This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimizationalgorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). ...
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This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimizationalgorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimizationalgorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimizationalgorithm (FPID-WOA), and the fuzzy PID control strategy based on particleswarmoptimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the X-axis direction, respectively, 64.2% and 49.7% in the Y-axis direction, respectively, and 29.1% and 12.2% in the Z-axis direction, respectively.
Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have mac...
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Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particleswarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this
Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address ...
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Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particleswarmoptimization model is introduced to construct an energy-saving design optimization model. The fitness of the particleswarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other tec
A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automo...
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A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particleswarmoptimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system's mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system's state transition diagram. The Markov birth-death technique examines the system's performance and determines which subsystem is more critical and needs greater maintenance. The system's performance is enhanced through the utilization of the PSO and GA algorithms. The system's optimism parameters value is determined using these algorithms, varying both population size and generation size;these optimal parameters help the casting production industry to enhance system performance.
Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack en...
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Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack environment, resulting in poor performance and portability of intrusion detection algorithms. Therefore, an intrusion detection method based on PSO-GA hyperparameter optimized ResNet-BiGRU is proposed. The two-layer bidirectional gated recurrent unit (BiGRU) is connected to the fully connected layer of the residual neural network (ResNet). Firstly, ResNet is used to extract parallel local features, and BiGRU is used to extract long-distance-dependent features from the parallel local features, and the attention mechanism is added after the BIGRU to utilize correlation between the features to assign weights to the extracted features, so as to more comprehensively capture the important features of network intrusion and improve the detection performance. At the same time, the parameters of the basic particleswarmoptimization (PSO) are dynamically optimized and combined with the genetic algorithm (GA) to perform a mutation operation when the iterative process falls into a local optimal solution, adding a random perturbation to the current velocity and position of the particles, so that the particles are able to explore new regions in the space in order to jump out of the local optimal solution, and ultimately achieve automatic optimization of the hyperparameters of the ResNet-BiGRU model to achieve a model with better generalization performance. Finally, the proposed method is validated by using the variant NSL-KDD dataset, which achieves an accuracy of 98.46% and average precision, average recall and average False Alarm Rate(FAR) of 91.84%, 95.99% and 0.31%, and achieved high accuracy on three datasets KDD99, UNSW-NB15, and CIC-IDS 2017. The method is proved to have a strong intrusion detection capability by comparison experiments with other algorithms.
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.
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.
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