Computational Point Research Articles

Automatic variogram calculation and modeling

The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.

Artificial Neural Network-Based Non-Linear Modeling and Simulation of CaO-SiO2-Al2O3-MgO Blast Furnace Slag Viscosity

Blast furnace slags are formed by CaO-SiO2-Al2O3-MgO systems and have several physical characteristics, one of which is viscosity. Viscosity is an important variable for the operation and blast furnace performance. This work aimed to model viscosity through linear and non-linear models in order to obtain a model with precision and accuracy. The best model constructed was a non-linear model by artificial neural networks that presented 23 nodes in the first hidden layer and 24 nodes in the second hidden layer with 6 input variables and 1 output variable named ANN 23-24. ANN 23-24 obtained better statistical evaluations in relation to 11 different literature equations for predicting viscosity in CaO-SiO2-Al2O3-MgO systems. ANN 23-24 was also subjected to numerical simulations in order to demonstrate the validation of the non-linear model and presented applications such as viscosity prediction, calculation of the inflection point in the viscosity curve by temperature, the construction of ternary diagrams with viscosity data, and the construction of iso-viscosity curves.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

ON HIGHER ORDER DYNAMIC MODE DECOMPOSITION

This paper introduces an alternative variant of Higher Order Dy namic Mode Decomposition (HODMD), which improves the standard approach from a computational point of view. In the new scheme, time delayed snapshots are used along with the special form of the Koopman operator. An algorithm is derived that allows the calculation of novel decomposition in a stable and efficient way. This method is suitable in cases where standard Dynamic Mode Decomposition (DMD) is not applicable. These are dynamics that show limited spatial complexity, and a very large number of included frequencies. We illustrate and explain the new method using some classical and sample dynamics.

A novel computer vision and point cloud-based approach for accurate structural analysis of a tall irregular timber structure

Wood material has been widely used in critical heritage structures such as pagodas, totem poles, and large-scale sculptures. Conducting rigorous structural analysis is crucial to protect these structures in high-seismic regions. Typically, these types of structures are modeled using an equivalent finite element (FE) model which used simple cylinder with a constant cross section equal to the average of the top and bottom cross section. However, simplified equivalent FE models may not accurately consider the irregularities and complexities of these structures. In this paper, advanced computer vision and point cloud techniques were adopted to accurately and rapidly construct a FE model of a 30-meter irregular timber sculpture. This was achieved using video scans, computer vision-aided 3D reconstruction, point cloud processing, and mesh to solid element conversion. The refined FE model was used to conduct capacity check, mesh sensitivity study, pushover analysis, and response spectrum analysis. The results of the refined FE model were compared to an equivalent FE model. The results show: 1) the proposed numerical modeling methodology for structural analysis can efficiently and accurately measure the dimension of the irregular sculpture up to 98.2 % accuracy; 2) the lateral stiffnesses of the 30-meter irregular sculpture vary significantly (up to 42.6 %) from one direction to the other; 3) the equivalent FE model overestimated the shear and moment capacities by 20.6 % and 13.2 %, respectively; 4) on average, the equivalent FE model overestimated the shear and moment demands by 8.9 % and 5.5 %, respectively. Overall, the proposed application has demonstrated a fast, economical and accurate method to conduct seismic evaluation and design for irregular structures.

Light culling based on the limitations of human visual perception

Abstract We propose a method to remove virtual point light sources (VPLs) in the scene to reduce the time overhead of lighting calculation for shading points in non-visually sensitive areas. We quantify the degree of light stimulation to the human eye and use it to calculate the lighting contribution of VPLs in the scene to visually sensitive areas. Based on this contribution, we remove VPLs that contribute less to visually sensitive areas to improve rendering speed. Experimental results show that the FLIP evaluation index difference between this method and FLC [1] is only 0.00077, while the average number of frames is 1.15 times that of the method in FLC. This proves that this paper can effectively improve rendering speed.

