Intersection Algorithm Research Articles

Development of a DEM method for predicting wear distribution on particle scale

Wear is a complex and frequently observed phenomenon in industrial processes. In past studies, the equipment wear has been the major focus for researchers, while particle wear and its distribution have received less attention. However, in processes such as grinding, mass finishing, and polishing, understanding particle wear and its distribution is equally important. Predicting particle wear and its distribution can help predict shape changes during processing and guide production to improve economic efficiency. In this study, a method combining an energy-based wear model IEEM (Impact Energy erosion model) and an intersection algorithm is proposed to obtain the wear distribution within the DEM (Discrete Element Method) framework. The accuracy of the method is validated by the comparison between experiments and corresponding simulations, where the experiment is conducted on cube workpieces using a laboratory-scale planetary ball mill under wet conditions. Furthermore, the influence of particle shape on the wear distribution is investigated by DEM simulations.

Multivariate modeling and optimization of the reverse cationic flotation process of iron ore using depressant-modified starch

The iron ore has low quality to be directly used in steel production. Reverse cation flotation is one of the most used processes in the separation of quartz and particles smaller than 0.150 mm, and, therefore, can be used in the production of pellet feed. This project aimed at the application of modeling and optimization of flotation parameters (starch mass and time) to improve and enhance the separation and process conditions. The study of a starch-based depressant modified by different levels of temperature, starch mass, time, and mass of ammonium hydroxide, was conducted to reduce the surface tension of the water and with that, enhance the stability and formation of the nano and microbubbles. Both steps were modeled via the Central Composite Design (CCD) response surface array and the multivariate and multiobjective optimization using the Normal Boundary Intersection (NBI) algorithm. The iron concentration was 77.83 % under the optimized conditions with only 0.33 % of SiO2 content.

Intraoperative fluoroscopic safety assessment of femoral head implants with 3-dimensional risk parameters to minimize cut-out.

This study aimed to introduce a method to extract the 3-dimensional spatial position of the femoral head implant from 2-dimensional fluoroscopic projections, allowing surgeons to assess fixation much more accurately and prevent cut-out complications in proximal femoral nailing. To define a safety region for the tip in the femoral head, a novel 3-dimensional distance-based risk parameter called TSD3D was introduced. An intersection algorithm was developed that solely takes the fluoroscopic anteroposterior and lateral distances to reveal the 3-dimensional location of the screw or Kirschner wire tip, enabling the utilization of the 3-dimensional parameter. Orthogonal per- spectives of 6 femur proximal bone substitutes with randomly inserted Kirschner wires were imaged under fluoroscopy. The developed algorithm was used to calculate the implant tip location in 3-dimensional from 2-dimensional images for each case. Algorithm accuracy was validated with the computed tomography-obtained 3-dimensional models of the same femur substitutes. The newly introduced risk parameter successfully visualizes 3-dimensional safety regions. Utilizing the 2-dimensional fluoro- scopic distances as inputs to the algorithm, the 3-dimensional position of the implanted Kirschner wire tip is calculated with a maximum of 9.8% error for a single Cartesian-coordinate measurement comparison. By incorporating the newly introduced 3-dimensional risk parameter, surgeons can more precisely evaluate the position of the implant and avoid cut-out complications, instead of relying solely on misleading 2-dimensional fluoroscopic projections of the femoral head.

Parameterized Site Selection Approach of Park Entrance Based on Crowd Simulation and Design Requirement

