Test Cases Research Articles

Arithmetic optimization algorithm with three-dimensional chaotic mapping in spherical coordinate system for combined economic emission dispatch problem

The objective of the Combined Economic Emissions Dispatch (CEED) problem is to reduce pollutant emissions by lowering the total cost of generating electricity while complying with all other constraints. The multiple objective functions of the CEED problem may be converted into a single objective by using a price penalty factor. Then it was solved with the Arithmetic Optimization Algorithm (AOA) with three-dimensional chaotic mapping in spherical coordinate system. Firstly, five three-dimensional chaotic mappings in Cartesian coordinate system are embedded in the Mathematical Optimization Accelerator (MOA) and the Mathematical Optimization Probability (MOP) of the original algorithm. Secondly, 15 three-dimensional chaotic mappings in spherical coordinate system are constructed from the values of 5 three-dimensional chaotic mappings in Cartesian coordinate system through the mathematical expressions of mode length, polar angle and azimuth angle in spherical coordinate system. Then through a large number of simulation experiments, five best three-dimensional chaotic mappings in the spherical coordinate system are selected to be embedded into the two parameters (MOA and MOP) of the original algorithm to better balance the algorithm's global and local searching ability. The superiority of the improved algorithm is verified by employing 12 benchmark test functions in CEC2022. Eventually, the improved optimal algorithm (IAOA-r) to solve the power system CEED problem is tested on six generating units with four different loads (150 MW, 175 MW, 200 MW and 225 MW). The results of the improved algorithm are compared and analyzed with the results of the Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Bat Algorithm (BAT), Whale Optimization Algorithm (WOA), Harris Hawk Optimization (HHO), Rat Swarm Optimization (RSO). The results demonstrate that the improved algorithm is capable of obtaining optimal fuel costs and smaller pollution emissions in every test case.

Technology adoption performance evaluation applied to testing industrial REST APIs

Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.

An efficient test case selection framework for multitenant SaaS applications using RTS4MTS

The augmentation of multitenant Software as a Service (SaaS) has significantly contributed to the success of cloud computing. To stay competitive, multitenant SaaS applications are frequently updated, necessitating regression tests. Regression testing ensures modifications do not alter existing functionalities or introduce new bugs. Rerunning all test cases is resource intensive. To minimize efforts, it's crucial to select a relevant subset of test cases. Thus, a novel framework, Regression Test Selection for Multitenant SaaS (RTS4MTS), is proposed for selective regression testing in multitenant SaaS applications. This framework uses the Functional Coverage Module (FCM) and Dependency Analysis Module (DAM) to select relevant test cases. It classifies existing test cases into two groups: those affected by modifications (need to test) and those unaffected (no need to test). Applying RTS4MTS to the multitenant SaaS MtBookTrip application demonstrated that the framework reduces the test suite size by 68% and lowers test execution time by 70%, thereby saving resources and testing costs.

Turbulent spray combustion modeling in reduced tabulation parameter space by similarity mapping

To overcome the modeling challenge from the coupling between liquid vaporization and chemical reaction in turbulent spray combustion, a similarity mapping approach is implemented to reduce the flamelet tabulation parameters. In the framework of Eulerian–Lagrangian multiphase large eddy simulations (LES), such a modelling idea is developed upon the conventional flamelet/progress variable model. The flamelet library is constructed from a series of quasi one-dimensional spray counterflow solutions, integrated with the multiple solution modes. Test cases, including the laminar spray counterflow flame and Sydney turbulent spray flame, indicate that this newly proposed model is in principle favorable to improve the numerical predictability with acceptable computational cost. Overall, the flame structure can be appropriately captured, showing better performance compared with some reported results. Novelty and significance statementA newly proposed similarity mapping spray flamelet/progress variable (SMFPV) model is implemented for turbulent spray combustion. In SMFPV, the number of entry parameters of the flamelet library is reasonably reduced and two-way coupling between flame and evaporation can be realized. Thus in principle, SMFPV is favorable to improve the numerical predictability with acceptable computational cost. Simulation results of test cases justify the modelling idea.