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

Improved identification of network anomalies through optimal CURE clustering

Abstract In this paper, we propose an advanced network anomaly behavior identification framework to overcome the constraints inherent in conventional rule- or signature-based approaches, which often struggle with emerging and previously unknown threats. Central to our framework is an Enhanced CURE (Cluster Updating and REfining) clustering algorithm, meticulously tailored and refined to incorporate a density-based methodology. This enhancement enables the algorithm to discern subtle shifts in network anomaly patterns with heightened precision. The implementation workflow commences with the application of the optimized CURE algorithm to analyze network data, followed by the deployment of a sophisticated anomaly degree ranking mechanism. This mechanism, through meticulous calculation of individual data points’ anomaly degrees and subsequent ranking, effectively isolates those deviating significantly from standard behavioral norms, incorporating a strategic threshold to filter out false positives. To validate our methodology’s efficacy and its superiority over existing techniques, experiments were conducted utilizing a substantial real-world network dataset. These tests affirm not only a marked increase in the accuracy of abnormal behavior recognition and a reduction in computational intricacy but also demonstrate the adaptability across diverse network ecosystems. Our approach has proven successful in pinpointing a wide array of network anomalies, encompassing malicious cyberattacks, fraudulent activities, unauthorized intrusions, and breaches of security protocols, thereby highlighting its comprehensive capability in bolstering network defense strategies. Despite the notable advancements and successful identification of various network anomalies, our framework currently lacks integration with real-time learning capabilities, limiting its immediate responsiveness to rapidly evolving attack patterns and necessitating ongoing research for dynamic updates and adaptive learning mechanisms.

Pengolahan Tamagiri Nasi Uduk Buah Naga pada Resto Kebun Naga Poernama Garut

The purpose of this innovation is to add to the menu at Resto kebun Naga Poernama. Data collection was carried out by organoleptic tests on 25 people conducted by lecturers, industry and consumers. Based on the tabulation of organoleptic test data, the author can conclude that the Dragon Fruit Uduk Rice at Resto Kebun Naga Poernama. from the results of the calculation on the level of liking obtained the value in the main test as follows: appearance 80% (very like), 20% (like). Color 68% (very like), 32% (like). Aroma 44% (very like). 56% (like). Texture 76% (like), 24% (like). Taste 84% (like very much), 16% (like). Taste 80% (really like), 20% (like). so it is stated that Tamagiri Dragon Fruit Shrimp Rice is liked and acceptable by panelists. After obtaining the results of the Break Event Point calculation, it is concluded that for 1 portion of Tamagiri Dragon Fruit Shrimp Rice, it is Rp.6,075, while the selling price per portion is Rp.16,000 with a food cost of 37%, BEP of 166 pcs, fixed cost of Rp. 1,500,000 and it is concluded that in order to get a target margin of Rp. 2,000,000,- / month, it must sell as many as 388 pcs / month or 13pcs / day. The author's suggestion for future researchers is to make eggshells that are not fishy and add more varied fillings.

The Intersection Curve of an Hyperbolic Cylinder with a Torus Sharing the Same Center

This work aims to classify the families of curves obtained by the intersection of an arbitrary hyperbolic cylinder with an arbitrary torus sharing the same center, based on the number of their connected components and the number of their self-intersections points. The graphic geometric representation of these curves, in GeoGebra, and the respective algebraic descriptions, supported from a theoretical and computational point of view, are of fundamental importance for the development of this work. In this paper, we describe the procedure and the necessary implementation to achieve the outlined objective.