With the extensive application of data analysis in various parts of the landscape design process, Building Information Modeling (BIM), City Information Modeling (CIM), and Landscape Information Modeling (LIM) all aim to achieve key data sharing and collaboration in the whole cycle and promote the development of smart cities. Landscape element indicators are complex, diverse, and difficult to quantify, which is one of the reasons for the slow development of LIM. However, with the development requirements of LIM, quantifying landscape elements and transforming landscapes into digital landscape information has become a hot spot in the landscape design industry. Landscape parametric design aims to transform the design elements into quantifiable parameters, obtain the design scheme by changing the value of the parameters, and form the design results based on digital information. It is one of the foundations of LIM. Based on the Rhino + Grasshopper (R+G) platform, this study takes Shuixizhuang Park as an example and establishes the parametric design approach for the park entrance. The approach involves several steps: (1) Confirming the boundary and key points of the park to prepare the basic data for parametric design. (2) Utilizing the Physarealm Algorithm Method (PAM) to simulate crowd paths, the Site Slope Algorithm Model (SSAM) to analyze terrain slope, the Road Intersection Algorithm Model (RIAM) to determine the scope of the park entrance area based on the national and local design codes, and the Crowd Density Algorithm Model (CDAM) and Crowd Convenience Algorithm Model (CCAM) to analyze the density and convenience of the crowd to preliminarily confirm the park entrance. (3) Meeting the basic requirements of the crowd and vehicle gathering and spread by using the Square Area Review Algorithm Model (SARAM) and Parking Lot Review Algorithm Model (PLRAM) to recheck the site area of the park entrance square and park lot to optimize the park entrance. This approach constructs several site analysis models based on the R+G platform and introduces PAM to analyze crowd activity paths, proposing a landscape parametric design method that integrates crowd activity and landscape design requirements. Compared with the classical design, the landscape parametric design derived from the comprehensive data analysis reduces human interference, is more scientific and practical, and better meets the requirements of people entering the park. The approach also provides ideas for other landscape parametric site selections. By adjusting the values of element parameters, the approach can also be applied to the site selection and design of other landscapes.

Multi-objective optimal control of tungiasis diseases with terminal demands

In this paper, we aim to minimize the epidemic size of tungiasis disease and economic costs simultaneously, with terminal demands for infected humans. A human–jigger parasite control system with four control schemes for humans and jiggers is established. We propose a multi-objective optimal control problem with terminal constraints, in which the accumulated number of infected humans and control costs are involved. By applying the modified normal boundary intersection algorithm and the interior point scheme, numerical simulations for different combinations of control schemes are carried out, and actual data in Madagascar are used. Effective combination schemes are indicated from the perspectives of disease eradication, cost saving and time saving. Once these effective combinations are properly performed, the disease can be controlled. When only minimizing the epidemic size, the combination of the optimal treatments and adulticiding efforts is the best choice in the rainy season; the combination of the optimal personal protections and treatments is the preferential option in the dry season. When only minimizing the economical cost, the combination of the optimal adulticide and larvicide is the better selection in the rainy season; the combination of the optimal personal protections, treatments and adulticiding efforts is the prior choose in the dry season. Thus, there is a trade-off between the two objectives for all the effective combinations, decision-makers may choose an appropriate one to control the disease.

A Polynomial Lower Bound on the Number of Rounds for Parallel Submodular Function Minimization and Matroid Intersection

Submodular function minimization (SFM) and matroid intersection are fundamental discrete optimization problems with applications in many fields. It is well known that both of these can be solved making queries to a relevant oracle (evaluation oracle for SFM and rank oracle for matroid intersection), where denotes the universe size. However, all known polynomial query algorithms are highly adaptive, requiring at least rounds of querying the oracle. A natural question is whether these can be efficiently solved in a highly parallel manner, namely, with queries using only polylogarithmic rounds of adaptivity. An important step towards understanding the adaptivity needed for efficient parallel SFM was taken recently in the work of Balkanski and Singer who showed that any SFM algorithm making queries necessarily requires rounds. This left open the possibility of efficient SFM algorithms in polylogarithmic rounds. For matroid intersection, even the possibility of a constant round, query algorithm was not hitherto ruled out. In this work, we prove that any, possibly randomized, algorithm for submodular function minimization or matroid intersection making queries requires (Throughout the paper, we use the usual convention of using to denote and using to denote for some unspecified constant ) rounds of adaptivity. In fact, we show a polynomial lower bound on the number of rounds of adaptivity even for algorithms that make at most queries for any constant . Therefore, even though SFM and matroid intersection are efficiently solvable, they are not highly parallelizable in the oracle model.

