Computational Efficiency Research Articles

A market-based load regulation method for heterogeneous residential air-conditioning loads under cloud-edge collaboration

Residential air-conditioning load on the power demand side has gradually increased. The air-conditioning loads have thermal storage characteristics, and adjusting their temperature settings will not cause significant negative impacts to users, making them well-suited for demand response. In existing studies, load aggregators usually serve as demand response implementors, and reducing the energy consumption levels of air conditioning loads directly. However, this method often lacks a clear definition of the flexibility of individual air-conditioning units and fails to account for their variations. The absence of well-defined commodity characteristics for the flexible regulation potential complicates pricing, making it difficult to safeguard the interests of the load aggregator and each air-conditioning user. Furthermore, the diversity among residential air-conditioning loads introduces high-dimensional variables into demand response optimization, making it challenging to balance data security with computational efficiency. To address these challenges, this paper proposes a market-based load reduction method for heterogeneous residential air-conditioning loads using cloud-edge collaboration. First, the flexible adjustment capability of air-conditioning loads is defined as a tradable commodity. Then, a cloud-edge collaborative trading scheme is proposed to guarantee the benefits of the load aggregator and each air-conditioning user in a data-neutrality manner. Finally, a hybrid method based on encrypted clustering is developed to compute the optimal trading strategy, ensuring data security and computational efficiency. Simulation results based on data from the Austin area show that Pareto improvement is achieved compared to other load reduction schemes, and the proposed encrypted clustering method reduces computational complexity and data security risks compared to traditional analytical and iterative approaches.

Accelerating chiller sequencing using dynamic programming

Effective management of heating, ventilation, and air conditioning (HVAC) systems is crucial for enhancing building energy efficiency. Chillers, a significant component of HVAC systems, are responsible for more than 50 % of energy usage of the whole cooling plant, underscoring the importance of optimizing chiller sequencing. Although Model Predictive Control (MPC) has demonstrated efficacy in enhancing chiller performance, its widespread adoption is hindered by its high computational complexity. This study introduces a dynamic programming approach coupled with a 2-step optimization method to expedite chiller sequencing optimization while upholding MPC’s real-time control capabilities. The MPC method implemented in this study integrates a load forecasting model with an artificial neural network (ANN) based chiller model. Through simulation tests utilizing historical HVAC operational data from a commercial building located in Shanghai, the proposed chiller sequencing strategy achieves a 9.73 % decrease in energy consumption. Moreover, the dynamic programming approach, especially in conjunction with the 2-step optimization process, significantly reduces the MPC solution time at each control step from 165 min to 3.61 s, making it a viable solution for real-time MPC deployment. Additionally, the research delves into the influence of the chiller model’s complexity, the optimization strategy, and control horizon on computational efficiency. This research provides a feasible solution to implement chiller sequencing in real buildings to pick up the low-hanging fruits in an effective way.

Survey of AI-driven routing protocols in underwater acoustic networks for enhanced communication efficiency

The high-speed growth of undersea communication networks requires sophisticated routing protocols to deal with challenging underwater conditions including large latencies, limited bandwidths and varying topologies. In this paper, we examine the use of Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL) and fuzzy logic to optimize routing protocols for underwater networks. We provide a comprehensive survey of existing AI-based approaches, emphasizing their novelties and constraints underwater.To assess the efficiency of these AI-based routing protocols, we carry out extensive simulations across various underwater environments where metrics such as packet delivery ratio, energy consumption, end-to-end delay, and computational efficiency are focused on. The results reveal that AI-aided protocols excel over conventional methods particularly in situations involving complex environmental dynamics as well as resources limitation.However, there are practical implementation issues which must be solved before the real-world application of AI-based routing such as hardware constraints, concerns on energy usage , and scalability. This study provides valuable insights into the integration of AI technologies into underwater communication networks, paving the way for more reliable and efficient underwater operations. Our findings contribute to the growing body of knowledge in this field and offer a foundation for future advancements in underwater communication technologies.

High-traversability and efficient path optimization for deep-sea mining vehicles considering complex seabed environmental factors

In cobalt-rich crust regions, rapid and safe pathfinding is critical for the operation of Deep-Sea Mining Vehicles (DSMVs). However, existing pathfinding solutions rarely account for traversability predictions in complex mining environments and extensive map data often results in computational inefficiencies. This paper proposes a High-Traversability and Efficient Path Optimization (HTEPO) strategy designed to ensure safe navigation and rapid decision-making accounting for complex seabed environmental factors. Initially, we develop a traversability assessment model combining the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) method. This model assigns traversability scores to map out high-risk and impassable areas effectively. We then introduce a novel map model that integrates these scores with Voronoi diagrams to reduce search space and improve computing efficiency, ensuring safer route selections. Further, based on traversability index and map model, an enhanced A* algorithm equipped with adaptive heuristic weights is utilized to swiftly identify high-traversability paths for the DSMVs. Finally, three case studies with different seabed mining environments validate the excellent performance of the proposed method over other major existing methods in terms of path safety and computational efficiency.