An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

Optimal siting and sizing of battery energy storage systems in unbalanced distribution systems: A multi objective problem under uncertainty

In this paper the siting and sizing problem of battery energy storage systems in unbalanced active distribution systems is formulated as a mixed-integer, non-linear, constrained multi objective (MO) optimization problem under uncertainties. The problem is cumbersome from the computational point of view due to the presence of intertemporal constraints, a great number of state variables and the presence of uncertainties in the problem input data. A new approach based on the trade-off/risk analysis is proposed to obtain with acceptable computational efforts a solution that may not be the optimal solution but represents a reasonable and robust compromise. We use the trade-off/risk analysis, because it was specifically developed for power system planning problems in which we deal with a wide range of options, with possible conflicting objectives, and with uncertainty and risk. The proposed approach includes new procedures to select an adequate set of planning alternatives to be considered in the trade-off/risk analysis framework and to assist the planning engineer when difficulties arise in setting probabilities of the input data. Numerical applications to an IEEE unbalanced test system demonstrate the effectiveness of the proposed procedure and indicate the best alternatives of storage systems in the range from 450 kW to 600 kW globally installed in a reduced set of nodes.

Development Management System of Student Religious Activity through User Centered Design Approach and UTAUT

Project-Based Learning (PBL) is increasingly adopted in educational settings, including MKWU, to foster active engagement and practical skill development. However, managing student religious activities within this framework presents challenges, particularly with manual attendance tracking and inaccurate point calculations. This research aimed to develop a system religious activity for students and mentors, utilizing the User Centered Design (UCD) approach. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) model, data was collected from 240 respondents through questionnaires. The findings revealed a 99% positive perception of the system regarding its effectiveness, ease of use, peer support, and resource availability. Key features, such as real-time attendance submission and point updates, significantly enhance operational efficiency and accuracy. The study concludes that this UTAUT-based evaluation system effectively meets user needs and boosts engagement in religious studies, marking a shift from traditional manual evaluation methods that often lack real-time feedback and comprehensive metrics

TreeCompR: Tree competition indices for inventory data and 3D point clouds

Abstract In times of more frequent global change‐type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices (CI) leave users with a difficult choice, as no single competition index has proven universally superior. To help users with the choice and computation of appropriate indices, we present the open‐source TreeCompR package, which handles 3D point clouds and classical forest inventory data, enabling the calculation of both innovative point cloud‐based indices and traditional distance‐dependent indices. It serves as a centralized platform for exploring and comparing different CIs, allowing users to test and select the most suitable CI for their specific research questions within a common interface. To evaluate the package, we used TreeCompR to quantify the competition situation of 307 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. The compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well‐structured output, our package can easily be integrated into existing data‐analysis workflows both for 3D point cloud and classical forest inventory data.

High-precision spatial measurement method of accelerator pre-alignment based on multiple total stations

Abstract With the development of accelerator technology, the scale of accelerators is becoming larger, ranging from hundreds of meters to several kilometers. For stable operation of accelerators, high-precision alignment, positioning, and installation are crucial. Installing all equipment inside the tunnel poses safety risks as personnel would be in a closed environment with potential radiation exposure for prolonged periods. To address the challenges of long adjustment and maintenance periods inside the tunnel due to the installation of equipment for large-scale accelerators, most accelerator devices under construction or in research have pre-alignment assemblies. Each assembly consists of a certain number of magnets distributed on girders. The magnets in one unit are pre-aligned with high precision in the laboratory and then transported to the tunnel. Aligning the entire magnet girder can significantly improve installation efficiency inside the tunnel. To meet the pre-alignment accuracy requirement of 10 μm in the horizontal and vertical directions for the magnet units in the high energy photon source (HEPS) storage ring, a system for high-precision pre-alignment of accelerator units using four total stations for angle observation has been designed in this paper. By employing different instrument layout configurations and incorporating reliable distance benchmarks, high-precision pre-alignment of the magnet are achieved. By arranging targets and utilizing recognition for automatic targeting, real-time point calculations during pre-alignment enhance efficiency. Subsequently, based on this system, pre-alignment simulation calculations and experimental verification of eight magnet focusing–defocusing units in the HEPS storage ring are conducted and ultimately realizing the 10 μm transverse and vertical pre-alignment measuring error within the units. This method, based on high-precision measurements in a small-scale space, reduces the period required for personnel on-site and improves pre-alignment efficiency. It also provides a reference for pre-alignment of multiple magnet units in large accelerators such as the Southern Advanced Photon Source and Circular Electron Positron Collider.