Optimal placement of locally controlled voltage regulators in traditional distribution systems using the IMDE algorithm

Abstract One of the main goals of the designers of the power systems is the reduction of extra systems costs. The main part of these costs is related to energy loss, and the methods for loss reduction significantly lead to a cost reduction. The voltage regulators (VRs) can be used along the distribution feeders and the improvement of the network voltage profile results in the loss reduction of the system. However, the important problem is the installation location of this equipment to have the most profitability with the lowest cost. In this paper, the optimal placement of the VRs along the distribution systems is performed using intersect mutation differential evolution algorithm. The method aims to reduce loss and the primary investment and maintenance of the VRs. To this aim, the studies are performed on two scenarios for two standard distribution systems with 33 and 69 buses. A genetic algorithm (GA) is also utilized for the VRs placement to validate the results. The results show that the VRs reduce the loss to about 15% of the maximum system load. Moreover, the improved differential evolution algorithm performs better than the GA and makes the objective function in the minimum value.

Detray: a compile time polymorphic tracking geometry description

A detailed geometry description is essential to any high quality track reconstruction application. In current C++ based track reconstruction software libraries this is often achieved by an object oriented, polymorphic geometry description that implements different shapes and objects by extending a common base class. Such a design, however, has been shown to be problematic when attempting to adapt these applications to run on heterogeneous computing hardware, particularly on hardware accelerators. We present detray, a compile time polymorphic and yet accurate track reconstruction geometry description which is part of the ACTS parallelization R&D effort. detray is built as an index based geometry description with a shallow memory layout, that uses variadic template programming to allow custom shapes and intersection algorithms rather than inheritance from abstract base classes. It is designed to serve as a potential geometry and navigation backend for ACTS and as such implements the ACTS navigation model of boundary portals and purely surface based geometric entities. detray is designed to work with a dedicated memory management library and thus can be instantiated as a geometry model in host and device code.

3D layout estimation of general rooms based on ordinal semantic segmentation

AbstractRoom layout estimation aims to predict the location and range of layout planes of interior spaces. Previous works treat each layout plane as an independent individual without considering the ordinal relation between walls, resulting the loss of the wall planes and the lack of integrity. This paper proposes a novel two‐branch neural networks model to estimate 3D layouts of cuboid and non‐cuboid room types. The model embeds the ordinal relation between layout planes into the layout segmentation branch through an proposed ordinal classification loss function, and outputs both pixel‐level layout segmentation maps and layout plane parameter maps. Then, the instance‐level plane parameters of each layout plane are determined by using an instance‐aware pooling layer. Finally, the sharpness of layout edges of the 2D layout semantic segmentation map is optimized by using an improved depth map intersection algorithm. Furthermore, we annotate a large‐scale 3D room layout estimation dataset, InteriorNet‐Layout, to obtain a steady model. Experiments on synthesized real‐world datasets show that the proposed method achieves faster calculation while maintaining high accuracy. Code is available at https://github.com/Hui‐Yao/3D‐ordinal‐layout‐estimation.

Consensus based target tracking against deception jamming in distributed radar networks

AbstractIn this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

A Brief Survey of Clipping and Intersection Algorithms with a List of References (including Triangle-Triangle Intersections)

This contribution presents a brief survey of clipping and intersection algorithms in ${E^{2}}$ and ${E^{3}}$ with a nearly complete list of relevant references. Some algorithms use the projective extension of the Euclidean space and vector-vector operations, which support GPU and SSE use. This survey is intended to help researchers, students, and practitioners dealing with intersection and clipping algorithms.

Biobased hybrid composite design for optimum hardness and wear resistance

The present investigation considered the design of a biobased hybrid particulate composite for optimal hardness and wear resistance. Tests were conducted based on the plan of 20 sets of experiments generated through Model-Based Calibration Toolbox™ contained in MATLAB routines. A Portable Ultrasonic Hardness tester was used to record the hardness properties while the wear behavior of the composite was tested using a pin-on-disk machine. The optimization study was applied to the Calibration Generation (CAGE) platform utilizing the Normal Boundary Intersection (NBI) algorithm which enables the development of a Pareto optimal set with a continuous and equally distributed chart. Scanning Electron Microscopy (SEM) was used to perform morphological examination. From the optimized results, it was observed that a particle size of 1752 µm, a volume fraction of 45%, and a stirring time of 70 s gave the best-ranked composite exhibiting optimal values of 784.91 Leeb hardness, 643.19 Rockwell hardness, 593.17 Brinell hardness, and 0.000139 mm3/Nm specific wear rate. Under the same conditions, the predicted values of the optimization model closely matched the experimental results. The NBI optimization technique proves to be a viable method for performing material design and property improvement tasks. Surface morphology analysis via SEM revealed that the wearing of bio-based hybrid particulate composite parts is associated with delamination and abrasion mechanisms. It is implied that the new material can be used for applications such as furniture, automotive spare parts, and other inexpensive technical solutions.

Modeling and optimization of the pyrolysis oil production process from polypropylene for the production of aviation kerosene

One of the greatest challenges in the world is in the final disposal of plastics in order to reduce the effect of its polluting potential. Thus, the application of pyrolysis processes in generating products of interest such as fuel oils can be part of the solution. In addition to reducing greenhouse gas emissions to the atmosphere, oil can enter the supply chain after the cracking process in petrochemical industries. In this context, this work used statistical modeling of the response surface linked to the normal boundary intersection algorithm, aiming at a higher yield of oil production and major selectivity of recycled polypropylene pyrolysis. From the analysis of the mechanisms proposed in the literature with the modeling and optimization in this work, it was possible, from a kaolin mass of 9.12 g and a heating ramp of 19.37 °C/min, to obtain higher percentages of aviation kerosene as well as a satisfactory performance. Graphical abstract

SmartLight: A smart efficient traffic light scheduling algorithm for green road intersections

Traveling vehicles participate in emphasizing the global warming problem due to the gases produced by them. The exponential increase in the number of daily traveling vehicles has exaggerated the world pollution problem threatening the life on the planet. This encourages several environmental organizations to look for designing green vehicles. Moreover, several countries have forced green driving rules and technologies. Road intersections are considered high fuel consumption areas over the road network. This is due to the required power to stop moving vehicles and restart stopped vehicles at these intersections. This work introduces an efficient traffic light scheduling algorithm (SmartLight) that controls the competing traffic flows at the road intersections. It is designed to reduce the total consumed fuel of vehicles and thus it reduces their produced gases. The topology of the road intersection, the context of the competing traffic flows, and the real-time traffic characteristics of each flow are mainly considered to schedule the phases of each located traffic light. The phases of the traffic light cycle are primly set to allow emergency vehicles to pass through the intersection without stopping. Then, the traffic flow that contains heavy vehicles or has the highest weight among the competing traffic flows is assigned the highest priority to pass through the signalized intersection. Finally, the average waiting delay time of each flow on the signalized intersection should not exceed a pre-determined threshold to guarantee fair sharing of the signalized intersection. The scheduling time of each phase is set based on the lengths of platoons of vehicles that are scheduled during that phase from different un-conflicted traffic flows. An experimental study has evaluated the performance of the proposed algorithm (SmartLight) compared to previous traffic light scheduling algorithms in terms of total fuel consumption, gas emission, the average queuing delay time of vehicles, and the throughput of the road intersection.

An efficient approach for mining weighted uncertain interesting patterns

The mining of weighted uncertain interesting patterns (WUIPs) is an interesting problem with many practical applications. In this study, we propose an effective method for mining WUIPs from uncertain databases. We first introduce the TPN-list structure, an extended version of the N-list structure, which is used to represent and discover the WUIPs. A TPN-list intersection algorithm is then developed, which has linear complexity and the ability to self-reduce its size. Several theorems are also proposed for the fast calculation and determination of a WUIP based on its TPN-list. Finally, we propose the HWUIPM algorithm, based on the above proposals, for mining WUIPs from uncertain databases. Our experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms for mining WUIPs in terms of running time, resource usage and scalability.

Reachability in arborescence packings

Fortier et al. proposed several research problems on packing arborescences and settled some of them. Others were later solved by Matsuoka and Tanigawa and by Gao and Yang. The last open problem is settled in this article. We show how to turn an inductive idea used in the latter two articles into a simple proof technique that allows to relate previous results on arborescence packings. We prove that a strong version of Edmonds’ theorem on packing spanning arborescences implies Kamiyama, Katoh and Takizawa’s result on packing reachability arborescences and that Durand de Gevigney, Nguyen and Szigeti’s theorem on matroid-based packing of arborescences implies Király’s result on matroid-reachability-based packing of arborescences. Further, we deduce a new result on matroid-reachability-based packing of mixed hyperarborescences from a theorem on matroid-based packing of mixed hyperarborescences due to Fortier et al.. Finally, we deal with the algorithmic aspects of the problems considered. We first obtain algorithms to find the desired packings of arborescences in all settings and then apply Edmonds’ weighted matroid intersection algorithm to also find solutions minimizing a given weight function.

PMRQ: Achieving Efficient and Privacy-Preserving Multidimensional Range Query in eHealthcare

Healthcare data explosion and cloud computing booming have motivated healthcare centers to outsource their healthcare data and data-driven services to a powerful cloud. Nevertheless, due to privacy concerns, the data are usually encrypted before being outsourced, which will degrade the data utility and make it challenging to implement data-driven services. Although the multidimensional range query over encrypted data, as one of the most popular outsourced services in eHealthcare, has been extensively studied, existing solutions still have some limitations in efficiency, privacy, and practicality. Aiming at this challenge, in this article, we design an efficient and privacy-preserving multidimensional range query (PMRQ) scheme. We first build an R-tree to index the data set and reduce the R-tree-based range queries to the multidimensional range intersection problem. Then, by delicately designing a data comparison algorithm and a homomorphic encoding technique, we present an encoding-based range intersection algorithm. After that, by employing matrix encryption to protect the privacy of the encoding-based range intersection algorithm, we design a multidimensional range intersection predicate encryption (MRIPE) scheme. Based on the MRIPE scheme, we then propose our PMRQ scheme. A detailed security analysis illustrates that our PMRQ scheme is privacy preserving, and experimental results demonstrate that it is computationally efficient.

Towards Practical and Privacy-Preserving Multi-Dimensional Range Query Over Cloud

It is undeniable that Internet of Things (IoT) in big data era can provide us with huge volumes of multi-dimensional data, transforming our society into a much more intelligent one. In order to fit for the multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received considerable attention in recent years. However, as the cloud server is not fully trustable, designing multi-dimensional range queries over encrypted data becomes a research trend, and many solutions have been proposed in the literature. Nevertheless, most existing solutions suffer from the leakage of the single-dimensional privacy, and such leakage would severely put the data at risk. Although a few existing works have addressed the problem of single-dimensional privacy, they are impractical in some real scenarios due to the issues of inefficiency, inaccuracy, and two-cloud-server requirement. Aiming at solving these issues, in this article, we propose a practical and privacy-preserving multi-dimensional range query (PRQ) scheme. Specifically, in our proposed PRQ scheme, we first index the multi-dimensional dataset with an R-tree and reduce R-tree based range queries to the problem of point intersection and range intersection. Then, by employing the lightweight matrix encryption technique, we design two novel algorithms for PRQ, i.e., multi-dimensional point intersection predicate encryption (PIPE) and multi-dimensional range intersection predicate encryption (RIPE), which can preserve the privacy of the proposed point intersection algorithm and range intersection algorithm, and further preserve the single-dimensional privacy of the proposed PRQ scheme. Detailed security analysis shows that our proposed PRQ scheme is indeed privacy-preserving. In addition, extensive simulations are conducted, and the results also demonstrate its efficiency.

Pfaffian Pairs and Parities: Counting on Linear Matroid Intersection and Parity Problems

Spanning trees are a representative example of linear matroid bases that are efficiently countable. Perfect matchings of Pfaffian bipartite graphs are a countable example of common bases of two matrices. Generalizing these two, Webb Counting Bases, Ph.D. thesis, University of Waterloo, 2004 introduced the notion of Pfaffian pairs as a pair of matrices for which counting of their common bases is tractable via the Cauchy--Binet formula. This paper studies counting on linear matroid problems extending Webb's work. We first introduce “Pfaffian parities” as an extension of Pfaffian pairs to the linear matroid parity problem, which is a common generalization of the linear matroid intersection problem and the matching problem. We show that a large number of efficiently countable discrete structures are interpretable as special cases of Pfaffian pairs and parities. Our study then turns to algorithmic aspects. We observe that the fastest randomized algorithms for the linear matroid intersection and parity problems by Harvey SIAM J. Comput., 39 (2009), pp. 679--702 and Cheung, Lau, and Leung ACM Trans. Algorithms, 10 (2014), pp. 1--26 can be derandomized for Pfaffian pairs and parities. We further present polynomial-time algorithms to count the number of minimum-weight solutions on weighted Pfaffian pairs and parities. Our algorithms make use of Frank's weight-splitting lemma for the weighted matroid intersection problem and the algebraic optimality criterion of the weighted linear matroid parity problem given by Iwata and Kobayashi SIAM J. Comput., 51 (2002) pp. STOC17-238--STOC17-280.

β-catenin cancer-enhancing genomic regions axis is involved in the development of fibrolamellar hepatocellular carcinoma.

Fibrolamellar hepatocellular carcinoma (FLC) is a disease that occurs in children and young adults. The development of FLC is associated with creation of a fusion oncoprotein DNAJB1‐PKAc kinase, which activates multiple cancer‐associated pathways. The aim of this study was to examine the role of human genomic regions, called cancer‐enhancing genomic regions or aggressive liver cancer domains (CEGRs/ALCDs), in the development of FLC. Previous studies revealed that CEGRs/ALCDs are located in multiple oncogenes and cancer‐associated genes, regularly silenced in normal tissues. Using the regulatory element locus intersection (RELI) algorithm, we searched a large compendium of chromatin immunoprecipitation–sequencing (ChIP) data sets and found that CEGRs/ALCDs contain regulatory elements in several human cancers outside of pediatric hepatic neoplasms. The RELI algorithm further identified components of the β‐catenin–TCF7L2/TCF4 pathway, which interacts with CEGRs/ALCDs in several human cancers. Particularly, the RELI algorithm found interactions of transcription factors and chromatin remodelers with many genes that are activated in patients with FLC. We found that these FLC‐specific genes contain CEGRs/ALCDs, and that the driver of FLC, fusion oncoprotein DNAJB1‐PKAc, phosphorylates β‐catenin at Ser675, resulting in an increase of β‐catenin–TCF7L2/TCF4 complexes. These complexes increase a large family of CEGR/ALCD‐dependent collagens and oncogenes. The DNAJB1‐PKAc–β‐catenin–CEGR/ALCD pathway is preserved in lung metastasis. The inhibition of β‐catenin in FLC organoids inhibited the expression of CEGRs/ALCDs‐dependent collagens and oncogenes, preventing the formation of the organoid's structure. Conclusion: This study provides a rationale for the development of β‐catenin‐based therapy for patients with FLC.