Optimal Operator Research Articles

Sensitivity Analysis and Comparative Study for Different Detection Modes of Logging-While-Drilling Ultradeep Azimuthal Electromagnetic Tools

Summary The logging-while-drilling (LWD) ultradeep azimuthal electromagnetic tool plays a pivotal role in real-time drilling optimization operations. Established tool designs include arrays of coaxial and tilted coils that, during drilling operations, can be processed to a multicomponent magnetic induction data. These data can then be combined into different detection modes, which accentuate sensitivity to particular geological features. Leveraging the established coil design and definitions of various detection modes for an electromagnetic look-ahead (EMLA) tool, this study undertakes a comprehensive exploration of the disparities in detection performance and characterization of subsurface parameters. Through sensitivity analysis, the varying degrees of sensitivity exhibited by these detection modes concerning parameters such as subsurface formation resistivity, formation inclination, and electrical anisotropy have been investigated. The ensuing conclusions derived from an in-depth analysis are as follows: Detection Mode I exhibits remarkable prowess in delineating subsurface boundaries. Optimal exploration distances can be achieved through the judicious selection of source-receiver distances and frequencies. Detection Mode II displays heightened sensitivity to wellbore inclination and anisotropy, effectively elucidating subsurface resistivity anisotropy. This sensitivity is particularly pronounced at wellbore inclinations approaching 60°. Detection Mode III, while lacking directional capability, nonetheless furnishes fundamental insights into subsurface resistivity. Detection Mode IV demonstrates exceptional sensitivity to electrical anisotropy, particularly at higher wellbore inclinations, manifesting a conspicuous response to subsurface resistivity anisotropy. In summary, the diverse detection modes within the realm of ultradeep azimuthal electromagnetic technology each offer distinctive attributes, facilitating optimal mode selection to attain superior outcomes as per specific requisites. This research contributes significantly to an enhanced comprehension of the performance and applicability of the ultradeep azimuthal electromagnetic tool in the field of optimal drilling.

Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield.

Transcranial ultrasound imaging assumes a growing significance in the detection and monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial differential equation (PDE) is one of the prerequisites for obtaining transcranial ultrasound wavefields. Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. The fact that physics-informed neural network can only target the same model limits its application. In addition, compared to time-domain approaches, frequency-domain solutions offer advantages of reducing computational complexity and enabling stable and accurate inversions. Therefore, we introduce a framework called FD-embedded UNet (FEUNet) for solving frequency-domain transcranial ultrasound wavefields. The PDE error is calculated using the optimal 9-point FD operator, and it is integrated with the data-driven error to jointly guide the network iterations. We showcase the effectiveness of this approach through experiments involving idealized skull and brain models. FEUNet demonstrates versatility in handling various input scenarios and excels in enhancing prediction accuracy, especially with limited datasets and noisy information. Finally, we provide an overview of the advantages, limitations, and potential avenues for future research in this study.

Sustainable Road Infrastructure Decision-Making: Custom NSGA-II with Repair Operators for Multi-Objective Optimization

The integration of sustainability principles into the structural design and decision-making processes for transportation infrastructure, particularly concerning reinforced concrete precast modular frames (RCPMF), is recognized as crucial for ensuring outcomes that are environmentally responsible, economically feasible, and socially beneficial. In this study, this challenge is addressed, with the significance of sustainable development in modern engineering practices being underscored. A novel approach, which is a combination of multi-objective optimization (MOO) with multi-criteria decision-making (MCDM) techniques, is proposed, tailored specifically for the design and selection of RCPMF. The effectiveness of three repair operators—statistical-based, random, and proximity-based—in optimizing economic, environmental, and social objectives is evaluated. Precise evaluation of objective functions is facilitated by a customized Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, complemented by a detailed life cycle analysis (LCA). The utilization of simple additive weighting (SAW) and fair un choix adéquat (FUCA) methods for the scoring and ranking of the MOO solutions has revealed that notable excellence in meeting the RCPMF design requirements is exhibited by the statistical-based repair operator, which offers solutions with lower impacts across all dimensions and demonstrates minimal variability. MCDM techniques produced similar rankings, with slight score variations and a significant correlation of 0.9816, showcasing their consistent evaluation capacity despite distinct operational methodologies.