Élő method based mobile TV recommendation system

AbstractStreaming services spread rapidly. Among these services there are the linear TV, video library or program review system, while the online platform offering these contents is called mobile TV. A recommendation system may not only keep existing clients, but may also generate further turnover, should it introduce new content to the users. In this paper a recommendation system based on the Élő point calculation method is addressed. It is detailed how the programs should be grouped into different dimensions and what type of categories should be considered. Further, the idea of punch cards is introduced. Besides, the user profiles are set. The match system introduced by Élő is applied to the present situation. The system is introduced at a local mobile TV provider with 20,000 users.

An efficient approach of meshless node placement in three-dimensional subsurface flow modeling

Meshless methods are valuable tools for real-time subsurface flow modeling and are particularly beneficial for adaptively adjusting node positions when incorporating supplementary measurements. However, an algorithm is required for automatically generating high-quality meshless nodes to adapt to irregularly shaped aquifers and arbitrarily distributed wells. This paper introduces a three-dimensional adaptive meshless node placement technique based on advancing front nodes and proposes a sigmoid exclusion circle to ensure adaptability to specific positions of interest. It is crucial for groundwater simulations to have specified computational points at wellbore locations. The generated meshless nodes were used as computational points in the generalized finite difference method. The node quality was assessed by comparing the numerical solutions to the analytical solution, achieving errors of the order of 10–12. The application scenarios of irregularly shaped aquifers demonstrated the high efficiency of the three-dimensional node placement in generating up to 10,000 nodes in <5 s. The effectiveness of the algorithm was verified using three-dimensional irregular computational domains. The algorithm could be easily adapted to two-dimensional problems, and an irregular boundary example is presented. The validation and application cases highlighted the versatility of the proposed method and established its potential for real-world hydrogeological applications.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

An effective picking point localization method for multi-posture lotus pods based on three-view depth vision observation

Different lotus pod individuals have different growth postures, and conventional single-view observation makes it difficult to ensure the visibility of the stalk’s picking segment of all lotus pods, thus affecting the efficiency and quality of automatic picking. To solve this problem, this study proposes a picking point localization method for multi-posture lotus pods based on three-view depth vision observation. It includes three steps: first, the RGB-D (red–green–blue–depth) images of lotus pods are collected from three viewing directions based on the designed three-view depth vision observation scheme. Second, instance segmentation on the lotus pods and stalks in the collected images is performed to obtain mask data. Third, the final lotus pod picking point coordinates and picking vector data are obtained using the proposed lotus pod picking information calculation algorithm. The algorithm comprises the two-dimensional image feature point calculation, single-view picking information calculation, and three-view picking information data synthesis. The three-view observation test of lotus pods in a planting environment shows that the proposed three-view observation scheme can meet the picking segment observation needs of lotus pods in any posture. In addition, lotus pod localization tests were conducted based on a specifically designed localization test bench. The proposed localization method achieved a localization success rate of 98.00 % and 90.86 % for single and multiple lotus pods, respectively. The results verify the feasibility of the proposed localization method for calculating the picking information of arbitrary lotus pods, which can provide essential support for the practical development of automatic lotus pod harvesting technology.

Aspects concerning the factorial diagnostic of the breakeven point

The purpose of the article consists in applying breakeven point as a forecasting method to estimate the ability of the entity to fully cover expenses in business start up or in day-to-day diagnostics, identifying influencing factors. The calculation of the breakeven point shall be carried out in value and natural terms according to the heterogeneous and homogeneous charcter of production, on the basis of the division of the expenditure into variable and constant. The results allowed us to infer that at the entity the breakeven point in dynamics has an increasing trend. Thus, compared to the previous year , it increased by 14,7%, which proves an unfavorable situation. The security margin decreased by 29,7%, and the reduction in operational profit is 26,0%, which signals that the entity is in an unstable situation.