A novel design approach for structures with inerter systems based on non-stationary seismic excitations

Existing research predominantly focuses on developing design methods for the structural configuration of inerter systems. However, the authors find that these optimization methods often overlook the non-stationary characteristics associated with seismic response. Additionally, traditional approximation algorithms yield significant errors in non-stationary processes, resulting in inaccurate predictions of the structural responses with inerter systems, which compromises the effectiveness of the optimization designs. This paper proposes a novel approach that combines the parameters of the inerter system with a modified Vanmarcke approximation to obtain unified calculation formulas for inerter parameters. The accuracy and computational efficiency in solving spectral moments are crucial for the feasibility of this method. The study presents an efficient calculation method to accurately determine non-stationary response spectral moments of structures with inerter systems. Using the complex modal method (CMM) and pseudo-excitation method (PEM), a unified solution for structural responses under seismic actions is obtained. The quadratic decomposition of the power spectral density function (QD-PSDF) is employed to derive analytical solutions for response spectral moments and non-geometric spectral characteristics (NGSC). Substituting the obtained moment values into the parameter calculation formulas allows effective determination of the inerter system parameters. Finally, Finite Element Method (FEM) modeling verifies the validity and general applicability of this approach. The results demonstrate that the proposed method accounts for non-stationary characteristics of structures with inerter systems under seismic excitations, enhancing seismic design reliability and structural safety. Moreover, the obtained inerter system parameters at each floor effectively achieve the target performance while providing a broader range of design options for enhanced flexibility in system configuration.

Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach

We propose a Bayesian hierarchical model to address the challenge of spatial misalignment in spatio-temporal data obtained from in situ and satellite sources. The model is fit using the INLA-SPDE approach, which provides efficient computation. Our methodology combines the different data sources in a “fusion” model via the construction of projection matrices in both spatial and temporal domains. Through simulation studies, we demonstrate that the fusion model has superior performance in prediction accuracy across space and time compared to standalone “in situ” and “satellite” models based on only in situ or satellite data, respectively. The fusion model also generally outperforms the standalone models in terms of parameter inference. Such a modeling approach is motivated by environmental problems, and our specific focus is on the analysis and prediction of harmful algae bloom (HAB) events, where the convention is to conduct separate analyses based on either in situ samples or satellite images. A real data analysis shows that the proposed model is a necessary step towards a unified characterization of bloom dynamics and identifying the key drivers of HAB events.

An improved high-precision polyhedron SBFEM with combinatorial interpolation strategies

Computational accuracy and solution efficiency are crucial indicators for evaluating the performance of finite element numerical algorithms, and the corresponding improvements in these areas are the motivation for the development of computational science. In this paper, a flexible and high-precision polyhedron algorithm is proposed in conjunction with SBFEM and the triangle/quadrilateral interpolation functions. The main content can be summarized as follows: The construction equations for a mixed-order polyhedron element are derived, meanwhile, a generalized procedural framework is designed for multi-performance applications, including automatic elements type identification, coupled solutions, and dynamic storage, and the integrated development is completed based on self-developed software GEODYNA. The correctness, convergence and practicability of the proposed method are demonstrated through several examples in different dimensions. The results show that the proposed method obtains results with an error of less than 3% compared to theoretical solutions and the fine numerical solutions, while significantly reducing computational costs; Besides, the proposed method overcomes the limitations of conventional methods on mesh shape and can be seamlessly integrated with octree algorithms, which offers a powerful technical means for the efficient and high-precision analyses of bending deformation and stress concentration problems. It is foreseeable that a good application potential in high-precision structural analysis would be revealed.

An iterative method for integrated hump sequencing, train makeup, and classification track assignment in railway shunting yard

In a railway shunting yard, the transformation of inbound trains into properly composed outbound trains is a complex task because it involves decisions of multiple operations processes. This study addresses the integrated optimization of hump sequencing, train makeup, and classification track assignment problem in a railway shunting yard. Several key practical yard operation constraints are considered, including train formulation constraints, hump sequencing constraints, and limitations of the maximum number and capacity of classification tracks. By introducing a new representation of block flow, the integrated problem, which adopts the extended single-stage strategy and the train-to-track policy, is formulated as a unified 0-1 integer linear programming model. The objective of the proposed model is to minimize the weighted-sum of the total dwell time of all railcars and the formulation deviation penalties of all outbound trains. Then, an iterative two-phase decomposition approach is developed to reduce the complexity of solving the integrated problem. The first phase aims to explore all feasible humping sequences using a Branch-and-Bound (B&B) algorithm. Each time a new humping sequence is generated in the first phase, the second phase containing a Branch-and-Price (B&P) algorithm is applied to solve the integrated train makeup and classification track assignment problem with the known humping sequence found in the first phase. In addition, greedy heuristics and lower bounding techniques are designed in both phases to improve computational efficiency. Comprehensive experiments are investigated based on a set of real-life instances. The results show that exact approaches provide optimal solutions, whereas heuristic approaches yield satisfactory solutions within a shorter computation time. Moreover, sensitivity analyses on the number of classification tracks and the effects of different deviation penalties are also performed to gain more managerial insights.

