A Data-Driven Optimization Method for Reallocating the Free-Floating Bikes

2019 ◽  
pp. 3-13
Author(s):  
Ming Liu ◽  
Xifen Xu
Keyword(s):  
Optimization Method ◽  
Data Driven

scholarly journals Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4642
Author(s):  
Li Dai ◽  
Dahai You ◽  
Xianggen Yin
Keyword(s):  
Robust Optimization ◽  
Dirichlet Process ◽  
Forecast Error ◽  
Optimization Methods ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Uncertainty Sets ◽  
Uncertainty Set ◽  
Simulation Results

Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

2020 ◽  
Vol 34 (14) ◽  
pp. 2059048
Author(s):  
Tong Gao ◽  
Wei Sheng ◽  
Mingliang Zhou ◽  
Bin Fang ◽  
Liping Zheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Optimization Method ◽  
Learning Rate ◽  
Data Driven ◽  
Adaptive Learning Rate ◽  
Improved Performance ◽  
Rate Optimization

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

scholarly journals The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 359 ◽  
Author(s):  
Yuxing Li ◽  
Xiao Chen ◽  
Jing Yu ◽  
Xiaohui Yang ◽  
Huijun Yang
Keyword(s):  
Feature Extraction ◽  
Optimization Method ◽  
Sample Entropy ◽  
Maximum Energy ◽  
Data Driven ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

scholarly journals Micro-Testing While Drilling for Rate of Penetration Optimization

2020 ◽  
Author(s):  
Magnus Nystad ◽  
Alexey Pavlov
Keyword(s):  
Noisy Data ◽  
Optimization Method ◽  
Extremum Seeking ◽  
Data Driven ◽  
High Fidelity ◽  
Drilling Process ◽  
Optimization Scheme ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. To avoid violation of constraints, the algorithm is adjusted with a combination of a predictive and a reactive approach. This method of constraint handling is demonstrated for a maximal limit imposed on the surface torque, but the method is generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested on a high-fidelity drilling simulator. The simulated scenarios show the method’s ability to steer the system to the optimum and to handle constraints and noisy data.

scholarly journals Data-Driven Models for Capacity Allocation of Inpatient Beds in a Chinese Public Hospital

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Peng Liao ◽  
Li Luo ◽  
Heng-Qing Ye
Keyword(s):  
Optimal Allocation ◽  
Limited Resource ◽  
Optimization Method ◽  
Capacity Allocation ◽  
Substantial Improvement ◽  
Public Hospitals ◽  
Data Driven ◽  
Random Capacity ◽  
Bed Capacity ◽  
Elective Patients

Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year’s data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.

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