Search Results (69)

In recent years, Transformer-based methods have shown full potential in power load forecasting problems. However, their computational cost is high, while it is difficult to capture the global characteristics of the time series. When the forecasting time length is long, the overall shift of the forecasting trend often occurs. Therefore, this paper proposes a gray relation analysis–frequency-enhanced decomposition transformer (GRA-FEDformer) method for forecasting power loads in power systems. Firstly, considering the impact of different weather factors on power loads, the correlation between various factors and power loads was analyzed using the GRA method to screen out the high-correlation factors as model inputs. Secondly, a frequency decomposition method for long short-time-scale components was utilized. Its combination with the transformer-based model can give the deep learning model an ability to simultaneously capture the fluctuating behavior of the short time scale and the overall trend of changes in the long time scale in power loads. The experimental results show that the proposed method had better forecasting performance than the other methods for a one-year dataset in a region of Morocco. In particular, the advantages of the proposed method were more obvious in the forecasting task with a longer forecasting length. Full article

Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models—a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)—were employed to predict k, with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. Gray relational analysis (GRA) revealed that all input parameters (α, β, d_max, n) significantly influence k (R > 0.6). The interquartile range (IQR) method confirmed data suitability for modeling. Among the models, BPNN achieved the best performance (R² = 0.99, MAE = 1.5, RMSE = 2.9, U₉₅ = 0.4), effectively capturing the complex nonlinear relationship between gradation and permeability. GPR (R² = 0.92) was hindered by kernel selection and noise sensitivity, while RF (R² = 0.97) was limited by its discrete regression nature. Compared to a traditional empirical model (R² = 0.9031), BPNN improved prediction accuracy by 10.13%, demonstrating the advantage of data-driven methods for evaluating CGS permeability. Full article

Compressed natural gas (CNG) refueling stations operate under highly dynamic thermodynamic conditions, requiring accurate modeling and optimization to ensure efficient performance. In this study, a dynamic simulation model of a CNG station was developed using MATLAB-SIMULINK, including detailed subsystems for multi-stage compression, cascade storage, and vehicle tank filling. Real gas effects were incorporated to improve prediction accuracy of the pressure, temperature, and mass flow rate variations during fast filling. The model was validated against experimental data, showing good agreement in both pressure rise and flow rate evolution. A two-stage multi-objective optimization approach was applied using Taguchi experimental design and gray relational analysis (GRA). In the first stage, storage pressures were optimized to maximize the number of vehicles filled and gas mass delivered, while minimizing compressor-specific work. The second stage focused on optimizing the volume distribution among the low, medium, and high-pressure tanks. The combined optimization led to a 12.33% reduction in compressor-specific energy consumption with minimal change in refueling throughput. These results highlight the critical influence of pressure levels and volume ratios in cascade storage systems on station performance. The presented methodology provides a systematic framework for the analysis and optimization of transient operating conditions in CNG infrastructure. Full article

The predicted rapid accumulation of end-of-life lithium-ion batteries (LIBs) from electric vehicles (EVs) has raised environmental concerns due to the toxic nature of LIB materials. Consequently, researchers have developed reusing and recycling plans for end-of-life LIBs to extend their life spans, mitigate residual capacity loss, and reduce their environmental impact. As a result, many studies have recommended establishing a lifecycle framework for LIBs to identify and manage the potential options for reusing, recycling, remanufacturing, or disposal of second life LIBs. In response, the state of health (SOH) and state of safety (SOS) methods were introduced as key performance indicators (KPIs) to determine the batteries’ health and usability based on their capacity levels. Thus, both SOH and SOS methods are crucial for battery cell selection frameworks employed to designate batteries’ second-life applications. Various papers have analyzed and compared SOH methods, yet none have clearly quantified their differences, to determine the most effective method. Therefore, this study aims to create a framework for selecting the most effective SOH method for use in LIB frameworks by identifying and quantifying their main KPIs. The proposed framework will utilize scientometric analysis to identify the KPIs necessary for a gray relation analysis (GRA)-based multi-criteria decision matrix (MCDM) to select the appropriate SOH method. Full article

Pavement humidity warping is a critical factor limiting the application of PPRBAC on low-volume roads. A nonlinear wet-warping stress formula for PPRBAC slabs has been derived based on previous experimental results, and the finite element method was employed to develop a single-board model in order to verify the accuracy of the analytical solution. Subsequently, the finite difference method, in conjunction with the finite element method, was employed to investigate the calculation methodology for wet-warping stress in PPRBAC slabs during service. Finally, the Taguchi–GRA (gray relational analysis) method was selected to analyze the sensitivity of humidity warping factors affecting PPRBAC slabs. The findings indicate that compared to the traditional bending moment equivalent method, the wetness warping stress formula established in this study accounts for the nonlinearity of wetness warping stress and demonstrates higher accuracy. For PPRBAC pavements during the service period, assuming uniform initial humidity distribution along the height within the concrete does not align with practical observations. The calculated humidity warping stress and deformation using this assumption are 1.1 and 1.7 times those obtained from the comprehensive dry–wet calculation method. It is crucial to consider the wet stage’s impact on the dry stage in the calculations. The Taguchi–GRA method objectively determines the weight of factors affecting humidity warping in PPRBAC, with slab size, thickness, and flexural strength having the greatest influence. Full article

At present, more than 200 cities in the world have developed metro systems. Under the agglomeration effect of traffic nodes, rapid population agglomeration and land development and utilization have formed around metro stations in cities. However, there is still the problem of uncoordinated development in each station area along the metro, so it is urgent to build an evaluation method of the coupling and coordination relationship between people and land to study the laws of population activities, industrial agglomeration, traffic resources, and other aspects in the metro station area and analyze its rationality and matching. In this study, Chengdu, the central city in the west of China, is selected as an example, and Metro Line 1, which has the longest history and is the most mature development in the city, is taken as an example. Starting from the coupling and coordination relationship between the human activity demand and metro resource supply, the evaluation indicator system of the coupling and coordination relationship between people and land in the station area of Chengdu Metro Line 1 is constructed. By collecting multi-source data, the coupling coordination degree model (CCDM) is used to quantitatively evaluate the human–land coupling coordination relationship in the station area. Then, the gray relational analysis (GRA) combined with the spatial distribution characteristics are used to analyze the characteristics and influencing factors of the coupling and coordination relationship, and it is concluded that the station area of Chengdu Metro Line 1 presents a circular and multi-center coupling and horizontal coordination spatial structure. Among them, the degrees of the population concentration and activity intensity, the levels of economic and industrial development, the level of service support, and the degree of contact with surrounding areas have great influences on the coupling and coordination levels of the station area. Finally, some improvement strategies are put forward, such as optimizing the network layout, building multi-level centers, strengthening functional connections, and enhancing the development intensity. This study provides a new method for the study of the coordinated development of metro station areas and has practical significance for evaluating the construction and development statuses of metro station areas, guiding the planning of metro stations, and formulating regional development strategies of metro stations. Full article

This study aims to analyze the influence of cutting parameters (cutting speed, feed rate, and depth of cut) on cutting temperature, forces, and energy consumption during the dry turning of Ti6Al4V, providing an optimized machining strategy to improve efficiency and sustainability. Due to the challenges of machining this alloy, such as high temperatures and tool wear, response surface methodology (RSM) was used to develop second-degree polynomial models, and analysis of variance (ANOVA) identified the most influential factors. The results indicate that depth of cut has the highest impact on cutting temperature (42.59%), cutting forces (53.08%, 74.73%, and 48.87% in the respective force components), and power consumption (49.78%), while feed rate is the dominant factor in energy consumption (63.36%). Gray relational analysis (GRA) was applied to optimize machining conditions based on the developed models, allowing a wider selection of cutting parameters beyond the experimental values. These findings provide a valuable tool for the industry, offering manufacturers a data-driven approach to optimizing the machining of Ti6Al4V and reducing energy consumption and tool wear while improving process stability. The proposed methodology enhances sustainability and cost-efficiency in titanium alloy machining, particularly in the aeronautical sector. Full article

The field of complex product design evaluation can attract high ambiguity due to difficulties in establishing indicators and the subjectivity of expert evaluation scoring. Indeed, traditional Kansei Engineering (KE) relies on user requirements and feedback for design evaluation, which may not fully and effectively validate the design evaluation results, let alone determine whether they apply to complex products with more evaluation index systems. To overcome these drawbacks, this study proposes an evaluation method based on Hybrid Kansei Engineering (HKE) modeling for complex product design evaluation. HKE modeling consists of two parts, namely Forward Kansei Engineering (FKE) and Backward Kansei Engineering (BKE). In this study, four electric forklift designs are used as an example. The FKE system adopts the multi-attribute decision evaluation method; obtains the evaluation indexes of the forklift product imagery and then establishes the perceptual word collection; constructs the evaluation index system of the forklift via the Analytic Hierarchy Process (AHP); calculates the weights of the evaluation indexes of each level and their rankings; and calculates the final rankings of the four electric forklift design solutions by adopting the Fuzzy Comprehensive Evaluation (FCE) method. The BKE system adopts eye tracking (ET) to extract the attention time, visual attention hotspot, and other eye movement index data, and the Gray Relation Analysis (GRA) method was used to validate the model to derive the ranking, which verifies the effectiveness and scientific validity of the evaluation method. The results of this study show that the two-way evaluation of HKE modeling can effectively avoid subjective factors in product design, improve the scientific nature of the design, and guarantee the logical rigor of the perceptual design procedure. Full article

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, quality assessment, and anomaly detection. A key advancement is the transition from static to adaptive design of experiments (DoE), using real-time analytics for iterative refinement. This paper introduces an innovative adaptive DoE embedded in an expert system for grinding, combining data-driven and knowledge-based methodologies. The KSF Grinding Expert™ system recommends optimized grinding control variables, guided by a multi-objective optimization framework using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Gray Relational Analysis (GRA). The rule-based adaptive DoE iteratively refines recommendations through feedback and historical insights, reducing the number of trials by excluding suboptimal parameters. A case study validates the approach, demonstrating significant enhancements in process efficiency and precision. This knowledge-based adaptive strategy reduces experimental trials, adapts DoE according to different grinding processes, and can prevent critical defects such as surface cracks. In the case study, optimized results which are offered by the expert system and validated with over 90% accuracy are incorporated into the system’s knowledge base, enabling continuous improvement and reduced experimentation in future iterations. Full article

The chemical composition of asphalt binder is closely related to its macroscopic properties, and as an important road building material, its performance directly affects the service performance of asphalt binder pavement. Saturate, aromatic, resin, and asphaltene are the four most common chemical components of asphalt binders, collectively known as the SARA components. The SARA components are used to establish the corresponding relationship between the chemical composition and the macroscopic properties of asphalt binder, which is of great significance for further research on and development of high-performance asphalt pavement materials. This study used eight types of virgin asphalt binders as raw materials, labeled A–H. Firstly, the thin-layer chromatography–flame ionization detection (TLC-FID) method was used to test the SARA contents of the different asphalt binders. Then, the conventional, rheological, and low-temperature properties of the different binders were tested. Finally, gray relational analysis (GRA) and Pearson correlation analysis (PCA) were used to study the correlation between the asphalt binder’s SARA content and its macroscopic properties. The results indicate that the contents of asphaltenes and resins are crucial in determining the high-temperature performance of asphalt binder. By adjusting the ratio of these components, the high-temperature performance of asphalt binder can be optimized. An increase in the content of heavy components, particularly asphaltenes, negatively affects the low-temperature performance of asphalt binder. In contrast, a higher aromatic content enhances its low-temperature performance. Full article

The high-pressure common rail (HPCR) injection system, a key technology for enhancing diesel engine performance, plays a decisive role in ensuring fuel injection precision and combustion efficiency through rail pressure stability. This study establishes a coupled simulation model of an electronically controlled HPCR injection system and a diesel engine, using GT-Suite to systematically investigate the effects of fuel supply pressure, camshaft speed, high-pressure pump plunger parameters, and inlet and outlet valve characteristics on rail pressure fluctuations. Gray relational analysis quantifies the correlation between these factors and rail pressure variations. The results demonstrate that increasing camshaft speed, injection pulse width, plunger mass, plunger length, plunger spring preload, inlet valve spring preload, and outlet valve body mass reduces rail pressure fluctuations, while variations in fuel supply pressure, plunger spring stiffness, and valve spring stiffness have minimal impact. Notably, the influence of outlet valve spring preload, inlet valve spring stiffness, and inlet valve body mass on rail pressure is nonlinear, with optimal values observed. Gray relational analysis further identifies inlet valve spring preload as having the highest correlation with rail pressure fluctuations (0.815), followed by inlet valve spring stiffness (0.625), with outlet valve spring preload (0.551) and stiffness (0.527) showing relatively lower correlations. This study provides valuable insights for optimizing the HPCR injection system design and contributes to advancements in diesel engine technology. Full article

Under the combined influences of climate change and human activities, the western Jilin (WJ) Province, as a typical ecologically fragile area, has experienced ecological degradation and resource depletion. Therefore, it is urgently needed to assess its ecological carrying capacity (ECC) to provide scientific support for regional ecological protection and resource management. This study integrated the “Pressure-State-Response” (P-S-R) model with the “production, living, and ecological spaces” (PLES) conceptual model to construct a comprehensive evaluation indicator system for ECC. The indicator weights were calculated using a Bayesian BWM-CRITIC-CWDF linear combination method, and the spatial–temporal distribution of ECC was then assessed using an improved TOPSIS and gray relational analysis (GRA). This evaluation model overcomes the limitations of traditional methods in weight allocation, indicator correlation, and non-linear effects, providing a more accurate, reliable, and objective assessment of ECC. Furthermore, a bivariate spatial autocorrelation model was applied to reveal the interaction between the “coupling coordination degree (CCD) of PLES” and ECC. The results indicate that the ECC value was divided into a period of decline (2000–2010) and a period of growth (2010–2020); spatially, the ECC level transitioned from a high-west, low-east to a high-east, low-west pattern. This change was primarily driven by factors such as fertilizer usage, per capita GDP, and per capita output. The “CCD of PLES” and ECC indicated positive spatial correlation, primarily forming “high-high” and “high-low” clusters. This study provides a reliable evaluation index system and an evaluation model for evaluating ECC in WJ. The findings provide a theoretical foundation for the region’s sustainable development and offer valuable insights for ecological carrying capacity research. Full article

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales. Full article

Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations. Full article

For civil high-speed rotorcraft designed to operate at specific cruising altitudes, this study proposes nine structural design schemes for pressurized cabins. These schemes integrate commonly used materials and processing technologies in the aviation industry with advanced PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure) technology. An analysis of the structural composition reveals that frames constitute 8–19% of the total structural weight, while stringers and beams make up 15–50%, and skins account for 11–25%, with thicknesses ranging from 1.0 mm to 2.0 mm. The separating interface of the pressurized cabin contributes 4–29% of the total structural weight. The weight distribution of each component in the pressurized cabin structure varies significantly depending on the chosen materials and processing technologies. Utilizing the Analytic Hierarchy Process (AHP), along with Gray Relational Analysis (GRA) and Dempster–Shafer (D-S) evidence theory, this study compares the simulation results of the nine schemes across multiple dimensions. The findings indicate that the configuration combining 7075 aluminum alloy and T300 composite material has the greatest advantages in terms of the high structural reliability of the configuration, light weight, mature processing technology, and low production cost. This comprehensive evaluation method quantitatively analyzes the factors influencing the structural configuration design of the pressurized cabin for civil high-speed rotorcraft, offering a valuable reference for the design of similar structures in related fields. Full article