In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable an...
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In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particleswarmoptimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.
Transparent heat reflective windows (THRW) effectively manage solar energy by reflecting near-infrared (NIR) heat while maintaining high visible (VIS) light transmittance, thus improving building energy efficiency. Co...
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Transparent heat reflective windows (THRW) effectively manage solar energy by reflecting near-infrared (NIR) heat while maintaining high visible (VIS) light transmittance, thus improving building energy efficiency. Conventional THRW designs using dielectric/metal/dielectric (D/M/D) structures often increase metal thickness for better NIR reflectance, which compromises VIS transmittance. In this work, we present a dielectric/metal/ dielectric/metal/dielectric (D/M/D/M/D) multilayer structure, utilizing TiO2 as the dielectric and Ag as the metallic component. The film thicknesses were systematically optimized using the particle swarm optimization algorithm to achieve an optimal balance between high VIS transmittance ((TVIS = 87 %) and high NIR reflectance (RNIR = 95 %). Additionally, the structure shows only 15 % UV transmittance due to absorption by TiO2 and reflection by Ag. The multilayer films are fabricated using electron beam evaporator, with the experimental results show that the multilayers maintain good VIS transmittance as well as high NIR reflectance. This highperformance THRW design demonstrates significant potential for energy-saving applications in hot climates, offering a viable solution for enhancing building energy efficiency and occupant comfort in the future.
Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire *** order to achieve high-precision real-time measurement of DFFMC,...
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Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire *** order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particleswarmoptimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is *** near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data *** surface fine dead fuel of Mongolian oak(Quercus mongolica *** Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were *** used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion *** results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological *** spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.
Carbon price forecasting is important for policymakers and market participants. Due to the non -stationary and non -linearity of the carbon price, the commonly used methods adopt the ideology of 'decomposition and...
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Carbon price forecasting is important for policymakers and market participants. Due to the non -stationary and non -linearity of the carbon price, the commonly used methods adopt the ideology of 'decomposition and integration' to conduct multiscale forecasting. On this basis, multivariable forecasting discovers more informative knowledge with exogenous variables for carbon price forecasting, but it ignores that (i) the high -frequency and low -frequency components of the carbon price are mainly affected by different variables, and (ii) each variable contributes differently to each component forecasting. To address these challenges, we propose a multiscale and multivariable differentiated learning method for carbon price forecasting in this study. Specifically, different variables are introduced to forecast the high -frequency and low -frequency components, and a novel attentionweighted least squares support vector regression method is first proposed, in which the weight matrix of variables is constructed according to the idea of the attention mechanism. Furthermore, we analyze the contribution of each variable to the carbon price using Shapley additive explanations, thereby providing a reference for carbon market participants. We conduct experiments on the carbon price of the European Union Emissions Trading System and Hubei carbon market in China. Extensive results demonstrate that the proposed model achieves competitive and superior performance over the baseline and compared models.
With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during...
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With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.
As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and e...
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As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and economic benefits should be taken into account under the competitive condition led by various modes of transportation in order to get an optimal strategy in ticket fare pricing of urban rail transit on the premise of meeting the service quality standard. Here, the factors considered in the ticket fares fare pricing of urban rail transit in the domestic and foreign cities are summarized, after which the Logit model of the mode split within the public transit system is established. With considering both the respective benefits of the urban rail transit company and the travellers, a bi-level programming model is established together with the solution idea to the model with the particle swarm optimization algorithm. The example demonstrates the feasibility and effectiveness of the bi-level programming model and the related measures and the particleswarm ooptimization aalgorithm is fittable for the urban rail transit fare pricing. The suggestions proposed from the result of the example are helpful for the decision making of ticket fare pricing of urban rail transit.
Due to the complex operating environment of LED lamp beads (hereinafter referred to as LED), the test method that only considers the action of multiple stresses alone ignores the mutual influence between stresses, mak...
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Due to the complex operating environment of LED lamp beads (hereinafter referred to as LED), the test method that only considers the action of multiple stresses alone ignores the mutual influence between stresses, making it difficult to accurately obtain the life indicators of LEDs, resulting in a large deviation in the theoretical life results. In this regard, this paper proposes a multi-stress accelerated degradation evaluation method considering generalized coupling and conducts an accelerated degradation test (ADT) to evaluate the life of LEDs. We identified three stress sources and designed five new high-gradient ADTs. Through experimental data, we found that the three stress sources are strongly coupled on this LED. Then, a generalized coupling maximum likelihood estimation method (MLE) for the entire sample was constructed, and the particleswarmalgorithm was used to solve the parameters. Finally, the life of this LED was evaluated based on the experimental data. The results show that the life of the LED considering multiple-stress coupling is within 6.5% of the historical life scatter point, which is more in line with the actual working environment.
Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particular...
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Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particleswarmoptimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.
Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address ...
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Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address this, this study proposes a new extended conformable fractional-order grey prediction model, namely the ECFGM(1,1) model. By integrating an adaptive weighting coefficient into the conformable fractional-order accumulation process, the model can effectively prioritize new information, thereby enhancing its rationality and adaptability. Moreover, the adjusted process can be tailored to either emphasize new information or adhere to traditional accumulation methods, which improves its adaptability. To verify the effectiveness of the ECFGM(1,1) model, ECFGM(1,1) is applied to two examples from the literature. The model evaluation results show that the ECFGM(1,1) model has higher fitting accuracy and predictive accuracy than the GM(1,1), CFGM(1,1), and NIPGM(1,1) models. Using the constructed ECFGM(1,1) for predictive analysis of the per capita electricity consumption for daily life in China, the results show that this model can capture the laws of its changes over time. Finally, per capita electricity consumption for daily life in China from 2022 to 2026 is predicted. The results show that by 2026, such consumption is estimated to reach 1165.35 KWh.
With global climate warming, Antarctic ice sheet melting has garnered increasing attention, as changes in liquid water content (LWC) significantly affect sea level rise and regional climate. This study integrates SMOS...
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With global climate warming, Antarctic ice sheet melting has garnered increasing attention, as changes in liquid water content (LWC) significantly affect sea level rise and regional climate. This study integrates SMOS L-band passive microwave data with the LS-MEMLS microwave emission model and employs the particle swarm optimization algorithm to retrieve the surface LWC of the Antarctic ice sheet and its spatiotemporal variations. We analyzed LWC, surface density, and melt days across different Antarctic regions, focusing on the trends in LWC and its relationship with multi-source remote sensing products. The results indicate a rising trend in LWC and melting of the Antarctic Peninsula and coastal ice shelves from 2018 to 2020, with a notable peak in 2020, potentially related to the anomalous climatic events. This research provides new methodological and theoretical insights into Antarctic ice sheet dynamics melt and their implications for the global climate system.
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