An Improved Discrete Bat Algorithm for Multi-Objective Partial Parallel Disassembly Line Balancing Problem

Product disassembly is an effective means of waste recycling and reutilization that has received much attention recently. In terms of disassembly efficiency, the number of disassembly skills possessed by workers plays a crucial role in improving disassembly efficiency. Therefore, in order to effectively and reasonably disassemble discarded products, this paper proposes a partial parallel disassembly line balancing problem (PP-DLBP) that takes into account the number of worker skills. In this paper, the disassembly tasks and the disassembly relationships between components are described using AND–OR graphs. In this paper, a multi-objective optimization model is established aiming to maximize the net profit of disassembly and minimize the number of skills for the workers. Based on the bat algorithm (BA), we propose an improved discrete bat algorithm (IDBA), which involves designing adaptive composite optimization operators to replace the original continuous formula expressions and applying them to solve the PP-DLBP. To demonstrate the advantages of IDBA, we compares it with NSGA-II, NSGA-III, SPEA-II, ESPEA, and MOEA/D. Experimental results show that IDBA outperforms the other five algorithms in real disassembly cases and exhibits high efficiency.

THERMOGRAPHIC STUDY IN THE LODANA-UTM EXTENSION PHOTOVOLTAIC POWER PLANT FOR THE PREVENTION OF BREAKDOWNS

Objective: Detect the hot spots present in the photovoltaic modules, to know what type of maintenance they need to achieve good energy performance. Methods: The bibliographic review method, the inductive-deductive and the experimental method were applied, through visual inspection and infrared thermography to determine the number of panels that are affected, using the temperature variation technique. Results: It was obtained that 7.41% need corrective maintenance, 11.11% preventive maintenance and 81.48% predictive maintenance, this maintenance must be carried out with the objective that the photovoltaic generator does not lose its optimal operability during its work process and decrease its performance, this prediction method is done automatically, the procedure allows you to organize the detection and classification process more effectively. Conclusions: Thermography is a passive, non-contact measurement method. Thermal images show the temperature distribution on the surface of the object. Thermal imaging technology has become one of the most valuable diagnostic tools for predictive maintenance by detecting anomalies that are not normally visible to the naked eye. These can prevent costly system failures before they occur, thereby achieving that reduces the occurrence of breakdowns, increases the overall performance factor, improves the profitability conditions of the project, reduces costs associated with its operation.

Learning to select the recombination operator for derivative-free optimization

Learning to select the recombination operator for derivative-free optimization

Holographic matrix method for rapid material structural feature extraction and detection

The extraction of the structural features of materials is fundamental for investigating novel properties in fields such as electronic information and biochemistry. However, existing experimental methods have limitations in analyzing material structures with sufficient depth. Therefore, rapid and accurate extraction and analysis of structural features from atomic coordinates obtained through simulation calculations are crucial for advancing the exploration of new material properties. Herein, we propose an approach for extracting the structural features of materials by combining the holographic matrix method with Bayesian optimization and tensor flow operations. The proposed algorithm efficiently classifies and statistically analyzes cluster structures within materials. Experimental validation conducted on a system comprising 8000 atoms demonstrated a correct recognition rate exceeding 99.213%. Moreover, the algorithm achieved an average recognition time of approximately [Formula: see text][Formula: see text]s. The proposed analytical framework exhibits scalability and robustness, establishing an algorithmic foundation for future advancements in big data analytics for complex materials.

Exact Non-Markovian Quantum Dynamics on the NISQ Device Using Kraus Operators.

The theory of open quantum systems has many applications ranging from simulating quantum dynamics in condensed phases to better understanding quantum-enabled technologies. At the center of theoretical chemistry are the developments of methodologies and computational tools for simulating charge and excitation energy transfer in solutions, biomolecules, and molecular aggregates. As a variety of these processes display non-Markovian behavior, classical computer simulation can be challenging due to exponential scaling with existing methods. With quantum computers holding the promise of efficient quantum simulations, in this paper, we present a new quantum algorithm based on Kraus operators that capture the exact non-Markovian effect at a finite temperature. The implementation of the Kraus operators on the quantum machine uses a combination of singular value decomposition (SVD) and optimal Walsh operators that result in shallow circuits. We demonstrate the feasibility of the algorithm by simulating the spin-boson dynamics and the exciton transfer in the Fenna-Matthews-Olson (FMO) complex. The NISQ results show very good agreement with the exact ones.

