materials.

Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC

AbstractModel‐based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model‐based approaches. Thanks to recent technological advancements in digitalization, machine‐learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model‐based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model‐based control and optimization is increasing rapidly. However, there are no easy‐to‐use, flexible, and freely available open‐source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy‐to‐use Python toolbox that allows to solve machine‐learning‐supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state‐of‐the‐art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO‐MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.

Underwater glider 3D path planning with adaptive segments and optimal motion parameters based on improved JADE algorithm

This paper presents a novel 3D path planning method for the underwater glider (UG) that incorporates adaptive segment strategy, motion parameters optimization, and an improved JADE algorithm. The method aims to generate an energy-efficient path by adapting to ocean currents and seabed topography and selecting favorable motion parameters. We establish an energy consumption model for a blended-wing-body UG, examining the influence of motion parameters and ocean currents on its performance. The proposed method encodes an energy-optimal gliding path through multiple path segments, each defined by a set of path points, pitch angles, and diving depths. The fitness function, based on energy consumption, guides the optimization process. To enhance the optimization, we present an improved JADE algorithm with multi-mutation strategies, which adaptively updates the mutation operation and mean crossover probability. Our method was assessed and compared with a classical UG path planning method on 6 test scenarios. Simulation results confirm that adaptive segments and motion parameter optimization contribute to better adaptation to ocean environments and reduced energy consumption.

Моделирование рабочего пространства планарного трёхзвенного манипулятора

A study of the working space of a three-link planar manipulator was carried out. The basis is taken from analytical dependencies that allow solving the direct problem of kinematics, i.e. determine the coordinates of the gripper center point using three generalized coordinates of the manipulator. The analysis performed made it possible to give a geo-metric interpretation of the dependencies. It has been established that the workspace of the manipulator is a three-parameter set of points. On a plane, this set of points is represented in the form of two disks consisting of ring cells, for which the corresponding analytical dependencies are obtained. The geometric image of this set is a three-dimensional torus. The resulting models are visualized, which facilitates the solution of this problem. To determine the values of the generalized coordinates of the gripper center points, a mapping was carried out by orthogonal projection of the families of circles obtained in the work into four-dimensional space. As a result, three-dimensional hypersurfaces in four-dimensional space were obtained. It is proposed to study them by constructing hypersurface sections models by hyper-planes. Such models in visualization mode allow solving direct and inverse kinematics problems of the manipulator under study.

Thermally stable Peltier controlled vacuum chamber for electrical transport measurements.

The design, manufacture, and characterization of an inexpensive, temperature-controlled vacuum chamber with millikelvin stability for electrical transport measurements at and near room temperature is reported. A commercially available Peltier device and a high-precision temperature controller are used to actively heat and cool the sample space. The system was designed to minimize thermal fluctuations in spintronic and semiconductor transport measurements, but the general principle is relevant to a wide range of electrical measurement applications. The main issues overcome are the mounting of a sample with a path of high thermal conductivity through to the Peltier device and the heat sinking of the said Peltier device inside a vacuum. A copper slug is used as the mount for a sample, and a large copper block is used as a thermal feedthrough before a passive heat sink is used to cool this block. The Peltier device provides 20W of heating and cooling power, achieving a maximum range of 30K below and 40K above the ambient temperature. The temperature stability is within 5 mK at all set points with an even better performance above the ambient temperature. A vacuum pressure of 10-8 hPa is achievable. As a demonstration, we present experimental results from current-induced electrical switching of a CuMnAs thin film. Transport measurements with and without the Peltier control emphasize the importance of a constant temperature in these applications. The thermal lag between the sample space measurement and the sample itself is observed through magnetoresistance values measured during a temperature sweep.

The Translation of (Jadal)Argumentation (Dialectic) Discourse in the Holy Qur’an into English: An Investigation of the Logical Construction of Dialectic in the Source Text

The present research seeks to investigate the equivalence of translating argumentative topics, which include the dialectic (controversial) style in the Holy Qur’an. It designs to know how the discourse of the Qur’anic controversy is manifested, based on a set of logical arguments and points of view. The research also aims to find out the extent to which the English translations succeeded in conveying the same dialectical logical structure connotation, taking into account the challenges faced by the translators: Hilali, Khan, and Abdel Haleem in translating the meanings of the Holy Qur’an, especially the dialectic style. Therefore, the translator must strive to convey the dialectical discourse with its stylistic features in terms of clarity, accuracy, and objectivity. The study adopts an eclectic method. It is a combination of a semantic approach, text discussion, analysis, and extracting a suggested translation, with applying the back-translation technique. All of this is in light of an applied approach to examples of the Holy Ayat (verses) for the controversial style. The study concluded that translating of the dialectical style and its links represent a real challenge for translators. So, they must resort to consult exegesis (tafsir), rhetoric books and other Arabic sources, in addition to the translation theories to overcome these challenges. The study recommends that the translator of the meanings of the Holy Qur’an must be familiar with the sciences of the Holy Qur’an and the Arabic language, such as interpretation, rhetoric, etc. The translator also need to seek help from language dictionaries to choose the specific meaning of the dialectical style. Moreover, the translator, before embarking on the translation, must consult scholars and experts when submitting a suggested translation. This suggested translation must be emanating from authentic Islamic visions. This due to verify the intended Qur’anic meaning for argumentative (Al-Hajjaj) dialectical and others rhetorical styles.

