Configuration Problem Research Articles

An a priori method for estimating the informativeness of the configuration of sensor placement when solving inverse problems of remote sensing

Abstract The question of finding the optimal location and number of sensors is important when solving applied inverse problems of remote sensing. An incorrect answer to this question can significantly affect (1) the accuracy of reconstructing the unknown parameters of the object under study, and/or (2) the computational complexity of the inverse problem being solved. The work proposes an economical algorithm that allows among all equivalent (in the sense of the computational complexity of obtaining a solution to the inverse problem) sensor configurations, to select the one that will give a more accurate result when processing experimental data. This algorithm also allows to make a conclusion about whether it is really necessary to process all the experimental data obtained in the experiment, or whether it is possible to limit ourselves to only part of them without a significant loss of accuracy in the reconstructed solution. The algorithm is based on (1) the application of the mosaic-skeleton approximation method to the matrix of the system of equations to which the inverse problem being solved is reduced, and (2) the calculation of the compression rate of the approximating matrix relative to the original dense matrix for a given permissible relative approximation error. Thus, the algorithm is a priori, that is, it does not require a preliminary search for a solution to the inverse problem for the sensor configuration under study. Moreover, if the formulas that define the elements of the system matrix are known, then the algorithm requires the calculation of not all of these elements, but only part of them.

Optimizing Business Process through Fault-Tolerant Scheduling in Cloud Environments: A Comparative Study

The fault tolerance study carried out in this research explores Bidirectional Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) to improve cloud computing dependability and functionality. Being an integral part of the rage for business operations, cloud-computing fundamentals of resource provisioning and fault tolerance have a bearing on the overall cost-dynamics, ROI and OpEx. Reliability covers such issues as hardware failures, configuration problems and other network issues that may have financial implications and even lead to revenue loss, and failure to meet service level agreement (SLA). The work develops a novel GAN-BiLSTM model for the accurate prediction of faults and the enhancement of recovery management, resulting in resource efficiency and cost of capital reduction (CapEx). Evaluation criteria involve deadline guarantee ratio, average task delay, and system scalability, confirming that the proposed model has better financial performance than DPSO and ANFIS. Cutting wastage of resources and increasing energy capacity in a system, the model displays attractive cost reduction and operating effectiveness for cloud service providers. In the simulation, important results of the model are demonstrated in the business continuity, financial risk reduction as well as maintaining accurate and resourceful service in high demand situations. All these developments have placed the fault tolerant systems powered by machine learning as indispensable instruments that can also enhance profitability, resources utilisation and sustainable competitiveness in the cloud computing business.

Heating dictates the scalability of CO2 electrolyzer types.

Electrochemical CO2 reduction offers a promising method of converting renewable electrical energy into valuable hydrocarbon compounds vital to hard-to-abate sectors. Significant progress has been made on the lab scale, but scale-up demonstrations remain limited. Because of the low energy efficiency of CO2 reduction, we suspect that significant thermal gradients may develop in industrially relevant dimensions. We describe here a model prediction for non-isothermal behavior beyond the typical 1D models to illustrate the severity of heating at larger scales. We develop a 2D model for two membrane electrode assembly (MEA) CO2 electrolyzers; a liquid anolyte fed MEA (exchange MEA) and a fully gas fed configuration (full MEA). Our results indicate that full MEA configurations exhibit very poor electrochemical performance at moderately larger scales due to non-isothermal effects. Heating results in severe membrane dehydration, which induces large Ohmic losses in the membrane, resulting in a sharp decline in the current density along the flow direction. In contrast, the anolyte employed in the exchange MEA configuration is effective in preventing large thermal gradients. Membrane dehydration is not a problem for the exchange MEA configuration, leading to a nearly constant current density over the entire length of the modeled domain, and indicating that exchange MEA configurations are well suited for scale-up. Our results additionally indicate that a balance between faster kinetics, higher ionic conductivity, smaller pH gradients and lower CO2 solubility causes an optimum operating temperature between 60 and 70 °C.