Offline and online task allocation algorithms for multiple UAVs in wireless sensor networks

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms.

Investigation on unstable flow characteristics and energy dissipation in Pelton turbine

The internal flow scale of a Pelton turbine is variable, and the interaction between the jet and the bucket has strong transient characteristics, resulting in an incomplete understanding of its internal vortex structure evolution and energy dissipation mechanisms. In order to reveal the influence of vortices on the flow regime and energy dissipation mechanisms in the turbine, this paper establishes the correlation between the unsteady flow characteristics and energy dissipation inside the Pelton turbine based on the energy balance equation and quantifies the energy losses inside the turbine. The results indicate that vortex structures present a non-uniform distribution inside the jet, which disrupts the uniformity of the jet velocity distribution, resulting in an uneven distribution of high-vorticity zones on the bucket surfaces and intensifying flow interference among the buckets. The strong shear flow caused by the downstream flow detachment of the needle guide, the turbulent boundary layer on the jet surface, and the wake effect downstream of the needle tip are the main reasons for the dissipation of fluid kinetic energy. The distribution range of energy loss on the rotating bucket closely corresponds to the position of the high-vorticity zone. Energy dissipation inside the turbine is primarily in the form of turbulent kinetic energy, and the injectors and runner are the primary energy dissipation components. Moreover, inside the injectors, each form of energy loss remains relatively constant, whereas inside the runner, its rotation induces fluctuating energy losses attributed to Reynolds stress work. The results contribute to an enhanced understanding of the energy dissipation characteristics and complex flow mechanisms within the turbine, providing reference for the optimisation and efficient operation of the multi-nozzle Pelton turbine.

An Efficient Reinforcement Learning Based Framework for Exploring Logic Synthesis

Logic synthesis is a crucial step in electronic design automation tools. The rapid developments of reinforcement learning (RL) have enabled the automated exploration of logic synthesis. Existing RL based methods may lead to data inefficiency, and the exploration approaches for FPGA and ASIC technology mapping in recent works lack the flexibility of the learning process. This work proposes ESE, a reinforcement learning based framework to efficiently learn the logic synthesis process. The framework supports the modeling of logic optimization and technology mapping for FPGA and ASIC. The optimization for the execution time of the synthesis script is also considered. For the modeling of FPGA mapping, the logic optimization and technology mapping are combined to be learned in a flexible way. For the modeling of ASIC mapping, the standard cell based optimization and LUT optimization operations are incorporated into the ASIC synthesis flow. To improve the utilization of samples, the Proximal Policy Optimization model is adopted. Furthermore, the framework is enhanced by supporting MIG based synthesis exploration. Experiments show that for FPGA technology mapping on the VTR benchmark, the average LUT-Level-Product and script runtime are improved by more than 18.3% and 12.4% respectively than previous works. For ASIC mapping on the EPFL benchmark, the average Area-Delay-Product is improved by 14.5%.

General approach on the best fitted linear operator and basis function for homotopy methods and application to strongly nonlinear oscillators

In this study, we propose a universal mathematical guideline for the selection of auxiliary linear operator L(u) and the basis function u0(x), which are the principal parts of Homotopy methods: Homotopy Perturbation Method (HPM) and Homotopy Analysis Method (HAM). We assume the coefficients of derivatives involved in L(u) as a function of auxiliary roots of L(u)=0. Based on the residual error minimization we compute unknown roots and thereby obtain the optimal linear operator at an order of approximation. Also, an effort is made to explore the role of the number of unknowns embedded into L(u) (for different choices of real roots of L(u)=0) on the rate of converges of the solution and CPU uses. Hence the best fitted linear-operator and corresponding basis function are proposed for a particular problem. Also, we propose to discretize the exact square residual using Simpson’s 1/3 algorithms, and compare its accessibility in comparison to other existing residual calculation methods. Initially, we applied our algorithms to three IVPs (Logistic, Stiff system and Riccati system) and three singular BVPs, and then finally to a strongly nonlinear oscillator (cubic-quintic Duffing equation). We compare our technique’s accuracy, efficiency and simplicity with other contemporary analytical and numerical methods. It is demonstrated that our optimal linear operator, which is updated at every order of approximation, is much more efficient, important (than the artificial parameters and functions of developed HAM), and self-sufficient for the convergence of series solutions. Our approach is more effective, straightforward and easy to use when applied to many nonlinear problems arising in mechanics and engineering. It is seen for the initial value problems that our technique provides the globally convergent series solution, while the solutions by HPM and optimal HAM are locally convergent. This novel development will make homotopy methods more powerful.