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

Comparative analysis of two static var compensator models in voltage control of transmission network

The static var compensator (SVC) is a member of the family of flexible alternating current transmission systems controllers used in power system engineering to manage specific transmission network characteristics to enhance the performance of the transmission networks and thus increase the networks’ reliability. Power system engineers typically find it difficult to choose which SVC model to implement for simulations. This research aims to address this issue by conducting a comparative examination of two key SVC models on a transmission network. The two models of SVC variable shunt susceptance and firing angle were mathematically modeled and methodically included into the Newton-Raphson power flow algorithm for the network power flow solution. The IEEE 30-bus network was adopted as the test case, and the method was implemented in the MATLAB/Simulink environment. The network performance metric utilized was the voltage profile of the network. The two SVC models were successful in enhancing the network's performance; however, the variable shunt susceptance model was computationally faster than the firing angle model, as revealed by the simulation results. Therefore, among the two SVC models, the variable shunt susceptance model may be taken into account for simulation to enhance the performance of the transmission networks.

CNN-based weight estimation from point clouds obtained from walking breed sows

In the livestock industry, reducing the cost of raising animals while maintaining quality is of utmost importance. Growth management is crucial for livestock quality control, with weight being a primary focus. This study proposes a method for estimating the weight of a breed sow from point clouds using a convolutional neural network (CNN). The proposed method consists of two stages: first, 2D images are generated from point clouds of a walking breed sow; next, these images are used as input in a CNN model to estimate the weight. The point clouds are divided into two halves along a longitudinal plane. Then, distances are calculated between each element of the 128 × 64 rectangular mesh created on the aforementioned plane and the corresponding points in the divided point cloud. A 2D image is generated by varying the grayscale intensity according to the calculated distances. Three types of CNNs, namely, VGG16, DenseNet121, and EfficientNet B0, were employed to estimate the weight using the generated 2D images as input. The CNNs were trained using approximately 150,000 images of 71 pigs. To verify the effectiveness of the method, the trained networks were used to conduct weight estimation experiments on test cases. The results of the experiments demonstrate a high weight estimation accuracy, with an error rate as low as 1.35%.

Numerical simulation of an efficient circular solar air heater integrated with a CPC

A numerical analysis of a solar air heater is conducted to evaluate the thermal performance of the proposed solar collector under various situations for providing high-temperature airflows. The research investigates the design of a circular solar air heater including revolving airflow combined with a compound parabolic concentrator to enhance incident radiation. The two principal components of the circular heater and the applied concentrator possess compatible geometries and are well integrated. The numerical CFD study is conducted by solving the Navier-Stokes and energy equations for the primary forced convection airflow in the circular solar air heater and the free convection airflow within the cavity of the compound parabolic concentrator. Multiple test cases with varying solar irradiation and air mass flow rates are analyzed, incorporating surface-to-surface radiation. The CFD study of the circular solar air heater is compared with the experimental results. In the analyzed test cases with an air mass flow rate of 0.01 kg/s and a solar heat flux of 1000 W/m2, the thermal efficiency is calculated to be 70 %, despite the elevated outlet air temperature reaching 120 °C. This indicates a 100 % increase in efficiency compared to conventional smooth duct solar air heaters. The research concludes that the circular solar air heater with revolving airflow, combined with a compound parabolic concentrator, results in a highly efficient heat exchanger for generating high-temperature airflow suitable for many applications.

Impact analysis of uncertainty in thermal resistor-capacitor models on model predictive control performance

Model Predictive Control (MPC) is extensively utilized for optimal control in building systems. Despite substantial research being dedicated to exploring the impact of uncertainties in external and internal disturbances on the performance of MPC, the existing studies neglect the potential impact of uncertainties in model parameter identification on control performance. To address this gap, this study quantifies the impact of model uncertainty on MPC performance through a test case in a virtual environment. Various levels of uncertainties for parameters R and C are artificially introduced to assess the MPC performance. The causes of the impact of model uncertainty on control performance are further explored through analysis. We select a first-order RC model to modelling building thermal dynamics. MPC is employed to optimize the heat pump signal with the goal of minimizing the energy cost while maintaining thermal comfort. The simulation results demonstrate that a negative deviation in model parameter identification has a more pronounced impact on MPC performance than a positive deviation, which has a negligible effect on MPC control performance. Deviations in parameters from their true values affect both heat losses from the zone and thermal capacity, thereby influencing the estimated temperature by the RC model. Consequently, these factors, in turn, affect the system’s control decisions, leading to variations in the objective function values. This study can provide an insight into the relationship of model parameters uncertainties and MPC performance and facilitate the practical application of MPC in buildings.

