Optimal Design of High-Speed Partial Flow Pumps using Orthogonal Tests and Numerical Simulations

To investigate the influence of structural parameters on the performances and internal flow characteristics of partial flow pumps at a low specific speed of 10000 rpm,special attention was paid to the first and second stage impeller guide vanes.Moreover,the impeller blade outlet width,impeller inlet diameter,blade inclination angle,and number of blades were considered for orthogonal tests.Accordingly,nine groups of design solutions were formed,and then used as a basis for the execution of numerical simulations(CFD)aimed at obtaining the efficiency values and heads for each design solution group.The influence of impeller geometric parameters on the efficiency and head was explored,and the“weight”of each factor was obtained via a range analysis.Optimal structural parameters were finally chosen on the basis of the numerical simulation results,and the performances of the optimized model were verified accordingly(yet by means of CFD).Evidence is provided that the increase in the efficiency and head of the optimized model was 12.11%and 23.5 m,respectively,compared with those of the original model.

Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.

Optimal Design of Multiple Stress Constant Accelerated Life Test Plan on Non-rectangle Test Region

For optimal design of constant stress accelerated life test（CSALT） with two-stress, if the stresses could not reach the highest levels simultaneously, the test region becomes non-rectangular. For optimal CSALT design on non-rectangle test region, the present method is only focused on non-rectangle test region with simple boundary, and the optimization algorithm is based on experience which can not ensure to obtain the optimal plan. In this paper, considering the linear-extreme value model and the optimization goal to minimize the variance of lifetime estimate under normal stress, the optimal design method of two-stress type-I censored CSALT plan on general non-rectangular test region is proposed. First, two properties of optimal test plans are proved and the relationship of all the optimal test plans is determined analytically. Then, on the basis of the two properties, the optimal problem is simplified and the optimal design method of two-stress CSALT plan on general non-rectangular test region is proposed. Finally, a numerical example is used to illustrate the feasibility and effectiveness of the method, The result shows that the proposed method could obtain the optimal test plan on non-rectangular test regions with arbitrary boundaries. This research provides the theory and method for two-stress optimal CSALT planning on non-rectangular test regions.

Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Optimal selection of tests for fault detection and isolation in multi-operating mode system

The complex systems are often in the structure of multi-operating modes, and the components implementing system functions are different under different operation modes, which results in the problems that components often fail in different operating modes, faults can be only detected in specified operating modes, tests can be available in specified operating modes,and the cost and efficiency of detecting and isolating faults are different under different operating modes and isolation levels. Aiming at these problems, an optimal test selection method for fault detection and isolation in the multi-operating mode system is proposed by using the fault pair coding and rollout algorithm. Firstly,the faults in fault-test correlation matrices under different operating modes are combined to fault-pairs, which is used to construct the fault pair-test correlation matrices under different operating modes.Secondly, the final fault pair-test correlation matrix of the multioperating mode system is obtained by operating the fault pair-test correlation matrices under different operating modes. Based on the final fault pair-test correlation matrix, the necessary tests are selected by the rollout algorithm orderly. Finally, the effectiveness of the proposed method is verified by examples of the optimal test selection in the multi-operating mode system with faults isolated to different levels. The result shows that the proposed method can effectively mine the fault detection and isolation ability of tests and it is suitable for the optimal test selection of the multi-operating mode system with faults isolated to the replacement unit and specific fault.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

Bayesian optimal design of step stress accelerated degradation testing

This study presents a Bayesian methodology for de- signing step stress accelerated degradation testing （SSADT） and its application to batteries. First, the simulation-based Bayesian de- sign framework for SSADT is presented. Then, by considering his- torical data, specific optimal objectives oriented Kullback-Leibler （KL） divergence is established. A numerical example is discussed to illustrate the design approach. It is assumed that the degrada- tion model （or process） follows a drift Brownian motion; the accele- ration model follows Arrhenius equation; and the corresponding parameters follow normal and Gamma prior distributions. Using the Markov Chain Monte Carlo （MCMC） method and WinBUGS software, the comparison shows that KL divergence is better than quadratic loss for optimal criteria. Further, the effect of simulation outiiers on the optimization plan is analyzed and the preferred sur- face fitting algorithm is chosen. At the end of the paper, a NASA lithium-ion battery dataset is used as historical information and the KL divergence oriented Bayesian design is compared with maxi- mum likelihood theory oriented locally optimal design. The results show that the proposed method can provide a much better testing plan for this engineering application.

Inference and optimal design on step-stress partially accelerated life test for hybrid system with masked data

Under Type-Ⅱ progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations(MLEs)of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover,two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria.Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.

Laboratory Research on Effective Test Area of Short-Crested Waves Generated by Two-Sided Segmented Wavemakers

The size and shape of the effective test area are crucial to consider when short-crested waves are created by segmented wavemakers. The range of the effective test area of short-crested waves simulated by two-sided segmented wavemakers is analyzed in this paper. The experimental investigation on the wave field distribution of short-crested waves generated by two-sided segmented wavemakers is conducted by using an array of wave gauges. Wave spectra and directional spreading function are analyzed and the results show that when the main direction is at a certain angle with the normal line of wave generators, the wave field of 3D short-crested waves generated by two-sided segmented wavemakers has good spatial uniformity within the model test area. The effective test area can provide good wave environments for seakeeping model tests of various ocean engineering structures in the deep ocean engineering basin.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.

