robust model
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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiang Li ◽  
Yang Ming ◽  
Hongguang Ma ◽  
Kaitao (Stella) Yu

PurposeTravel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.Design/methodology/approachThe authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.FindingsCase studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.Originality/valueThis study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.


2022 ◽  
Vol 3 ◽  
Author(s):  
Yi Chang ◽  
Xin Jing ◽  
Zhao Ren ◽  
Björn W. Schuller

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR).


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xuchen Deng

This paper studies the location-routing problem of emergency facilities with time window under demand uncertainty. We propose a robust mathematical model in which uncertain requirements are represented by two forms: the support set defined by cardinal constraint set. When the demand value of rescue point changes in a given definition set, the model can ensure the feasibility of each line. We propose a branch and price cutting algorithm, whose pricing problem is a robust resource-constrained shortest path problem. In addition, we take the Wenchuan Earthquake as an example to verify the practicability of the method. The robust model is simulated under different uncertainty levels and distributions and compared with the scheme obtained by the deterministic problem. The results show that the robust model can run successfully and maintain its robustness, and the robust model provides better protection against demand uncertainty. In addition, we find that cost is more sensitive to uncertainty level than protection level, and our proposed model also allows controlling the robustness level of the solution by adjusting the protection level. In all experiments, the cost of robustness is that the routing cost increases by an average of 13.87%.


2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Khalid K. Dandago ◽  
Ameer Mohammed ◽  
Osichinaka C. Ubadike ◽  
Mahmud S. Zango ◽  
Abdulbasit Hassan ◽  
...  

A robust model is essential for the design of system components such as controllers, observers state estimators, and simulators. State estimators are becoming increasingly important in modern systems, especially systems with states that may not be measured with sensors. Therefore, it is imperative to analyze the performance of different modelling and state estimator design techniques. In this research work, a parametric model of a pick and place robotic arm was obtained using system identification technique. Pick and place robotic arms have a lot of industrial applications. The parameters of the obtained model were determined using the general second-order characteristics equation and manual tuning. Furthermore, five state estimators were designed based on the developed model. The accuracy of the model, and the performance of the observers were analyzed. The model was found to provide a good representation of the system. Nonetheless, with very small divergence between the model and the real system. The performance of the observers was found to be dependent on their pole locations; the higher the magnitude of the poles, the higher the state estimators’ gain and the better the estimation provided. It was found out that the state estimators with high gains were more susceptible to measurement noise. Keywords— Modelling, pick and place robots, observers, and state estimators.


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