Set Of Algorithms Research Articles

Modeling of dynamic bridging of solid particles in multiple propagating fractures

During multi-cluster fracturing, solid particles are widely used to alleviate the non-uniform fluid distribution and fracture growth. Even though diversion technology has been applied and proved effective in numerous field applications, many involved physical mechanisms, such as solid diverters transport, settling, bridging, and packing in multiple propagating fractures, remain unclear. In this work, a computational mechanics system is developed to interpret the coupling process of fluid flow, fracture propagation, and particle transport. The moving fracture front is located using the implicit level set algorithm (ILSA) and tip asymptotic solution. The diverter particle transport model can capture the settling, bridging, and packing of particles; thus the slurry flow in the fracture can transit from Poiseuille flow to Darcy flow naturally. Application of the developed system to simulate a single stage of three equally spaced clusters indicates that it is possible to create multiple fractures with uniform length using diversion technology, but their widths are non-uniform. Instead, they demonstrate pinched or ballooned shapes, because of the stress interference. An extensive parametric study is also performed to investigate how various parameters affect diversion efficiency.

Impact of dose calculation accuracy on inverse linear energy transfer optimization for intensity‐modulated proton therapy

AbstractObjectiveTo determine the effect of dose calculation accuracy on inverse linear energy transfer (LET) optimization for intensity‐modulated proton therapy, and to determine whether adding more beams would improve the plan robustness to different dose calculation engines.MethodsTwo sets of intensity‐modulated proton therapy plans using two, four, six, and nine beams were created for 10 prostate cancer patients: one set was optimized with dose constraints (DoseOpt) using the pencil beam (PB) algorithm, and the other set was optimized with additional LET constraints (LETOpt) using the Monte Carlo (MC) algorithm. Dose distributions of DoseOpt plans were then recalculated using the MC algorithm, and the LETOpt plans were recalculated using the PB algorithm. Dosimetric indices of targets and critical organs were compared between the PB and MC algorithms for both sets of plans.ResultsFor DoseOpt plans, dose differences between the PB and MC algorithms were minimal. However, the maximum dose differences in LETOpt plans were 11.11% and 15.85% in the dose covering 98% and 2% (D2) of the clinical target volume, respectively. Furthermore, the dose to 1 cc of the bladder differed by 11.42 Gy (relative biological effectiveness). Adding more beams reduced the discrepancy in target coverage, but the errors in D2 of the structure were increased with the number of beams.ConclusionHigh modulation of LET requires high dose calculation accuracy during the optimization and final dose calculation in the inverse treatment planning for intensity‐modulated proton therapy, and adding more beams did not improve the plan robustness to different dose calculation algorithms.

On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features

People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those. This paper investigates the prediction of the occurrence of flight arrival delays with three prominent machine learning algorithms for a data set of domestic flights in the USA. The task is regarded as a classification problem. The focus lies on the investigation of the influence of short-term features on the quality of the results. Therefore, three scenarios are created that are characterised by different input feature sets. When forgoing the inclusion of short-term information in order to shift the prediction timing to an early point in time, an accuracy of 69.5% with a recall of 68.2% is achieved. By including information on the delay that the aircraft had on its previous flight, the prediction quality increases slightly. Hence, this is a compromise between the early prediction timing of the first model and the good prediction quality of the third model, where the departure delay of the aircraft is added as an input feature. In this case, an accuracy of 89.9% with a recall of 83.4% is obtained. The desired timing of prediction therefore determines which features to use as inputs since short-term features significantly improve the prediction quality.

