Search Results (10,315)

This study investigates the integration of reinforcement learning (RL) with optimal control to enhance precision and energy efficiency in industrial robotic manipulation. A novel framework is proposed, combining Deep Deterministic Policy Gradient (DDPG) with a Linear Quadratic Regulator (LQR) controller, specifically applied to the ABB IRB120, a six-degree-of-freedom (6-DOF) industrial manipulator, for pick-and-place tasks in warehouse automation. The methodology employs an actor–critic RL architecture with a 27-dimensional state input and a 6-dimensional joint action output. The RL agent was trained using MATLAB’s Reinforcement Learning Toolbox and integrated with ABB’s RobotStudio simulation environment via TCP/IP communication. LQR controllers were incorporated to optimize joint-space trajectory tracking, minimizing energy consumption while ensuring precise control. The novelty of this research lies in its synergistic combination of RL and LQR control, addressing energy efficiency and precision simultaneously—an area that has seen limited exploration in industrial robotics. Experimental validation across 100 diverse scenarios confirmed the framework’s effectiveness, achieving a mean positioning accuracy of 2.14 mm (a 28% improvement over traditional methods), a 92.5% success rate in pick-and-place tasks, and a 22.7% reduction in energy consumption. The system demonstrated stable convergence after 458 episodes and maintained a mean joint angle error of 4.30°, validating its robustness and efficiency. These findings highlight the potential of RL for broader industrial applications. The demonstrated accuracy and success rate suggest its applicability to complex tasks such as electronic component assembly, multi-step manufacturing, delicate material handling, precision coordination, and quality inspection tasks like automated visual inspection, surface defect detection, and dimensional verification. Successful implementation in such contexts requires addressing challenges including task complexity, computational efficiency, and adaptability to process variability, alongside ensuring safety, reliability, and seamless system integration. This research builds upon existing advancements in warehouse automation, inverse kinematics, and energy-efficient robotics, contributing to the development of adaptive and sustainable control strategies for industrial manipulators in automated environments. Full article

Anthropogenic heat is the heat generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic heat are essential for studying the impacts of human activities on the climate and atmospheric environment. Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. Multi-source remote sensing data have also been widely used to obtain more details of the spatial and temporal distribution characteristics of anthropogenic heat. This paper reviews the main approaches for estimating anthropogenic heat emissions. The typical algorithms of the abovementioned three methods are introduced, and their advantages and limitations are also evaluated. Moreover, the recent progress in the application of remote sensing data and machine learning are discussed as well. Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. More in-depth research of this issue is recommended to provide important support for curbing global warming, mitigating air pollution, and achieving the national goals of carbon peak and a carbon neutrality strategy. Full article

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs. Full article

With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty. Full article

The main objective of this paper is to present a methodology for the reliable estimation of the energy consumption of electric vehicles, focusing on the main electrical subsystems of passenger cars. This paper presents a comparative analysis of the available regression models and the results of our simulation experiments. While numerous regression models have been documented in the literature, their accuracy is not always satisfactory. Consequently, there is a need to develop a sufficiently accurate and comprehensive generalized simulation framework, which is presented in the paper. Currently, most of the major vehicle manufacturers have developed pure electric vehicle platforms and are using them in the production of many models available on the market. The estimation of consumption data for these vehicles is still based on traditional techniques, namely, prediction from historical operation data. To overcome this problem, in this article, we have constructed a multi-element, model-based simulation for the purpose of implementing an energy consumption monitoring system. In order to create a simulation that reflects real-life vehicle behavior, the input data are based on empirical measurements, while the simulation model is based on actual electric vehicle parameters. In the main simulation model, it is possible to simulate the energy consumption of the vehicle’s drive system and to extract the requisite input data for the simulation of the other vehicle subsystems. In regard to the simulation, the subsystems that have been incorporated are the electric vehicle steering system, the vehicle lighting system and the HVAC system. After running the simulation, the total system consumption for a given trip segment is obtained by running each vehicle subsystem simulation. The findings were validated with real data and compared with two relevant regression models. Our preliminary expectation is that, given the level of detail of our simulation, the developed model can be considered validated if the error of the estimate remains below 4% and if the simulation model in question yields superior results in comparison to other regression models. Full article

Background/Objectives: As an endurance multi-sport race, triathlon places significant energy demands on athletes during performance and training. Insufficient energy intake from food can lead to low energy availability (LEA) and Relative Energy Deficiency in Sport (RED-S). We aimed to measure symptoms related to LEA, examine the risk of RED-S, and find how diet relates to the risk of RED-S in highly trained female amateur triathletes. Methods: Our sample was 20 top-performing female triathletes competing in Quarter Ironman (IM), Half IM, IM, or Double IM triathlons for 5.5 ± 2.5 y who were during the preparatory phase of training (training load 11 ± 3.76 h/week, a single workout 84 ± 25 min). Triathletes completed 3-day food diaries, training diaries, and the Low Energy Availability in Females Questionnaire (LEAF-Q). Exercise energy expenditure was estimated using wrist-worn activity trackers. To examine dietary patterns related to the first signs of LEA, predating RED-S, we created two groups: the L-LEA group (LEAF-Q score 0–5, no symptoms related to LEA, low risk of RED-S, n = 10) and the H-LEA group (LEAF-Q ≥ 6, at least one LEA-related symptom, high risk of RED-S, n = 10). Results: The risk of RED-S was prevalent in 30% of female triathletes, and 50% showed at least one symptom related to LEA. Macronutrient intake was similar in all participants, but triathletes from the H-LEA group tended to eat more plant-sourced protein and fiber. They consumed less saturated fatty acids but ingested more significant amounts of n-6 polyunsaturated fatty acids (PUFAn6). Conclusions: We conclude that foods higher in plant proteins, fiber, and PUFAn6 might predispose female triathletes to LEA by reducing the diet’s energy density. Full article

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics. Full article

Polyurethane (PU) grouting materials are widely used in underground engineering rehabilitation, particularly in reinforcement and waterproofing engineering in deep-water environments. The long-term effect of complex underground environments can lead to nanochannel formation within PU, weakening its repair remediation effect. However, the permeation behavior and microscopic mechanisms of water molecules within PU nanochannels remain unclear. In this paper, a model combining PU nanochannels and water molecules was constructed, and the molecular dynamics simulations method was used to study the effects of water pressure and channel width on permeation behavior and microstructural changes. The results reveal a multi-stage, layered permeation process, with significant acceleration observed at water pressures above 3.08 MPa. Initially, water molecules accelerate but are then blocked by the energy barrier of PU nanochannels. After about 20 ps, water molecules overcome the potential barrier and enter the nanochannel, displaying a secondary acceleration effect, with the maximum permeation depth rises from 1.8 nm to 11.8 nm. As the channel width increases, the maximum permeation depth increases from 7.5 nm to 11.6 nm, with the rate of increase diminishing at larger widths. Moreover, higher water pressure and wider channels enhance the stratification effect. After permeation, a hydrophobic layer of approximately 0.5 nm thickness forms near the channel wall, with a density lower than that of the external water. The middle layer shows a density slightly higher than the external water, and the formation of hydrogen bonds between water molecules increases toward the channel center. Full article

Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats. Full article

The film superposition prediction model is a crucial tool in the preliminary design of the turbine outer ring, enabling the rapid estimation of adiabatic wall temperatures and significantly reducing computational costs. This study established the relationship between the mainstream temperature correction coefficient and the air bleed ratio based on energy conservation principles in the boundary layer during film injection. A superposition model grounded in mainstream temperature corrections was developed. The proposed model utilizes cooling efficiency characteristics based on the equivalent blowing ratio to accurately predict the cooling efficiency of multi-row hole layouts with varying hole spacings. Experiments on the adiabatic film cooling efficiency were conducted with four different hole configurations at various blowing ratios. The limitations of the traditional Sellers superposition method in predicting cooling efficiency distributions are discussed by comparing them with experimental data. The comparison reveals that the Sellers method accumulates prediction errors as the number of hole rows increases, leading to an overestimation of the cooling efficiency. Introducing a mainstream temperature correction factor effectively addressed this issue. The prediction accuracy of the improved model was higher at $M=0.3$, with relative deviations remaining within 10% across different test plates. As the blowing ratio increased, the prediction deviation gradually increased when the number of hole rows was less than 10. At a blowing ratio of 1.0, the deviation exceeded 20%. However, as the number of hole rows increased, the deviation remained within 10% under various blowing ratios. Compared to existing advanced models in the literature, the improved model demonstrated higher prediction accuracy under most conditions. For Case 1 in this study, the model predicted an average surface cooling efficiency deviation of 3.4% at a blowing ratio of 0.5. Similarly, for Case 14 in the literature, the deviation was 3.4% at a blowing ratio of 0.6. In contrast, the prediction deviations in the literature models were 16.1% and 8%, respectively. Furthermore, the introduction of the equivalent blowing ratio reduced the data requirements for calculating the single-hole-row cooling efficiency when performing cooling efficiency superposition predictions. Full article