Designing brittle fracture-resistant structures:A tensile strain energy-minimized topology optimization

This research proposes a novel method for designing fracture-resistant structures. By analyzing the relationship between tensile strain energy and phase field brittle fracture, it has been found that minimizing tensile strain energy can delay fracture and enhance resistance. Capitalizing on this insight, a new topology optimization method is proposed. This method focuses on minimizing tensile strain energy to suppress the well-documented tension-dominated fracture behavior observed in phase field brittle fracture analysis. In contrast to traditional topology optimization methods based on von Mises stress, this method generates more robust structures under tension. Furthermore, the method can incorporate stress constraints to mitigate the potential for stress concentrations arising from geometric discontinuities. Numerical results demonstrate the effectiveness of the proposed method. Using phase-field modeling, the mechanical fracture properties of the optimized structures, including peak load, failure displacement, and absorbed elastic energy before fracture, are quantified. Furthermore, experimental tests are also conducted. Both numerical simulations and experimental results are consistently show that structures designed with minimized tensile strain energy exhibit superior fracture toughness. Furthermore, the method offers significant computational efficiency compared to conventional approaches due to its reliance solely on linear elasticity analysis within the optimization process.

Learning-based Control for Deployment and Retrieval of a Spinning Tethered Satellite Formation System

This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. Based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy.

Transformation Decoupling Strategy Based on Screw Theory for Deterministic Point Cloud Registration With Gravity Prior.

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging the screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, enhancing computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis, and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory, and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle, and we propose a global voting method for its estimation. The proposed method solves three consensus maximization sub-problems sequentially, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%.

Nonlinear subspace clustering by functional link neural networks

Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it involves a balance between effectiveness and computational cost. In this study, we employ a functional link neural network to transform data samples into a nonlinear domain. Subsequently, we acquire a self-representation matrix through a learning mechanism that builds upon the mapped samples. As the functional link neural network is a single-layer neural network, our proposed method achieves high computational efficiency while ensuring desirable clustering performance. By incorporating the local similarity regularization to enhance the grouping effect, our proposed method further improves the quality of the clustering results. We name our method as Functional Link Neural Network Subspace Clustering (FLNNSC). Furthermore, we propose a convex combination subspace clustering scheme that combines a linear subspace clustering method with the functional link neural network subspace clustering approach. This combination method is named as Convex Combination Subspace Clustering (CCSC), which allows for a dynamic balance between linear and nonlinear representations. Extensive experiments conducted on four widely used datasets, including Extended Yale B, USPS, COIL20, and ORL, demonstrate that both FLNNSC and CCSC outperform several state-of-art subspace clustering methods in terms of clustering accuracy. Our affinity graph experiments reveal that FLNNSC exhibits clear block diagonal structures. We provide recommendations for hyperparameters in FLNNSC by performing a parameter sensitivity analysis, and empirically verify the convergence of FLNNSC. Additionally, we show that FLNNSC has a lower computational cost compared to two high-performing methods.

Performance Analysis of Semi-refined Digital Forearm Modeling and Simplified Forearm Model in Electromagnetic Simulation

Objective: The objective of this study is to analyze the performance difference between simplified and digital models based on medical images. Methods: According to the characteristics of human anatomy, the finite element simulation software COMSOL Multiphysics 5.5 was employed to construct a simplified arm model using cylinders and a digital arm model based on Chinese digital human regarding electroacupuncture therapy as an example. A comparative analysis was then performed considering three aspects: mesh number, potential distribution, and resource consumption. Results: Through analysis, the digital arm model based on Chinese digital human requires significantly more mesh cells than the simplified arm model in mesh generation. Meanwhile, because the digital arm model based on the Chinese digital human fully expresses the nonuniformity of the tissue distribution in a real human body, its signal distribution in its interior is also relatively scattered, and the coupling potential slightly differs at the electrode vertex with the smallest change. In addition, the digital arm model has much higher resource consumption and computer hardware resource requirements compared with the simplified arm model. Conclusion: As a result, the digital model based on the Chinese digital human can more fully express the tissue distribution and electrical signal characteristics of a real human body. However, due to its high computational requirements, appropriate simplification can be selected to improve the computational efficiency of the model in practical applications.

