Zoned HVAC control via PDE-constrained optimization

He, Rong; González, Humberto

doi:10.1109/acc.2016.7524977

Cited by 12 publications

(2 citation statements)

References 39 publications

(37 reference statements)

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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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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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“…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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Cooperative navigation control of a moving target with multiple nonholonomic wheeled mobile robots based on dynammic feedback

Zheng

Shi

et al. 2019

2019 Chinese Automation Congress (CAC)

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Zoned HVAC control via PDE-constrained optimization

Cited by 12 publications

References 39 publications

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

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

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

Cooperative navigation control of a moving target with multiple nonholonomic wheeled mobile robots based on dynammic feedback

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