A reinforcement learning-based neighborhood search operator for multi-modal optimization and its applications

In this paper, a reinforcement learning-based neighborhood search operator (RLNS) is proposed for multi-modal optimization problems where the main novelties lie in the reinforcement learning-based neighborhood range selection strategy, the neighborhood subpopulation generation strategy and the local vector encirclement model. The reinforcement learning-based neighborhood range selection strategy is proposed to dynamically adjust the subpopulation size to address the issue of too many parameters to be adjusted in the multi-modal optimization algorithm based on the niching methods, while the neighborhood subpopulation generation strategy and the local vector encirclement model are designed with the hope of enhancing the individual’s ability to local exploitation to obtain more accurate solutions. To verify the effectiveness of the proposed RLNS, SSA-RLNS, PSO-RLNS and EO-RLNS are proposed by integrating the proposed RLNS with the existing sparrow search algorithm, particle swarm optimization and equilibrium optimizer. The performances of the proposed SSA-RLNS, PSO-RLNS, EO-RLNS and existing multi-modal optimization algorithms are tested in CEC2015 multi-niche benchmark functions. The experimental results show that the SSA-RLNS, PSO-RLNS and EO-RLNS could locate multiple global optimal solutions with satisfactory accuracy, which illustrate that the proposed RLNS could be successfully used to deal with multi-modal optimization problems by integrating with common population-based optimization algorithms. Finally, the SSA-RLNS is successfully applied in the inverse kinematics of robot manipulator.

Determining the optimal generalization operators for building footprints using an improved graph neural network model

Determining the optimal generalization operators of city buildings is a crucial step during the building generalization process and an important aspect of realizing cross-scale updating of map data. It is a decision-making behavior of the cartographer that can be learned and simulated using artificial intelligence algorithms. Multi-scale data can provide rich generalization samples to train the determination process. However, previous studies have focused primarily on the intelligent use of each generalization operator separately, neglecting the intelligent scheduling issue between multiple operators when they are used simultaneously. Herein, we propose an improved graph neural network (GNN) called self-neighborhood merged GNN (SNGNN) that selects the optimal generalization operators for different buildings. In SNGNN, node and edge information are passed with different weights through two modules to simulate the effects of a building on itself and the neighborhood on either side. SNGNN has been experimentally validated using sample datasets for Ningbo, China, at 1:10,000 and 1:25,000. The F1-score of the testing dataset was 94.19%, and the classification precision of each operator was ≥87%. Compared with other popular intelligent algorithms, the experimental results for SNGNN revealed better performance in determining the optimal generalization operators.

Energy saving and safe operation of Tidal Pumping Stations

Tidal pumping station pumps would consume lots of energy for pumping massive water. This paper provides an effective method to optimize operating schedules of the tidal pumping stations. A multi-objective model was proposed to minimize the total switching times and energy power. In the model, sub-periods are divided according to heads. A classical tidal pumping station, which mainly serves for the China's South to North Water Transfer Project, is performed as a case. As the energy consumption per unit pumped decreases, the switching times increases, and the operational reliability of the T-PS decreases. The optimal operation scheme reduces the switching times by about 58.824% and saves the energy consumption per unit pumped by about 3.803%, respectively. Therefore, the optimal operation scheme not only has excellent safety but also effectively reduces the operation energy consumption. Thus, this paper could provide effective optimized operation schedules for tidal-pumping stations.

FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition.

Efficient and accurate BRDF acquisition of real world materials is a challenging research problem that requires sampling millions of incident light and viewing directions. To accelerate the acquisition process, one needs to find a minimal set of sampling directions such that the recovery of the full BRDF is accurate and robust given such samples. In this article, we formulate BRDF acquisition as a compressed sensing problem, where the sensing operator is one that performs sub-sampling of the BRDF signal according to a set of optimal sample directions. To solve this problem, we propose the Fast and Robust Optimal Sampling Technique (FROST) for designing a provably optimal sub-sampling operator that places light-view samples such that the recovery error is minimized. FROST casts the problem of designing an optimal sub-sampling operator for compressed sensing into a sparse representation formulation under the Multiple Measurement Vector (MMV) signal model. The proposed reformulation is exact, i.e. without any approximations, hence it converts an intractable combinatorial problem into one that can be solved with standard optimization techniques. As a result, FROST is accompanied by strong theoretical guarantees from the field of compressed sensing. We perform a thorough analysis of FROST-BRDF using a 10-fold cross-validation with publicly available BRDF datasets and show significant advantages compared to the state-of-the-art with respect to reconstruction quality. Finally, FROST is simple, both conceptually and in terms of implementation, it produces consistent results at each run, and it is at least two orders of magnitude faster than the prior art.

ON THE OPTIMAL DISCRETIZATION OF THE SOLUTIONPOISSON’S EQUATION

The paper studies the problem of discretizing the solution of the Poisson equation with the right hand side f belonging to the multidimensional periodic Sobolev class. The research methodology is based on considering the problem of discretizing the solution of the Poisson equation as one of the concretizations of the general problem of optimal recovery of the operator Tf and using well known statements of approximation theory. Within the framework of this general optimal recovery problem, we first estimate from above the smallest discretization error N of the solution of the Poisson equation in the Hilbert metric using the discretization operator (l(N) , N) constructed from a finite set of Fourier coefficients of the function f. A lower estimate, coinciding in order with the upper estimate, for the smallest error N was obtained by involving all linear functionals defined on the multidimensional Sobolev class. It should be noted that the optimal discretization operator (l(N) , N) better approximates the solution under consideration in the Hilbert metric than any discretization operator constructed from values f at given points. Poisson’s equation is an elliptic partial differential equation and describes many physical phenomena such as electrostatic field, stationary temperature field, pressure field and velocity potential field in hydrodynamics. Therefore, the relevance of the research conducted here is beyond doubt.

Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate

As the energy demand continues to rise, renewable energy sources such as photovoltaic (PV) systems are becoming increasingly popular. PV systems convert solar radiation into electricity, making them an attractive option for reducing reliance on traditional electricity sources and decreasing carbon emissions. To optimize the usage of PV systems, intelligent forecasting algorithms are essential. They enable better decisionmaking regarding cost and energy efficiency, reliability, power optimization, and economic smart grid operations. Machine learning algorithms have proven to be effective in estimating the power of PV systems, improving accuracy by allowing models to understand complex relationships between parameters and evaluate the output power performance of photovoltaic cells. This work presents a study on the use of machine learning algorithms Catboost, LightGBM, XGboost and Random Forest to improve prediction. The study results indicate that using machine learning algorithms LightGBM can improve the accuracy of PV power prediction, which can have significant implications for optimizing energy usage. In addition to reducing uncertainty, machine learning algorithms improve PV systems’ efficiency, reliability, and economic viability, making them more attractive as renewable energy sources.

Real‐World Image Deraining Using Model‐Free Unsupervised Learning

We propose a novel model‐free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real‐world image deraining, dubbed MUL‐Derain. Beyond existing unsupervised deraining efforts, MUL‐Derain leverages a model‐free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL‐Derain can efficiently compute spatial coherence and global interactions by modeling long‐range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL‐Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real‐world datasets demonstrate that our MUL‐Derain obtains state‐of‐the‐art performance over un/semisupervised methods and exhibits competitive advantages over the fully‐supervised ones.

Optimal Single-Valued Neutrosophic Sine Trigonometric Aggregation Operators for Accurate Financial Fraud Detection Model

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability