Zoned HVAC control via PDE-constrained optimization

He, Rong; González, Humberto

doi:10.1109/acc.2016.7524977

Cited by 13 publications

(3 citation statements)

References 39 publications

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“…In the process of parameter setting, it is ensured that the parameters are meaningful and the assigned values are consistent with the research hypothesis. Therefore, the model was assigned numerical values [41][42][43][44].…”

Section: Numerical Simulation and Resultsmentioning

confidence: 99%

Stability Analysis of Low-Carbon Technology Innovation Cooperation under a Reward and Punishment Mechanism

Wang

He²,

Ren³

et al. 2022

Systems

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In this study, we constructed a tripartite evolutionary game model for a large-scale enterprise (A), a small-scale enterprise (B), and a government, based on finite rationality and information asymmetry. First, we investigated and analyzed the stakeholders of low-carbon technology innovation (LCTI) and constructed a tripartite evolutionary game model. Then, we analyzed the conditions for the stability of different equilibrium points. Finally, we carried out numerical simulations using MATLAB 2020b to analyze the evolutionary processes and patterns of the different parameters. We found that the government’s strategic choice is always to participate in LCTI, which is conducive to a healthy LCTI environment. Governments can participate in LCTI consortiums through policy-driven reward and punishment mechanisms. Our simulation demonstrated that the size of enterprises influences their choice of LCTI strategy, and government reward and punishment mechanisms influence both large and small firms. Large firms are more prepared to accept the risks of LCTI and maintain a reciprocal LCTI strategy even in a high-cost scenario, while small firms are more sensitive to the costs and benefits. Thus, government reward and punishment mechanisms should take full account of small-scale enterprises. We propose a sound reward and punishment mechanism for LCTI that limits the opportunistic behavior of enterprises. Therefore, this paper is a complement to theories such as innovation systems and provides new thinking for low-carbon technology innovation cooperation of enterprises. Meanwhile, the reward and punishment mechanism proposed in this paper has important practical value for the government.

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Section: Numerical Simulation and Resultsmentioning

confidence: 99%

Stability Analysis of Low-Carbon Technology Innovation Cooperation under a Reward and Punishment Mechanism

Wang

He²,

Ren³

et al. 2022

Systems

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“…The total quantity of goods on each transport route must not exceed the load per vehicle, therefore: 0 0 6) indicates that the total number of paths cannot exceed the total number of vehicles; Equation (7) indicates that a customer is served by only one truck; Equation (8) indicates that the starting point and starting point of each path are distribution centers.…”

Section: Related Variable Descriptionmentioning

confidence: 99%

Research on Cold Chain Logistics Distribution Routing Algorithm Model Based on Customer Satisfaction

Deng¹,

He²,

Li³

2019

dtcse

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In view of the characteristics of cold chain logistics different from ordinary products and the competition within the industry, the distribution of chilled and frozen foods requires the use of refrigerated vehicles, and the cost of transportation and distribution is high. In the study of cold chain logistics, in order to more fully reflect customer satisfaction, this paper establishes a multi-objective optimization model with the lowest distribution cost and the shortest time; then introduces the customer satisfaction index and establishes a multi-objective optimization model of customer satisfaction. Linear processing transforms multiple goals into single-objective optimization problems.

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“…Recently, the batch algorithms are being continuously improved. Some of them try to choose different base-classifiers, such as Decision Tree, Fuzzy Rule, K-nearest neighbor and so on [6][7][8]. Some of them try to choose different window error thresholds to improve the classification accuracy [9].…”

Section: Relevant Algorithmsmentioning

confidence: 99%

A fast learn++.NSE classification algorithm based on weighted moving average

Shen¹,

Zhu²,

Du³

et al. 2018

Filomat

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Current researches of incremental classification learning algorithms mainly focus on learning from data in a stationary environment. The incremental learning in a non-stationary environment (NSE), where the underlying data probability distribution changes over time, however, has received much less attentions despite the abundant real applications have generated the long-term and cumulative big data in NSE. Thus, the incremental learning in NSE has gradually received extensive attentions. Nevertheless, the popular incremental classification learning algorithms currently for NSE such as SEA and DWM generally place strict restrictions on the changes. These algorithms can only deal with gradual drift and noncyclical and no new category situations. Therefore, it is highly necessary to develop a novel efficient incremental classification learning algorithm for the gradually cumulative big data in complex NSE. The recently proposed Learn++.NSE algorithm is an important research achievement in this field. However, the vote weight of each base-classifier of the Learn++.NSE depends on its whole error rates in the environments experienced. Therefore, the classification learning efficiency of the Learn++.NSE should be further improved. A novel fast Learn++.NSE algorithm based on weighted moving average (WMA-Learn++.NSE) is presented in this paper, which computes the weighted average of error rates using the sliding window technology to optimize the weight calculation. By only using the recent classification error rates of each base-classifier inside the sliding window to calculate the vote weight, the WMA-Learn++.NSE accelerates the compute of vote weight and improves the efficiency of classification learning. The verification experiments and performance analyses on both synthetic and real data set are presented in this paper. The experimental results show that the WMA-Learn++.NSE can achieve a higher execution efficiency compared to the Learn++.NSE in getting the equivalent classification correct rate.

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Zoned HVAC control via PDE-constrained optimization

Cited by 13 publications

References 39 publications

Stability Analysis of Low-Carbon Technology Innovation Cooperation under a Reward and Punishment Mechanism

Stability Analysis of Low-Carbon Technology Innovation Cooperation under a Reward and Punishment Mechanism

Research on Cold Chain Logistics Distribution Routing Algorithm Model Based on Customer Satisfaction

A fast learn++.NSE classification algorithm based on weighted moving average

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