Multi-lattice sampling of quantum field theories via neural operator-based flows

Abstract We consider the problem of sampling lattice field configurations on a lattice from the Boltzmann distribution corresponding to some action. Since such densities arise as approximationw of an underlying functional density, we frame the task as an instance of operator learning. We propose to approximate a time-dependent neural operator whose time integral provides a mapping between the functional distributions of the free and target theories. Once a particular lattice is chosen, the neural operator can be discretized to a finite-dimensional, time-dependent vector field which in turn induces a continuous normalizing flow between finite dimensional distributions over the chosen lattice. This flow can then be trained to be a diffeormorphism between the discretized free and target theories on the chosen lattice, and, by construction, can be evaluated on different discretizations of spacetime. We experimentally validate the proposal on the 2-dimensional φ 4-theory to explore to what extent such operator-based flow architectures generalize to lattice sizes they were not trained on, and show that pretraining on smaller lattices can lead to a speedup over training directly on the target lattice size.

Rational configuration problems and a family of curves

Given ▪, we consider the number of rational points on the genus one curveHη:y2=(a(1−x2)+b(2x))2+(c(1−x2)+d(2x))2. We prove that the set of η for which Hη(Q)≠∅ has density zero, and that if a rational point (x0,y0)∈Hη(Q) exists, then Hη(Q) is infinite unless a certain explicit polynomial in a,b,c,d,x0,y0 vanishes.Curves of the form Hη naturally occur in the study of configurations of points in Rn with rational distances between them. As one example demonstrating this framework, we prove that if a line through the origin in R2 passes through a rational point on the unit circle, then it contains a dense set of points P such that the distances from P to each of the three points (0,0), (0,1), and (1,1) are all rational. We also prove some results regarding whether a rational number can be expressed as a sum or product of slopes of rational right triangles.

Service product family optimization design for demand-driven older adult home care.

Diversification of demand leads to a wide range of older adults home care services and services market flexibility. In order to quickly respond to changing market demand, during the standard functioning of individual services, enterprises will choose the optimal combination of packages to meet the market's expected demand and preferences to ensure that the enterprise can be in the uncertainty of the market regulation and has a relatively stable source of customers. The ability of companies to design older adults care service product families based on their operational strategy and financial position, thereby increasing their ability to enter emerging markets, is a key challenge. Firstly, the demand of the initial older adults group is obtained. Secondly, the attributes of each service item are classified using the Kano model. Finally, the actual operation constraints of the enterprise are incorporated, taking into account customer preference. Critical configuration problems such as module type, customer demand, probability of selection, and cost are also considered. The genetic algorithm is used to maximize the enterprise profit while considering customer preference. The efficacy of the approach is illustrated by the design exemplification of an older adult home care services firm. This research suggests a novel method to product family design for older adults care services. The method determine the optimal older adults home care service product architecture system. The article discusses three aspects of model parameters, potential constraints and extended application of the model for different contexts to expand the applicability and relevance of the study.

Measurement configuration for integrated electric–gas systems via observability analysis considering gas flow dynamics

AbstractState estimation plays an important role in the monitoring and control of integrated electric–gas systems (IEGSs), but it faces limitations due to insufficient measurement configurations and low data redundancy in these systems; additional measurement configurations are needed to increase the overall system observability. Owing to the lack of suitable observability analysis methods, optimal measurement configurations for IEGSs remain underexplored. This paper presents an IEGS observability analysis method that incorporates gas flow dynamics via the Lie derivative. This method incorporates the complex topological structure of the gas network and the dynamic process of gas flow into the IEGS observability analysis. Furthermore, the measurement configuration problem for IEGSs considering gas flow dynamics is formulated as a rank‐constrained optimization problem. To handle the rank constraints effectively, an iterative cutting method is developed with convergence guarantees. Finally, the efficacy and practicality of the proposed methods are validated through case studies of varying scales. The proposed optimal measurement configuration model reduces measurement configuration costs while maintaining system observability.