A robust pairing method for two-pulse particle tracking velocimetry based on coherent point drift

Particle tracking velocity has reached a high level of maturity in time-resolved measurements since the introduction and development of the Shake-The-Box algorithm. The effectiveness of this approach lies, in part, in its ability to exploit the temporal coherence of particle trajectories to reject the ghost particles while increasing the density of true particles. However, certain situations may prevent time-resolved measurements. In those cases, a Two-Pulse configuration is often the only option. This raises a challenge with regard to the capacity in separating the ghost from the true particles due to the lack of long-term trajectories. This article proposes a new approach to solve this problem using the coherent point drift (CPD) method. This method identifies a spatially coherent deformation field that models the transformation between two correlated sets of points. In the context of particle tracking velocimetry, the imposed spatial coherence of this calculation is believed to act in the same way as the temporal coherence that made Shake-The-Box successful. The CPD is governed by three parameters whose optimal values have been evaluated in the present contribution. These values were found to be weakly sensitive to the characteristics of the flow under study, ensuring that this method is robust without further tuning of the parameters. The method is then compared with the Two-Pulse implementation of Shake-The-Box (2P-STB) available in Davis 10.2. For this purpose, sets of realistic images were generated at two successive times for different configurations based of synthetically generated turbulent flows. The Iterative-Particle-Reconstruction in Davis 10.2 was then used to extract the list of particles to be processed by CPD. The comparison shows a better recall with 2P-STB than CPD, especially for large time intervals between frames, but an overall better rejection of ghost particles by CPD than 2P-STB, which was the expected benefit of this method.

A Generalized Full-to-Partial Registration Framework of 3D Point Sets for Computer-Aided Orthopedic Surgery.

The precise alignment of full and partial 3D point sets is a crucial technique in computer-aided orthopedic surgery, but remains a significant challenge. This registration process is complicated by the partial overlap between the full and partial 3D point sets, as well as the susceptibility of 3D point sets to noise interference and poor initialization conditions. To address these issues, we propose a novel full-to-partial registration framework for computer-aided orthopedic surgery that utilizes reinforcement learning. Our proposed framework is both generalized and robust, effectively handling the challenges of noise, poor initialization, and partial overlap. Moreover, this framework demonstrates exceptional generalization capabilities for various bones, including the pelvis, femurs, and tibias. Extensive experimentation on several bone datasets has demonstrated that the proposed method achieves a superior C.D. error of 8.211 e-05 and our method consistently outperforms state-of-the-art registration techniques. Hence, our proposed method is capable of achieving precise bone alignments for computer-aided orthopedic surgery.

OPTIMAL DESIGN OF A PUMPED IRRIGATION SYSTEM OPERATING IN SHIFTS

The optimization of pumping systems is important because it improves energy efficiency, increases production, reduces operating and maintenance costs, and contributes to sustainability. To address this challenge, it is essential to effectively manage irrigation systems. In this research, an optimization algorithm was used to minimize the economic cost of irrigation system design. The implemented methodology includes the following steps: determining the fictitious continuous flow rate (qfc) and the number of shifts, functionally designing the irrigation network with pumping operations in shifts, and finally designing the network using Linear Programming (LP). Inputs to the model include network topology, set point pressures, design speed ranges, and pump characteristic curve. The model output provides the diameters of the irrigation system lines. In consequence, this methodology was applied in the irrigation system of the community of San Pedro de la Bendita in the province of Loja. The study demonstrates a significant cost reduction in the hydraulic design of the irrigation network when operating in shifts using LP compared to the cost of a conventional hydraulic design.

Heat exchanger control: Using deep-Q network

To achieve stable operation of district heating systems and improve the performance of heat exchange station control systems, this paper explores the application of intelligent control algorithms for controlling district heating systems. Initially, a heat exchanger neural network model is established. Secondly, a mass regulation controller based on a deep reinforcement learning algorithm is designed to accurately track the desired outlet temperature of the secondary side set point. Finally, the performance of the controller in terms of tracking response and disturbance suppression is verified using an example of a district heating system. The experimental results demonstrate that the controller exhibits minimal overshoot, with the outlet temperature of the secondary side deviating by less than 1 % when the controller setting value changes. Additionally, during disturbance suppression response, the deviation of the outlet temperature of the secondary side remains within ±2 °C. Compared to other controllers, the designed controller exhibits superior dynamic performance across various operating points and under different disturbances. Additionally, it outperforms traditional control methods in terms of stability and disturbance rejection capabilities.

On the geometry of fixed points and discontinuity

Recently, there has been a considerable effort to obtain new solutions to the Rhoades' open problem on the existence of contractive mappings that admit discontinuity at the fixed point. An extended version of this problem is also stated using a geometric approach. In this paper, we obtain new solutions to this extended version of the Rhoades' open problem. A related problem, the fixed-circle problem (resp. fixed-disc problem) is also studied. Both of these problems are related to the geometric properties of the fixed point set of a self-mapping on a metric space. Furthermore, a new result about metric completeness and a short discussion on the activation functions used in the study of neural networks are given. By providing necessary examples, we show that our obtained results are effective.

A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier

An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.