A double-layer robust optimization method for capacity configuration of shared energy storage considering cluster leasing of wind farms in a market environment is proposed based on the autonomy and profitability of shared energy storage. The feasibility of the leasing model of shared energy storage in the current market environment in China is discussed, and a commercial operation model for shared energy storage to provide leasing services and participate in spot market transactions is proposed. A robust optimization model of a master-–slave game for the capacity configuration of shared energy storage is constructed, considering output uncertainties of wind-driven generators and spot prices at multiple time scales. The upper layer of the model aims to minimize the annual cost of shared energy storage and determines the leasing prices and capacity-planning schemes for each period of shared energy storage in the scenario of an interactive game of wind farm clusters. The lower level of the model aims to minimize the assessment cost of the wind farm cluster and updates the leasing capacity for each time period by utilizing the leasing prices and the leasing demand of the wind turbine output power in the worst scenario. By comparing and analyzing multiple scenarios, the master–slave-game-formed lease improves the shared-storage lease benefit by $1.46 million compared to the fixed tariff, and the multi-timescale uncertainty promotes the shared-storage cost-effectiveness to be reduced by 8.7%, while the configuration result is more robust, providing new ideas for optimizing the capacity configuration of shared energy storage in multiple application scenarios. Full article

Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and cloud computing to harness their collective strengths, facilitating optimised data processing across these tiers. Our approach optimises performance, reducing energy consumption, and lowers costs. We evaluate our architecture through a series of experiments conducted on a purpose-built testbed. The results demonstrate significant improvements, with speedups of up to 7.5 times and energy savings reaching 80%, underlining the effectiveness and practical benefits of our cooperative edge-fog-cloud model in supporting the dynamic computational needs of IoT ecosystems. We argue that a multi-tier (e.g., edge-fog-cloud) dynamic task offloading and management of heterogeneous devices will be key to flexible edge computing, and that the advantage of task relocation and offloading is not straightforward but depends on the configuration of devices and relative device capabilities. Full article

Composite materials play a crucial role in various industries, including aerospace, automotive, and shipbuilding. These materials differ from traditional metals due to their high specific strength and low weight, which reduce energy consumption in these industries. The damage behavior of such materials, especially when subjected to stress discontinuities such as central holes, differs significantly from materials without holes. This study examines this difference and predicts the damage behavior of carbon fiber composites with multiple holes using a progressive damage model through finite element analysis (FEM). Two holes were positioned along the central axis of symmetry in the longitudinal and transverse directions relative to the load. The presence of additional holes acts as a stress-relief factor, reducing stress by up to 17% when the holes are arranged in the longitudinal direction. A cohesive zone model with two parameters, including constant and linear shapes, was applied to develop a simple analytical model for calculating the nominal strength of multi-hole composite laminates, based on the unnotched plate properties of the material. The results closely match experimental findings. The data also provide design tables that can assist with material selection. Full article

The low-carbon city pilot (LCCP) policy represents a pioneering approach to fostering sustainable development. It offers a scientific framework to reconcile the relationship between economic growth, resource utilization, and environmental protection. This study measures urban ecological efficiency (UEE) through the non-radial directional distance function (NDDF) model using the panel data of 284 cities in China, from 2007 to 2021, and analyzes the impact of the LCCP policy on UEE, adopting a multi-period difference-in-differences (DID) model. The results of the baseline regression indicate that the pilot cities exhibit an average ecological efficiency that is approximately 3.0% higher than that observed in non-pilot cities, which pass both the parallel trend test and the robustness test. Mechanism analysis reveals that industrial upgrading and energy consumption reduction are the primary pathways through which the LCCP policy enhances UEE. In addition, the policy effects are particularly significant in improving UEE in non-resource-based cities, large cities, and cities in the eastern region. Finally, the spatial spillover effects demonstrated by the LCCP policy can effectively inform neighboring cities of strategies to enhance their UEE. The research findings provide invaluable insight and direction for China’s efforts in the development of low-carbon cities and ecological sustainability. Full article

In this study, we research the innovative application of multi-walled carbon nanotubes (MWCNTs) as corrosion inhibitors in Portland cement embedded steel. The physicochemical properties of the dispersion solutions were evaluated, varying the storage time, to analyze their effect on corrosion resistance. Using a dispersion energy of 440 J/g and a constant molarity of 10 mM, stable dispersions were achieved for up to 3 weeks. These dispersions were characterized using Raman spectroscopy, UV-Vis spectroscopy and Zeta potential spectroscopy to assess the stability and structural damage of the MWCNTs. These results show that the addition of MWCNTs not only reduces the porosity of the cement matrix, but also forms an effective barrier against chloride ion intrusion, protecting the reinforcing steel. This approach stands out for combining improved mechanical properties and significant corrosion resistance, representing a promising innovation in the development of more durable construction materials. Full article