A bi-objective optimization framework for configuration of battery energy storage system considering energy loss and economy

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Simulating adiabatic quantum computing with parameterized quantum circuits

Abstract Adiabatic quantum computing is a universal model for quantum computing whose implementation using a gate-based quantum computer requires depths that are unreachable in the early fault-tolerant era. To mitigate the limitations of near-term devices, a number of hybrid approaches have been pursued in which a parameterized quantum circuit prepares and measures quantum states and a classical optimization algorithm minimizes an objective function that encompasses the solution to the problem of interest. In this work, we propose a different approach starting by analyzing how a small perturbation of a Hamiltonian affects the parameters that minimize the energy within a family of parameterized quantum states. We derive a set of equations that allow us to compute the new minimum by solving a constrained linear system of equations that is obtained from measuring a series of observables on the unperturbed system. We then propose a discrete version of adiabatic quantum computing that can be implemented in a near-term device while at the same time is insensitive to the initialization of the parameters and to other limitations hindered in the optimization part of variational quantum algorithms. We compare our proposed algorithm with the variational quantum eigensolver on two classical optimization problems, namely MaxCut and number partitioning, and on a quantum-spin configuration problem, the transverse-field ising chain model, and confirm that our approach demonstrates superior performance.

Systematic Layout Planning and Analytical Hierarchy Process for Laboratory Layout Optimization: A Case Study of DESPRIN

The Product Design and Innovation Laboratory (DESPRIN) at Del Institute of Technology plays a crucial role in supporting innovation. Yet, its current layout struggles to meet its users' diverse and evolving needs. This study addresses the problem of inadequate spatial configuration, which hampers workflow efficiency, ergonomics, and the lab's overall capacity to accommodate various activities. To resolve this issue, a flexible layout is designed using the Systematic Layout Planning (SLP) method, emphasizing optimizing space utilization, workflow, and spatial adaptability. Data collected from literature reviews, interviews, statistical analyses, and anthropometric measurements inform the design process to enhance the lab’s quality and spatial efficiency. The Analytical Hierarchy Process (AHP) is employed to prioritize key layout design factors, identifying capacity as the most critical element (0.4930), followed by facilities (0.1688), accessibility (0.1414), security (0.1270), and environment (0.0708). The study results in 13 new layout configurations that can accommodate various activities within DESPRIN, providing a more dynamic and responsive user environment.

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.

A novel and easy‐to‐implement modulation scheme for reducing modulation complexity in modular multilevel converter

AbstractModulation strategy plays a key role in the operation of modular multilevel converters (MMC). Although many studies have been carried out on its optimization in MMC, the limitations of hardware implementation are seldom considered. This paper presents a novel and easy‐to‐implement modulation strategy that simplifies both hardware design and application. From the analysis in this paper, the widely‐used modulation methods have the problems of pulse width modulation (PWM) signal feedback and complex carrier configuration in hardware implementation. By setting up a flexible insertion and removal submodule and proposing a capacitor voltage balance algorithm based on duty ratio allocation, the proposed modulation method can allow for the separate design of the Algorithm Unit and the PWM Unit in the hardware, which solves the problem of PWM feedback and reduces the hardware requirement of the trigger system. Moreover, the proposed method can broaden the selection range of processor chips. Finally, a downscaled MMC prototype is built; the proposed method is verified by both simulation and experiment.

SPH simulations of complex 3D fluid–solid interactions with an improved single-layer particle boundary technique preventing boundary penetration

The smoothed particle hydrodynamics (SPH) method has been widely used to simulate fluid–solid interaction (FSI) problems. The solid boundary treatment under complex geometric configurations has always been a hot topic in the SPH numerical simulation. An improved single-layer particle technique combined with an effective anti-penetration boundary improvement algorithm is proposed in this paper, which allows for multi-resolution single-layer particle distribution and effectively prevents particle penetration under complex geometric configurations. Four engineering problems are simulated with the improved technique: dam-break flows with a rectangular prism, water entry of a revolution body, oscillations of a floating body, and gearbox operation. The simulation results of four engineering problems are in good agreement with the reference results, which demonstrates that the improved single-layer particle boundary technique proposed in this paper can accurately simulate the FSI problem of complex geometric configurations.

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.

Multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

PurposeThe multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting goals.Design/methodology/approachA multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current coefficient.Findings(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure intensifies.Originality/valuei) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. The NSGA-III is combined with the decision-making model to address the multi-objective optimization configuration problem of C0.