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

DHash: Dynamic Hash Tables With Non-Blocking Regular Operations

Once started, existing hash tables cannot change their pre-defined hash functions, even if the incoming data cannot be evenly distributed to the hash table buckets. In this paper, we present DHash, a type of hash table for shared memory systems, that can change its hash function and rebuild the hash table on the fly, without noticeably degrading its service. The major technical novelty of DHash stems from an efficient distributing mechanism that can atomically distribute every node when rebuilding, without locking the corresponding hash table buckets. This not only enables non-blocking lookup, insert, and delete operations, but more importantly, makes DHash independent of the implementation of hash table buckets, such that DHash allows programmers to select the set algorithms that meet their requirements best from a variety of existing lock-free and wait-free set algorithms. Evaluations show that DHash can efficiently change its hash function on the fly. Moreover, when rebuilding, DHash consistently outperforms the state-of-the-art hash tables in terms of throughput and response time of concurrent operations, at different concurrency levels, and with different operation mixes and average load factors.

Rough Set: Utilizing Machine Learning for the Covid-19 Vaccine

Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community’s interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated.

Single-phase Grounding Fault Section Locating Technology for Distribution Network Based on Rough Set Algorithm

With the continuous development of distribution networks, the proportion of the power electronic equipment that accesses distribution networks is increasing, which increases the system capacitance current, making the probability of single-phase grounding fault via intermittent arc in the system significantly increased. However, most traditional locating technologies are suitable for single grounding fault type via stable resistance, and cannot locate the fault above, which seriously impairs the reliability of the power supply of distribution networks. In order to solve the problem above, this paper puts forward the single-phase grounding fault section locating technology for the distribution network based on a rough set algorithm. This technology is able to obtain the phase current signals in the system for judgment, and analyze multiple characteristics of current signals with a rough set algorithm, thus determining the fault types in the system correctly. Moreover, it is also able to determine fault sections with proper section locating algorithms based on different fault types. Furthermore, it is able to locate sections of various fault types, such as resistance fault and fault via intermittent arc, with an expanded feasible region. Besides, it only makes a judgment based on phase current without collecting the zero-sequence voltage of the system, ensuring adaptability. Additionally, it is able to supplement and optimize fault type identification categories by adding condition attributes of fault characteristic analysis in the rough set algorithm, having certain engineering application values. In the end, the feasibility of this locating technology was verified with ATP power system simulation software.

Comprehensive studies of Grassmann manifold optimization and sequential candidate set algorithm in a principal fitted component model

Comprehensive studies of Grassmann manifold optimization and sequential candidate set algorithm in a principal fitted component model

Applications of Bar Code to Involutive Divisions and a “Greedy” Algorithm for Complete Sets

Given a finite set of terms U in n variables, we describe an algorithm which finds – if it exists – an ordering on the variables such that U is a complete set according to Janet involutive division. The algorithm, based on Bar Codes for monomial ideals, is able to adjust the variables ordering with a sort of backtracking technique, thus allowing to find the desired ordering without trying all the n! possible ones.

Alternatives to a nonhomogeneous partial differential equation quantum algorithm

Recently J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $A\psi(\textbf{r})=f(\textbf{r})$. Its nonhomogeneous solution is obtained by inverting the operator $A$ along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for a specific set of inputs. These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.

Simulations of Organic Aerosol with CAMx over the Po Valley during the Summer Season

A new sensitivity analysis with the Comprehensive Air Quality Model with Extensions (CAMx) using a traditional two-product scheme (SOAP) and the newer Volatility Basis Set (VBS) algorithm for organic aerosol (OA) calculations is presented. The sensitivity simulations include the default versions of the SOAP and VBS schemes, as well as new parametrizations for the VBS scheme to calculate emissions and volatility distributions of semi- and intermediate-volatile organic compounds. The focus of the simulations is the summer season (May to July 2013), in order to quantify the sensitivity of the model in a period with relatively large photochemical activity. In addition to the model sensitivity, we validate the results with ad hoc OA measurements obtained from aerosol mass spectrometers at two monitoring sites. Unlike winter cases previously published, the comparison with experimental data showed limited sensitivity to total OA amount, with an estimated increase in OA concentrations limited to a few tenths of µg m−3, for both the primary and secondary components. We show that the lack of pronounced sensitivity is related to the effect of the new parametrizations on different emissions sectors. Furthermore, the minor sensitivity to the new parametrizations could be related to the greater partitioning of OA towards the gaseous phase in the summer period, thus reducing the organic fraction in the aerosol phase.

Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data.

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.

Two-step scalable spectral clustering algorithm using landmarks and probability density estimation

Spectral clustering is one of the most important clustering approaches, often yielding performance superior to other clustering approaches. However, it is not scalable to large data sets in its original form due to the computational burden of the required large-matrix eigen-decomposition. In this paper, a two-step spectral clustering algorithm is introduced by extending recent advances of scalable spectral clustering based on low-rank affinity matrix using landmarks. In the first step, a scalable spectral clustering algorithm using raw landmark-based affinity matrix is adopted. In the second step, a novel low-rank affinity matrix is learnt via the probability density estimators, constructed from the estimated clusters as derived from the first step. Since the prior information on cluster labels can be utilised in the second step, this learnt affinity matrix reflects intrinsic pairwise data relationships much better. While the proposed more complicated algorithm results in a higher computational cost than the previous landmark-based spectral research, it can be shown that the associated computational cost still scales well with data size. It is demonstrated that the proposed algorithm is capable of achieving far superior performance than other state-of-the-art algorithms for several benchmark multi-class image data sets.

An Asynchronous Maximum Independent Set Algorithm By Myopic Luminous Robots On Grids

Abstract We consider the problem of constructing a maximum independent set with mobile myopic luminous robots on a grid network whose size is finite but unknown to the robots. In this setting, the robots enter the grid network one by one from a corner of the grid, and they eventually have to be disseminated on the grid nodes so that the occupied positions form a maximum independent set of the network. We assume that robots are asynchronous, anonymous, silent and they execute the same distributed algorithm. In this paper, we propose two algorithms: The first one assumes that the number of light colors of each robot is three and the visible range is two, but uses the additional assumption that a local edge-labeling exists for each node. To remove this assumption, the second one assumes that the number of light colors of each robot is seven, and that the visible range is three. In both algorithms, the number of movements is $O(n(L+l))$ steps, where $n$ is the number of nodes and $L$ and $l$ are the grid dimensions.

Optimization of service scheduling problem for overlapping tower cranes with cooperative coevolutionary genetic algorithm

PurposeIn regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.Design/methodology/approachThe cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.FindingsThe computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.Originality/valueThis paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

Levy Equilibrium Optimizer algorithm for the DNA storage code set.

The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high density, low energy consumption and high durability. DNA coding is to store data information with as few base sequences as possible without errors. Coding is a key technology in DNA storage, and its results directly affect the performance of storage and the integrity of data reading and writing. In this paper, a Levy Equilibrium Optimizer (LEO) algorithm is proposed to construct a DNA storage code set that satisfies combinatorial constraints. The performance of the proposed algorithm is tested on 13 benchmark functions, and 4 new global optima are obtained. Under the same constraints, the DNA storage code set is constructed. Compared with previous work, the lower bound of DNA storage code set is improved by 4-13%.

Reduction Algorithm of Interval-Valued Intuitionistic Fuzzy Probability Rough Set Under Dominant Relation

Attribute reduction is a part of the most basic and significant research contents in rough set theory. The so-called attribute reduction is the smallest independent subset that keeps certain properties of information table unchanged. In this paper, four interval-valued intuitionistic fuzzy probabilistic rough set models and their natures are given on the basis of the dominant-, inferior- and interval-valued intuitionistic fuzzy probabilistic rough set models. At the same time, the interval-valued intuitionistic fuzzy numbers are transformed by fuzzy degree, and the approximate accuracy and approximate classification quality under the dominance relation are used for reduction, at last, the feasibility is verified by an example.

Automated analysis method for high throughput nanoindentation data with quantitative uncertainty

High throughput nanoindentation techniques can provide rapid materials screening and property mapping and can span millimeter length scales and up to 106 data points. To facilitate rapid sorting of these data into similar groups, a necessary task for establishing structure–property relationships, use of an unsupervised machine learning analysis called clustering has grown in popularity. Here, a method is proposed and tested that evaluates the uncertainty associated with various clustering algorithms for an example high entropy alloy data set and explores the effect of the number of data points in a second Damascus steel data set. The proposed method utilizes the bootstrapping method of Efron to resample a modeled probability distribution function based upon the original data, which allows the uncertainty related to the clustering to be evaluated in contrast to the classical standard error on the mean calculations. For the Damascus, it was found that results data from a 104 point subsample are comparable to those from the full 106 set while representing a significant reduction in data acquisition.

Optimized convolutional neural network for glaucoma detection with improved optic-cup segmentation

Glaucoma is the foremost cause of permanent loss of visioahn; it develops slowly with no discernible sign. Early glaucoma recognition is critical because it might aid to slow the progression of the disease. Customary schemes are less precise and manual. As a result, automatic glaucoma analysis is required to identify glaucoma at the earliest with greater precision. The purpose here is to bring in a new scheme in which pre-processing is done using Gaussian filtering, which aids in the removal of unwanted noise in images. Then Optic Cup segmentation is performed using the Modified Level Set Algorithm. Followed by segmentation, the morphological features (disc area, cup area, and blood vessel), as well as non-morphological features (Color, Shape, and Modified LBP), are derived. The Blood vessel thickness is 5 to 100 micrometers. These features are then classified using the Optimized CNN framework, where the weights get optimized via the Self Adaptive Butterfly Optimization Algorithm (SA-BOA). The precision of the developed approach was 21.16%, 7.35%, 6.62%, 2.98%, 4.29%, 3.89%, 5.67%, 6.23%, 6.79%, and 1.63% better than the values obtained for conservative techniques Similarly, the adopted model's negative metrics show negligible values when compared to other models. Thus, the proposed method supremacy was validated successfully.

Modeling of solids particle diversion to promote uniform growth of multiple hydraulic fractures

Solid particulate additives are sometimes used to promote the uniform growth of multiple hydraulic fractures in horizontal oil and gas wells. The principle is that solid particulates block, accumulate, and form larger porous plugging zones preferentially at entrances of fracture taking in the most fluid volume. These porous zones create fluid flow resistance or additional pressure loss; thereby, inhibiting the growth of these dominant fractures and diverting fluid to suppressed fractures. While this technology is promising, governing design parameters and ramifications of placing solids diverters inside the fracture remain unclear. This paper models the propagation of multi-fractures with diverter pressure losses induced by the porous plugging zones. The resulting non-linear hydraulic fracturing problem is solved numerically with an Implicit Level Set Algorithm (ILSA) for each time step and the mechanisms of diversion are illustrated by comparing and contrasting cases with and without particle diverter. In both cases, during the fluid ramp-up period (pumping rate gradually increases from 0 to fracturing rate (QT)), the injection can be equally distributed among fractures before the stress interference affects the fluid allocation (Phase I). Then, stress interference starts to partition more fluid into outer fractures and suppress the growth of the middle fracture (Phase II). Once the perforation friction loss is sufficient to counteract the stress interaction, injection begins to shift to the middle fracture, but still gives a significantly non-uniform fracture growth (Phase III). At this point, solid diverter particles are introduced, leading to three additional phases of growth. Phase IV introduces solid diverters to the treatment at a reduced pumping rate. Particles bridge, accumulate and create porous plugging zones at the flow entrance. A higher pressure drop in outer fractures diverts injection fluids to the middle fracture. Phase V resumes the treatment rate to QT without diverter. The increased pump rate in turn increases the pressure drop in outer fractures and diverts more fluids to the middle fracture. This results in a rapid extension velocity for the middle fracture, enabling it to have the chance to catch up with the longer outer fractures (in Phase VI). This process is controlled by the interplay among stress interference, perforation friction loss, and diverting pressure drop. These simulations demonstrate that a model-based optimization could improve the effectiveness of the diverter technology and promote a uniform multi-fracture growth.