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.
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.
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.
To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particleswarmoptimization(APSO-BPNN)is ***,on...
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To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particleswarmoptimization(APSO-BPNN)is ***,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is ***,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN *** experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and *** presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.
In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this rese...
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In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.
The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle...
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The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particleswarmoptimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.
PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises ...
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PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing ***/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particleswarmoptimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service *** major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.
In this brief, a high-efficiency optimization design method is proposed for a two-stage Miller-compensated operational amplifier (TSMCOA). In the proposed method, the parameters and performance metrics of TSMCOA are s...
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In this brief, a high-efficiency optimization design method is proposed for a two-stage Miller-compensated operational amplifier (TSMCOA). In the proposed method, the parameters and performance metrics of TSMCOA are simulated by Cadence software. Then, the neural network (NN) models are utilized to describe the relationship between its parameters and performance metrics, which can greatly improve simulation efficiency. Based on the performance metrics of TSMCOA, a multi-objective function is established. Then, according to the NN models and multi-objective function, the parameters of TSMCOA are optimized by particle swarm optimization algorithm with linearly decreasing inertia weight (PSO-LDIW). The optimized area is 0.1371 mu m2 72.09 mu 20803
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