Characterization of rigid open-cell foams using direct ultrasonic simulation.

An ultrasonic simulation technique based on the direct fluid model is proposed as an alternative to the analogous experimental technique to determine the tortuosity and characteristic lengths for high pore-density foams. It is beneficial as it reduces cost and almost eliminates the signal-to-noise issues encountered in the experiment. The proposed method is demonstrated for periodic microlattices with three different unit-cell configurations, 75%-90% porosity, and a pore size of about 200 microns. The technique is also applicable to high-resolution computed tomography (CT) scans of open-cell foams with a priori unknown microporous structure. An acoustic simulation software, ACTRAN® (Hexagon AB, Stockholm, Sweden), is used to model and perform analysis of the ultrasonic pulse propagation through the foam. Based on through-transmission by foam saturated with two different mediums, the tortuosity, and characteristic lengths are estimated from the high-frequency asymptotic behavior of the square of the propagation index (Nr2) versus the inverse square root of frequency (1/f). The predicted parameters are validated by comparing them with those determined by solving the electric conduction boundary value problem for the same configuration. Further, detailed parametric sensitivity analysis reveals the sensitivity of the Johnson-Champoux-Allard parameters to errors in Nr2 and so the effect of these errors on the acoustic absorption behavior of the rigid porous sample.

Outage-guaranteed transmission for IRS-assisted FSO systems.

Intelligent reflecting surface (IRS)-assisted free space optical (FSO) communication can overcome line-of-sight and bring spatial diversity, which is considered to be a good complement to direct FSO links. This paper is interested in understanding the role of IRS-assisted FSO links when coexisting with direct FSO links. Due to the diverse nature of existing FSO channel models and the reliability requirements of IRS-assisted FSO systems, this paper aims to investigate the transmission problems of link selection and power configuration under the quality of service guarantee for link outage, which can be applicable to different channel models. Despite the inherent complexity of the problems, transmission schemes, i.e., sum rate transmission (SRT) and rate fairness transmission (RFT) are developed, which can achieve the respective optimal performance. Also, some insights for transmission schemes are provided. The results verify the efficacy of the proposed transmission schemes. The performance of the system may be improved by 40% and 41% for SRT and RFT, respectively.

Research on power allocation strategy and capacity configuration of hybrid energy storage system based on double-layer variational modal decomposition and energy entropy

This paper deals with the study of the power allocation and capacity configuration problems of Hybrid Energy Storage Systems (HESS) and their potential use to handle wind and solar power fluctuation. A double-layer Variable Modal Decomposition (VMD) strategy is proposed. Firstly, using the Sparrow Search Algorithm with Sine-cosine and Cauchy mutation (SCSSA) to optimize the parameters in VMD. Considering the different characteristics of batteries and supercapacitors and the richness of information that may be contained in the high frequency residuals obtained from the primary VMD, the secondary VMD is carried out to decomposition. In response to the modal aliasing phenomenon of the secondary VMD, the high and low frequency demarcation points of the secondary VMD are discriminated by combining the energy entropy (eN) to enable the more accurate power allocation strategy. Secondly, a mathematical model for cost analysis of the HESS is established by considering the daily integrated operating cost of the HESS and the relevant constraints of the renewable generation system. Finally, HESS capacity configuration and validation are based on the power allocation strategy proposed in this paper. The simulation results show that the power allocation strategy proposed in this paper is more accurate and has a better economy for the capacity configuration of HESS.

Emotional Intelligence in the Labor of University Lecturers

The paper examines the problem of the emotional intelligence configuration among lecturers and the importance of the role that this construct plays in their professional activities. The author notes the need to practice emotional intelligence in higher educational institutions to prevent teacher burnout syndrome. The study presented survey results of employees of the Department of English Language and Professional Communication of the Financial University conducted with based on the Rotterdam Emotional Intelligence Scale (REIS) methodology. The author concluded the data obtained can develop and coordinate actions to create a specialized course. This course aims to prevent professional burnout among lecturers by focusing on the major components development of emotional intelligence.