Improved fracture toughness evaluation for fiber-reinforced asphalt concrete through double-parameter theory

Fracture toughness serves as a key parameter in the design and durability assessment of asphalt concrete. Quantifying the fracture toughness for fiber-reinforced asphalt concrete (FRAC), remains challenging due to the incorporation of fibers. This study proposes an improved method for evaluating the fracture toughness of FRAC, based on the double-parameter theory derived from the theoretical concepts of stress intensity factor K and energy release rate (double-K and double-G fracture models). Using Digital Image Correlation (DIC) technology, crack propagation during three-point bending beam tests on FRAC beams was measured, refining the calculation of critical crack length ac in the double-K fracture parameters. Additionally, a method is introduced to calculate the improved solution of double-G fracture parameters by monitoring crack propagation in real-time to determine the effective crack length a, and simultaneously computing the energy absorbed per unit crack extension, thereby constructing the J-R curve during stable crack growth phases. The fracture toughness of FRAC is evaluated by the improved method obtained double-G fracture parameters were used in Virtual Crack Closure Technique (VCCT) simulations to achieve virtual modeling of FRAC. The results demonstrate that the improved method provides accurate fracture performance indicators for FRAC. Moreover, the study establishes modified calculation formulas for (GICu) and (KICu) at unstable fracture toughness for FRAC at different temperatures, considering various fiber types and contents, thereby enhancing computational efficiency for practical applications. This research contributes to more accurate and effective methods for testing and evaluating the fracture performance of FRAC

A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.

Sensitivity and uncertainty analysis of neutron activation source terms based on statistical sampling method

The calculation of neutron activation source terms is an essential task in the radiation of nuclear reactors, and its sensitivity and uncertainty (S&U) analysis can help better identify and control the influencing factors and safety margin of activation source terms. In this paper, we studied the statistical sampling strategies for the S&U analysis of activation source terms, established the calculation process of sensitivity coefficient and uncertainty, and the S&U analysis module based on sampling methods was integrated for the proprietary developed activation computation program ABURN. Two sampling ways, Monte Carlo and Latin hypercube sampling treatments, were employed to figure out the cases of pure decay single chain, pure activation single chain, and complex reaction network. The sensitivity coefficients and uncertainty of nuclide inventory were evaluated by taking the uncertainty of decay constant or reaction cross-section into account. The simulation results indicate that after introducing the LHS sampling method, the activation calculation program ABURN can improve computational efficiency in sensitivity and uncertainty analysis. Subsequent results demonstrated that the sensitivity coefficients and uncertainty solutions based on the sampling methods in the ABURN program were in good agreement with the analytical solutions and the FISPACT program, which verified the accuracy of the approaches and program in this paper.

Data-driven approaches for generating probabilistic short-term renewable energy scenarios

Renewable energy sources (RES) are becoming increasingly prevalent in power systems, but their intermittent and unpredictable nature challenges deterministic optimal generation scheduling. Stochastic planning or operating methodologies offer superior performance compared to deterministic approaches, making renewable energy generation scenarios increasingly valuable inputs for multistage decision-making problems. In this paper, we introduce and compare three data-driven approaches for generating probabilistic renewable energy scenarios. Numerical results from both simulated and real-world datasets demonstrate the accuracy and computational efficiency of these methods. Our proposed approaches provide a powerful tool for creating precise and efficient probabilistic renewable energy scenarios, which can enhance optimal generation scheduling in power systems with high RES penetration.

Preference learning based on adaptive graph neural network for multi-criteria decision support

Intelligent decision-making assists decision-makers (DMs) in making choices through data analysis, model prediction, and automated processes. Central to this field are two key concepts: multi-criteria decision making (MCDM) and preference learning (PL). While MCDM and PL both aim to develop decision models that rank alternatives based on observed or revealed preferences, they diverge in focus. MCDM concentrates on the DMs' perspectives, whereas PL emphasizes model-driven approaches. This divergence presents significant challenges in integrating these methodologies, particularly in ensuring the integrated method remains scalable and interpretable amidst the complexity of decision scenarios. To bridge this gap, our study introduces the use of graph structures to frame decision problems and proposes a novel PL method employing graph neural network (GNN) for multi-criteria decision support. This method is anchored in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) technique and combines an adaptive GNN model with a weight determination model. The GNN model updates embeddings from the alternative's criteria and category features, utilizing an attention mechanism to adaptively assess their importance. Concurrently, the weight determination model contains a weight neural network module to set objective criterion weights and a game theory-based module for calculating combined criterion weights. The method not only inherits the interpretability and intuitive appeal of decision models but also leverages the computational efficiency and high accuracy of machine learning. In experiments conducted on benchmark datasets, our method exhibits significant performance improvements, especially in ranking-related evaluation metrics, outperforming the best baseline by 5.78%.