Evaluation Of Algorithms Research Articles

Matching pursuit algorithm for cement evaluation behind the casing

The oil and gas sector makes extensive use of ultrasonic tool technology for casing and cement examination for well integrity. This requires the determination of the thickness of the casing, its internal structure, and the mechanical properties of the cement behind the casing. In essence an ultrasonic instrument analyses the ultrasonic pulse echoes returned off the internal surface of the casing. To determine the acoustic impedance of the material behind the casing, it can be difficult to distinguish between the primary reflection from the interior surface of the casing and the ones due to the ringing inside the casing wall itself. The primary reflection offers details regarding the internal geometry of the casing, and the decay of the energy within the casing wall reveals more about how well the cement is bonded to the casing. The ultrasonic signals that the transducer receives when it functions as a receiver are easily contaminated by noise, or part of the reflection may be blocked, or the main waveform and the many ringing waveforms are difficult to separate. As a result, the echo signals will have frequency and phase drift or be deformed. To address the various energy packets and extract the pertinent component independently, a matching pursuit algorithm was developed with a specific dictionary to deal with the different energy packets and extract the relevant component separately. The algorithm was tested on simulated and field data.

An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain

Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy-and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty-and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the effective selection valve</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the effective expectation of merit</i> are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.

Approximate Method to Compute Hypersingular Finite-Part Integrals with Rapidly Oscillating Kernels

In this paper, an algorithm for the numerical evaluation of hypersingular finite-part integrals with rapidly oscillating kernels is proposed. The method is based on an interpolatory procedure at zeros of the orthogonal polynomials with respect to the first kind Chebyshev weight. Bounds of the error and of the amplification factor are also provided. Numerically stable procedures are obtained and the corresponding algorithms can be implemented in a fast way.

Crowdsourcing Deep Learning Algorithms to Automatically Contour GI Luminal Organs on Serial MRIs

A major barrier to a sustainable real-time adaptive MR-guided radiotherapy workflow is the time-consuming process of contouring the target and organs-at-risk (OARs) before the delivery of each fraction. While auto-contouring algorithms perform relatively well for many solid organs, the performance on luminal organs in the abdomen remain difficult due to the variability between patients and variability in daily shape and position. The purpose of this study is to evaluate the performance of crowdsourced deep learning algorithms to automatically contour GI luminal organs on serial MRIs. The stomach, small intestines, and large intestines were manually contoured on MRIs from patients who had undergone radiotherapy on an MR-Linac by a team of radiation therapists and medical physicists and were verified by a board-certified radiation oncologist. The MRIs and the contours were de-identified and uploaded to Kaggle, an online machine learning competition platform with portion of the data open to the public as training data and the remaining data hidden as a test set. Prize money was offered to teams submitting the best auto-contouring algorithms based on the Dice coefficient and Hausdorff distance evaluation metrics. The average performance of the winning algorithm and of manual contours were compared using unpaired t-test. Four hundred sixty-seven MRIs were collected from 107 patients who underwent 1-5 serial MRI sessions between 2015 and 2019. The most common anatomic site of treatment was the pancreas with 41 patients, followed by the liver with 38 patients. The manual contours of the stomach, small intestines, and large intestines on 4 representative MRIs had mean and standard deviation Dice coefficient of 0.90 +/- 0.02, 0.76 +/- 0.04, and 0.85 +/- 0.03 respectively and Hausdorff distance of 18.0 +/- 6.9, 35.5 +/- 12.6, and 32.3 +/- 12.3 mm respectively. The Kaggle competition was held from April to July 2022 and 1548 teams submitted algorithms for evaluation. The auto-contouring performance of the winning solution on the stomach, small intestines, and large intestines, when evaluated on a hold-out test set with 188 MRIs, had mean and standard deviation Dice coefficient of 0.92 +/- 0.04, 0.80 +/- 0.09, 0.85 +/- 0.08 respectively and Hausdorff distance of 15.2 +/- 11.2, 33.9 +/- 15.2, 34.8 +/- 20.3 mm respectively. Unpaired t-test was performed to compare the average performance across three organs of the human (N = 120) and of the algorithm (N = 564). The results suggest that average algorithm performance was statistically superior to manual contours for Dice (p = 0.01), yet not for Hausdorff (p = 0.64). Crowdsourced deep learning algorithms to auto-contour GI luminal organs on serial MRIs perform superiorly compared to manual contours when using a Dice coefficient metric but not when using a Haudorff distance metric. These auto-contouring algorithms may be used to efficiently adapt radiotherapy plans according to the anatomy of the day for patients with abdominal tumors on MR-Linacs.

Retracted: Adoption of Magnetic Resonance Image Features under Segmentation Algorithm in Effect Evaluation of Ginkgo Diterpenoid Lactone Glucamine Injection in Treatment of Cerebral Infarction.

[This retracts the article DOI: 10.1155/2022/4558702.].

Algorithm for fast evaluation of in-plane fiber orientation in reinforced plastics using light microscopy images

Methods for evaluating the orientation of carbon fibers in reinforced plastics vary in complexity and are application specific. Here, we report an algorithm that quickly evaluates in-plane fiber orientation based on determining the correlation coefficient of adjacent regions in microscopy images. The result is not the fiber orientation of individual fibers, but the principal fiber orientation of small image areas. This method is applicable to large areas due to its low computation time and captures varying fiber orientations, making it suitable for the study of injection molded samples with complex geometries. A great advantage is that no information about the fibers or the matrix, or their combination, is needed prior to the analysis. This approach is also suitable for samples with surface defects. Demonstrations of this technique are given for Polyamide 6 and Polypropylene with 30 weight % carbon fibers of different fiber lengths.

Deep adaptive temporal network (DAT-Net): an effective deep learning model for parameter estimation of radar multipath interference signals

Accurate parameter estimation in radar systems is critically hindered by multipath interference, a challenge that is amplified in complex and dynamic environments. Traditional methods for parameter estimation, which concentrate on single parameters and rely on statistical assumptions, often struggle in such scenarios. To address this, the deep adaptive temporal network (DAT-Net), an innovative deep learning model designed to handle the inherent complexities and non-stationarity of time series data, is proposed. In more detail, DAT-Net integrates both the pruned exact linear time method for effective time series segmentation and the exponential scaling-based importance evaluation algorithm for dynamic learning of importance weights. These methods enable the model to adapt to shifts in data distribution and provide a robust solution for parameter estimation. In addition, DAT-Net demonstrates the capability to comprehend inherent nonlinearities in radar multipath interference signals, thereby facilitating the modeling of intricate patterns within the data. Extensive validation experiments conducted across parameter estimation tasks and demonstrates the robust applicability and efficiency of the proposed DAT-Net model. The architecture yield root mean squared error scores as low as 0.0051 for single-parameter estimation and 0.0152 for multiple-parameter estimation.

Research and Application of Multi-model-based Comprehensive Evaluation and Scale Prediction Algorithm for Eco-Regional Economy

This paper establishes a mathematical model to analyze the data of Saihanba area, and establishes a prediction and evaluation model to evaluate the ecological zone planning and carbon neutrality. This paper reviewed a large number of literature and screened out 15 factors, then established a factor screening model based on Pearson correlation coefficient, drew a correlation heat map, and then screened out 8 factors to establish a model for evaluating the impact of Sehampa on the ecological environment based on EWM-TOPSIS. Thirty-four major observation points in China are selected for data collection, then the environmental scores of the 34 observation points are calculated based on the 2020 data, and then the scores are visualized as heat maps. Finally, this paper establishes a time-series prediction model to compare and predict China's current carbon stock, with a view to providing some reference for other fields.

A new type of bridge structure reinforcement effect evaluation algorithm based on time series

A supplement method based on reusing the original bridge structure is suggested to expedite construction and reduce costs. Furthermore, theoretical analysis of the single pile bearing capacity is conducted, primarily involving the calculation of the bridge pile foundation settlement, horizontal resistance and axial bearing capacity. At the same time, considering that the time series data generated in the actual process often have characteristics of imbalance between classes, a cost-sensitive hybrid network model is also constructed. Its significance is the analysis of bridge reinforcement performance data. The experimental results show that compared with the cross-entropy loss function, the introduction of a cost-sensitive loss function can significantly improve the monitoring accuracy of abnormal data sets. The ACC– score, G-means and F-measures are higher than those of the specific cross-entropy loss function. The permissible bearing capacity of muddy clay determined using a network model complies with engineering standards in the evaluation of bridge reinforcement performance. As a result, the reliability of this form of reinforcement is improved.

Utilization of Rectal Examinations Before Magnetic Resonance Defecography Studies.

More information is needed to guide referring subspecialists on the appropriate patient evaluation before magnetic resonance defecography (MRD). This study aimed to evaluate how often health care providers perform digital rectal examination (DRE) before ordering MRD to investigate causes of bowel and pelvic floor complaints. We conducted a retrospective cohort review, including MRD performed on female patients at an integrated health care system from 2016 through 2020. The primary outcome was the presence or absence of documented rectal examination in the year before defecography by the referring provider or 6 months prior by a primary care physician or pelvic floor physical therapist. We hypothesized that the overall rate of rectal examination would be high and unaffected by the referring provider's subspecialty. Three hundred-four defecography tests were performed, with 209 patients (68.8%) referred by gastroenterology providers and 95 (31.2%) from other specialties. Gastroenterologists performed DRE in 32.8% of patients, in contrast to 84.4% of patients referred by other specialties ( P < 0.001). When comparing subspecialties that most commonly refer patients for MRD (gastroenterology, colorectal surgery and urogynecology), there was a statistically significant difference between gastroenterologists and colorectal surgeons ( P < 0.001) as well as urogynecologists ( P < 0.001) but no difference in the rate of rectal examination between colorectal surgeons and urogynecologists ( P = 1.00). At our single integrated health system, the rate of DRE before MRD testing varied significantly by specialty. Our findings highlight the need for better understanding of DRE utility in the algorithms for evaluation of bowel and pelvic floor disorders.

Design of teaching quality evaluation system for higher vocational education based on data mining technology

Abstract It is advantageous for schools to realize exact education management, the cornerstone for developing abilities, by constructing higher vocational education teaching quality evaluation systems. The division of frequent item sets based on the modified Apriori algorithm under the association rule algorithm is used for the evaluation algorithm of this system to examine the evaluation data in the evaluation system, which is created in this work using data mining technology. Data validation was carried out from system performance and application analysis of association rules for the teaching quality evaluation system constructed in this paper. Regarding system performance, the highest response time of the system in this paper is 8.34s, which is about 56.94% and 40.68% shorter than that of the evaluation system based on AHP and gray correlation theory, respectively. This shows that using association rule algorithms can realize the operation of the evaluation system faster, improve evaluation efficiency, and use visual data to achieve precise education management in schools.

Development of zero-noise extrapolated projective quantum algorithm for accurate evaluation of molecular energetics in noisy quantum devices.

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue normlandscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately.

Integration of the methods of algebra of algorithms and computational intelligence for automation of program systems design

An approach to the creation of intelligent systems is considered, which combines the ideas of Glushkov’s algebra of algorithms with the well-known neuroevolutionary algorithms of computational intelligence, which can be used to automate the design and synthesis of programs. The method of neuroevolution of augmenting topologies is intended to reduce the dimensionality of the space for searching for neural network parameters in the form of gradual development of its structure in the process of evolution. A software toolkit has been developed to support the approach, which is based on the construction of high-level specifications of algorithms represented in systems of algorithmic algebras, and generation of corresponding programs based on implementation templates in a target programming language. Parameterized algorithms called hyperschemes are used to solve a certain class of problems. Setting specific values of parameters and subsequent interpretation of hyperschemes allows obtaining algorithms adapted to specific application conditions. The developed program design and synthesis toolkit provides step-by-step development of programs, starting from a high-level algebraic-algorithmic specification. At the output of the toolkit, a program is automatically generated in one of the target programming languages (С, С++, Java, Python), that also include a subject-oriented language for designing neural networks. The mentioned language includes operators and conditions for working with populations, configurations, genomes, and fitness function. The work of the toolkit is illustrated by the example of designing a parameterized evaluation algorithm for a binary multiplexer and generation of a program for the single-pole balancing problem

Automation of Winglet Wings Geometry Generation for Its Application in TORNADO

The paper outlines an algorithm for the rapid aerodynamic evaluation of winglet geometries using the TORNADO Vortex Lattice Method. It is a very useful tool to obtain a first approximation of the aerodynamic properties and for performing an optimization of the geometry design. The TORNADO tool is used to systematically calculate the aerodynamic characteristics of various wings with wingtip devices. The fast response of the aerodynamic models allows obtaining a set of results in a remarkably short time. Therefore, the development of an algorithm to generate wing geometries with great ease and complex shapes is of vital importance for the mentioned optimization process. The basic outline of the algorithm, the equations defining the wing geometries, and the results for unconventional wingtip devices, such as blended winglets and spiroid winglets, are presented. Finally, this algorithm allows designing a procedure to study the improvement of aerodynamic properties (lift, induced drag, and moment). Some examples are included to illustrate the capabilities of the algorithm.

Machine learning application in modelling marine and coastal phenomena: a critical review

This study provides an extensive review of over 200 journal papers focusing on Machine Learning (ML) algorithms’ use for promoting a sustainable management of the marine and coastal environments. The research covers various facets of ML algorithms, including data preprocessing and handling, modeling algorithms for distinct phenomena, model evaluation, and use of dynamic and integrated models. Given that machine learning modeling relies on experience or trial-and-error, examining previous applications in marine and coastal modeling is proven to be beneficial. The performance of different ML methods used to predict wave heights was analyzed to ascertain which method was superior with various datasets. The analysis of these papers revealed that properly developed ML methods could successfully be applied to multiple aspects. Areas of application include data collection and analysis, pollutant and sediment transport, image processing and deep learning, and identification of potential regions for aquaculture and wave energy activities. Additionally, ML methods aid in structural design and optimization and in the prediction and classification of oceanographic parameters. However, despite their potential advantages, dynamic and integrated ML models remain underutilized in marine projects. This research provides insights into ML’s application and invites future investigations to exploit ML’s untapped potential in marine and coastal sustainability.

Forecasting of Flood Flow of Panam River Basin using Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN with Comparative Study

Flood forecasting is one of the most important issues in the hydrology due to its indispensable contribution in lowering monetary and life losses. In recent years reliability of flood forecasting using various modelling tools has improved to a great extent due to hydrologic modelling, development in expertise and knowledge, and advancement in the collection of data and algorithms for evaluation. The current study presents the use of the Adaptive Neuro Fussy Inference System (ANFIS) in forecasting floods for the Panam River basin system. ANFIS combines neural network algorithms and fuzzy reasoning to map an input space to an output space. This paper includes the development of ANFIS models using various membership function and their comparison for forecasting the inflow rate into the Panam dam, which creates flooding conditions on the downstream side. The different evaluation parameters like Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R2) and Discrepancy Ratio (D) are used to evaluate the results of each model. From all the developed ANFIS models, the best ANFIS model is selected, having RMSE as 271.91, R as 0.98, R2 as 0.96, and D as 1.00 for training the model, and RMSE as 2000.74, R as 0.95, R2 as 0.90 and D as 1.12 for validating the model. Artificial Neural Network (ANN) model has also been developed for forecasting flood. In the Neural network there are total 3 types of transfer function in each layer i.e., LOGSIG, TANSIG and PURELIN. All the developed is evaluated with the coefficient of correlation (R) and Mean Squared Error (MSE). In the end, future headings for innovative research work are discussed.

The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the impact of urbanization. Thus, an accurate flood sensitivities assessment is a key tool for flood prevention and urban planning and development. To successfully link the urban flood sensitivity assessment with the real flood situation, a method combining clustering algorithm with comprehensive evaluation is presented. The proposed method is not affected by the classification standard of sensitivities levels and has a small and undemanding demand for flood data. First, Maximal Information Coefficient between conditional factors and flood is employed to determine the weight. Then, the different results are obtained by three clustering algorithms. Finally, a four-layer evaluation structure weighted by analytic hierarchy process is established to select the best flood susceptibility map. A case study in the Zhengzhou city, China shows that the positive scale amplification strategy is relatively best and the flood sensitivity of sub-regions in Zhengzhou city should be divided into four levels obtained by K-Means clustering. Hence, it supplies the valuable insights for the urban planning and flood mitigation.

Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration

The weapon system of systems (WSoS) is a collection of various weapon systems used to achieve a common goal and mission. Stakeholders are committed to lowering design costs while achieving greater resilience. In this paper, we establish a resilience evaluation and optimal design method for WSoS under different attack and reconfiguration strategies that can balance its resilience and cost. Firstly, a WSoS-oriented three-phase resilience model and evaluation algorithm are developed, taking into account resistance, adaptability, and recovery factors. Then, a resilience-based WSoS cost model is presented to assess the cost of new equipment, improved equipment, and resilience-associated costs. Additionally, a resilience-based optimization model is proposed that considers reduced costs and increased resilience. Non-dominated sorting in genetic algorithms: II (NSGA-II) is selected to achieve the Pareto optimal solution. Finally, a typical 5-node formation air defense WSoS is utilized to illustrate the resilience evaluation and optimization design, which can serve as a reference for WSoS design.

Known-Interference Cancellation in Cooperative Jamming: Experimental Evaluation and Benchmark Algorithm Performance

Known-Interference Cancellation in Cooperative Jamming: Experimental Evaluation and Benchmark Algorithm Performance

Efficient Anonymous Authentication and Privacy-Preserving Reliability Evaluation for Mobile Crowdsensing in Vehicular Networks

Mobile crowdsensing (MCS) is widely applied in vehicular networks where several sensing vehicles complete the same sensing task. Recently, the privacy and reliability of sensing vehicles have aroused extensive attention of researchers in academia. Although the majority of existing schemes have achieved anonymous authentication with large computation and communication overheads, they do not take the reliability of sensing vehicles into account when selecting sensing vehicles. In this paper, we propose an efficient anonymous authentication and privacy-preserving reliability evaluation (AARE) scheme for MCS, which not only improves the efficiency of mutual authentication but also guarantees the reliability of sensing vehicles. Specifically, an efficient anonymous authentication method is proposed to achieve the anonymous authentication of sensing vehicles. Besides, even if a sensing vehicle passes anonymous authentication, it is difficult to fully guarantee its reliability. Then, a privacy-preserving reliability evaluation algorithm is adopted to evaluate the reliability of sensing vehicles. Meanwhile, in dynamic vehicular networks, the reliability of sensing vehicles is uncertain since there exist many attacks, thus, an accurate reputation update algorithm is designed. Subsequently, the privacy features, computation, and communication overheads in the proposed scheme are analyzed. Comparisons with the existing schemes show that the authentication efficiency of the proposed scheme is increased by 46%-74%, the communication overhead is reduced by 50%-80%, and the accuracy of reputation values of sensing vehicles which submit the reliable/unreliable sensing data is improved/reduced by 16%-22%/67%-74%.