Optimizing reserve-constrained economic dispatch: Cheetah optimizer with constraint handling method in Static/Dynamic/Single/ Multi-Area Systems

Managing the power-generating units on the horizon of one-day scheduling considering all practical equality, inequality, and realistic constraints has always been a significant challenge in power systems. The constraints, such as valve-point effects, prohibited operation zones, transmission losses, and ramp rate limits corresponding to dynamic economic dispatch, change the optimization problem to a complex, nonlinear, non-smooth, high-dimensional, and non-convex one. Therefore, an efficient algorithm and a suitable constraint handling method are needed to solve practical constrained dynamic economic dispatch (DED). This paper proposes a newly developed Cheetah optimizer (CO) that coincides with a backward-forward constraint handling method to tackle the optimum operational cost. The CO algorithm’s performance is verified using eight DED and ED test cases from five different systems. The suggested technique is compared with several state-of-the-art optimization algorithms regarding the effectiveness of achieved results. Numerical results evaluate the performances of the CO advantages on the benchmarks and the DED cases where the results of 5-,10- and 30-unit systems are enhanced in different cases. To achieve a higher level of realism in modeling the ED and DED problem, adopting a multi-area DED (MADED) approach has emerged as a promising strategy. In this paper, three distinct cases of MAED and MADED problems are investigated to demonstrate the effectiveness of the proposed method. Specifically, in cases involving DED-10 and 30 units, two-area 40 units ED, and four-area 40 units DED, significantly improved solutions were obtained compared to previous studies.

ESM-scan-A tool to guide amino acid substitutions.

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.

Improved convergence speed using hybrid AI for TD EM modeling in power electronics

This paper presents a time-domain (TD) approach based on hybrid artificial intelligence (AI) to speed up convergence of radiating sources characterization in power electronics. To obtain a representative equivalent model of device under test, a dedicated optimization framework has been developed in TD using a particle swarm optimization (PSO) toolbox. In addition, for elementary feature extraction, a pseudo-Zernike moment invariant (PZMI) descriptor has been defined. Finally, with the aim of identifying remaining dipole parameters and classification problems, artificial neural networks (ANN) have been implemented. A coupling of TD electromagnetic (EM) inverse method based on a PSO algorithm along with PZMI and ANN application has been investigated and applied to a real test case. Experimental measurements have been conducted using the near-field scanning technique above an alternating current (AC)/direct current (DC) converter. Obtained results are discussed based on a comparison between measured and estimated EM field distributions using both the hybrid AI method and a conventional TD inverse method based on genetic algorithms (GA) only. This study confirms that, compared with those given by non-hybrid method, the proposed algorithm further improves the convergence speed while maintaining high accuracy. Hence, the present work offers an impressive perspective for radiated emissions characterization using hybrid AI algorithms.

Optimizing residential flexibility for sustainable energy management in distribution networks

In search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy.

Physics-informed neural networks and higher-order high-resolution methods for resolving discontinuities and shocks: A comprehensive study

Addressing discontinuities in fluid flow problems is inherently difficult, especially when shocks arise due to the nonlinear nature of the flow. While handling discontinuities is a well-established practice in computational fluid dynamics (CFD), it remains a major challenge when applying physics-informed neural networks (PINNs). In this study, we compare the shock-resolving capabilities of traditional CFD methods with those of PINNs, highlighting the advantages of the latter. Our findings show that PINNs exhibit less dissipative behavior compared to conventional techniques. We evaluated the performance of both PINNs and traditional methods on linear and nonlinear test cases, demonstrating that PINNs offer superior shock-resolving properties. Notably, PINNs can accurately resolve inviscid shocks with just three grid points, whereas traditional methods require at least seven points. This suggests that PINNs are more effective at resolving shocks and discontinuities when using the same grid for both PINN and CFD simulations. However, it’s important to note that PINNs, in this context, are computationally more expensive than traditional methods on a given grid.

A Prioritization Method for Combinatorial Test Cases Based on Convolutional Neural Network with Wide Multi-Layer Kernels and Its Application

When the quantity of parameters or values in systems being tested is substantial, there is a significant escalation in the number of combinatorial test cases. The execution of all test cases will require a substantial allocation of time and resources. Prioritization technology enables early detection of system faults and enhances test efficiency. Most existing prioritization methods rely on historical empirical data, which can be challenging to obtain in many cases. In the meantime, random prioritization often leads to lower fault detection rates. This paper presents a prioritization method for combinatorial test cases based on a convolutional neural network (CNN) model. The weights of suspected fault-inducing interactions are initially extracted through a convolution operation in the subset of upfront test cases. Second, the features related to fault-inducing interactions are derived using wide multi-layer kernels convolutional neural network (WCNN). Third, the deep WCNN-SVM model undergoes training and makes predictions on the entire set of test cases. The predicted results are then combined with weights to prioritize combinatorial test cases. Test cases of equal priority are adjusted based on distance entropy. Application experiments on UAV demonstrate that the proposed method effectively enhances both fault detection speed and fault detection rate.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

Performance evaluation of uranium enrichment cascades using fuzzy based harmony search algorithm

The production of energy in nuclear reactors needs enrichment of fuels. There is some interest in taking the fuel enrichment level to 3–5% by cascades. Optimization of the isotopic cascades is essential to make this process economic. This study presents a Fuzzy-based Harmony Search (FHS) algorithm aimed at dynamic parameter adaptation as well as establishing a balance between exploration and exploitation, which significantly increases the convergence speed of the algorithm. Accelerating the convergence of the algorithm is demonstrated in the Sphere, Schwefel, Ackley, and Drop-Waves benchmarks at first. This approach also enhances performance in several test cases of optimum cascade problems, with results validated through comparisons with conventional methods. According to the results, the total number of centrifuges using FHS reached 6306 in test case 1, which was reduced 44 pieces compared to the method used by Palkin, and 55 pieces compared to the real coded genetic algorithm. The total number of centrifuges using FHS reached 2808 in test case 4 with a different type of gas centrifuge, which decreased 27 pieces compared to the direct search method. Similar results were obtained in other test cases, indicating the effectiveness of the FHS algorithm in minimizing the total number of centrifuges and total flow rates.

Improved weighted sum estimation of total radiated power at W7-X

AbstractAs magnetic confinement devices move toward higher fusion powers, moderating the heat load to the plasma-facing components becomes increasingly challenging. Efficient power dissipation can be achieved through control of the plasma radiation. However, defining a reliable proxy for the total radiated power is particularly challenging for non-axisymmetric devices such as stellarators. To address this problem, the radiated power can be estimated through a sum of the individual line-integrated bolometer measurements with weights properly calculated to account for the three-dimensional magnetic geometry. The present contribution aims to apply this weighted sum approach to Wendelstein 7-X (W7-X) and quantitatively validate it. First, we generate synthetic radiated power phantoms with characteristic W7-X radiation features to derive a set of optimized line-of-sight weights. Then, we test the weights on mock-ups and EMC3-EIRENE radiation patterns, including acquisition and analysis errors such as random noise fluctuations, camera misalignments, and field errors. Compared to other methods, the optimized weighted sum technique exhibited the best performance in all the presented synthetic test cases. When applied to experimental bolometer data, the optimized weights provided a proxy that is both reliable and real-time capable. Further validation is foreseen for the next experimental campaign.

Online public opinion prediction based on a novel conformable fractional discrete grey model

PurposeAs social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.Design/methodology/approachFirst, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.FindingsThe test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.Originality/valueA conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

Formal Verification of Code Conversion: A Comprehensive Survey

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its original form. Formal verification emerges as a crucial methodology to address these limitations. Although numerous surveys have explored formal verification in various contexts, a significant research gap exists in pinpointing appropriate formal verification approaches to code conversion tasks. This paper provides a detailed survey of formal verification techniques applicable to code conversion. This survey identifies the strengths and limitations of contemporary adopted approaches while outlining a trajectory for future research, emphasizing the need for automated and scalable verification tools. The novel categorization of formal verification methods provided in this paper serves as a foundational guide for researchers seeking to enhance the reliability of code conversion processes.