Seismic effectiveness evaluation and optimized design of tie up method for securing museum collections

To quantify the seismic effectiveness of the most commonly used fishing line tie up method for securing museum collections and optimize fixed strategies for exhibitions,shaking table tests of the seismic systems used for typical museum collection replicas have been carried out.The influence of body shape and fixed measure parameters on the seismic responses of replicas and the interaction behavior between replicas and fixed measures have been explored.Based on the results,seismic effectiveness evaluation indexes of the tie up method are proposed.Reasonable suggestions for fixed strategies are given,which provide a basis for the exhibition of delicate museum collections considering the principle of minimizing seismic responses and intervention.The analysis results show that a larger ratio of height of mass center to bottom diameter led to more intense rocking responses.Increasing the initial pretension of fishing lines was conducive to reducing the seismic responses and stress variation of the lines.Through comprehensive consideration of the interaction forces and effective securement,it is recommended to apply 20%of breaking stress as the initial pretension.For specific museum collections that cannot be effectively protected by the independent tie up method,an optimized strategy of a combination of fishing lines and fasteners is recommended.

Optimal design and dynamic impact tests of removable bollards

Anti-ram bollard systems, which are installed around buildings and infrastructure, can prevent unauthorized vehicles from entering, maintain distance from vehicle-borne improvised explosive devices （VBIED） and reduce the corresponding damage. Compared with a fixed bollard system, a removable bollard system provides more flexibility as it can be removed when needed. This paper first proposes a new type of K4-rated removable anti-ram bollard system. To simulate the collision of a vehicle hitting the bollard system, a finite element model was then built and verified through comparison of numerical simulation results and existing experimental results. Based on the orthogonal design method, the factors influencing the safety and economy of this proposed system were examined and sorted according to their importance. An optimal design scheme was then produced. Finally, to validate the effectiveness of the proposed design scheme, four dynamic impact tests, including two front impact tests and two side impact tests, have been conducted according to BSI Specifications. The residual rotation angles of the specimen are smaller than 30~ and satisfy the requirements of the BSI Specification.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Optimal Budget Spending for Software Testing under the Condition of Nonlinear Constraint

Software testing is a very important phase of the software development process. It is a very difficult job for a software manager to allocate optimally the financial budget to a software project during testing. In this paper the problem of optimal allocation of the software testing cost is studied. There exist several models focused on the development of software costs measuring the number of software errors remaining in the software during testing. The purpose of this paper is to use these models to formulate the optimization problems of resource allocation: Minimization of the total number of software errors remaining in the system. On the assumption that a software project consists of some independent modules, the presented approach extends previous work by defining new goal functions and extending the primary assumption and precondition.

Mitigation of Low Frequency Oscillations by Optimal Allocation of Power System Stabilizers: Case Study on MEPE Test System

The increases in power network and weak tie-line have led power system oscillation problems. To improve the oscillatory stability, installing the power system stabilizer (PSS) with optimal allocation is considered due to excessive cost. This paper recommends the suitable PSS locations by using eigenvalue analysis and participation factor to enhance the system oscillation damping. The effects of installed PSSs in damping local and inter-area modes of oscillations are confirmed through time domain simulation results. The effectiveness of proposed approach is tested and validated on MEPE test system. Robustness of stabilizers against dynamic response of generator speed deviation, rotor angle deviation, and response of mechanical power are observed to access the performances of PSSs.

An Optimal Design of Accelerated Degradation Tests Based on Degradation Performance

This paper mainly talks about the relationship between an optimal design of accelerated degradation tests and the degradation performance. When there is a linear relationship between the parameter of distribution and the critical values, a special pattern of accelerated degradation tests is presented by taking the critical values as accelerated variable. And the optimal test plan is obtained by minimizing the asymptotic variance of the MLE of the parameter of distribution. Finally, a numerical example is presented to illustrate the procedures of the test plan.

甘薯杂粮粉品质特征与主要理化性质的Mantel test分析

以不同品种甘薯全粉为原料,复配黄豆粉、芝麻粉制备甘薯杂粮粉,并测定其β-胡萝卜素、玉米黄质、槲皮素含量,利用曼特尔检验(Mantel test)方法探讨这3种天然成分含量与其理化指标之间的相关性,研究甘薯品种对甘薯杂粮粉品质的影响。结果表明:甘薯杂粮粉的抗氧化能力与β-胡萝卜素含量呈强正相关(r=0.80),与玉米黄质含量(r=0.26)和槲皮素含量(r=-0.14)呈现弱相关性。甘薯杂粮粉的干物质含量与β-胡萝卜素含量呈负相关(r=-0.72),与玉米黄质含量(r=0.91)和槲皮素含量(r=1.00)呈强正相关。总体表明β-胡萝卜素是甘薯杂粮粉中有效的抗氧化成分。

Optimal Multiperiodic Control for Inventory Coupled Systems: A Multifrequency Second-Order Test

A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.

Optimization and Testing for PCR System of Rice by Orthogonal Design

[Objective] This study was to screen the economic or stable PCR system of rice and detect the generality of the selected system in different molecular markers based on PCR.[Method] With DNA extracted from rice leaves by CTAB method as the template,PCR system was optimized by L16(45)orthogonal design.[Result] Clear bands were amplified from 16 different combinations,but the amplification effects and yields had difference.The most economic and applicable system was as follows:20 ng DNA template,150 μmol/L dNT...