arXiv subjects

Tingting Su

Publications and source records attributed to Tingting Su.

6 recordsLinked to original sources

arXiv2025

Energy-Efficient Routing for Electric Vehicles under Acceleration and Load Effects

This paper proposes an Acceleration and Load-Dependent Electric Vehicle Routing Problem (ALD-EVRP), to optimize the energy consumption (EC) while capturing the effects of changing traffic conditions between peak and off-peak periods. We generalize the time-dependent speed model by replacing step functions with piecewise linear functions. The EC of each vehicle is influenced by its speed, acceleration, and real-time load. A mathematical model is developed and solved using BonMin, and a custom meta-heuristic algorithm is proposed for large-scale problems, yielding the same results as BonMin on small problems and performing better on larger ones. This is validated with real data from Singapore.

math.OC↗

arXiv2023

An Improved Scheduling with Advantage Actor-Critic for Storm Workloads

Various resources as the essential elements of data centers, and the completion time is vital to users. In terms of the persistence, the periodicity and the spatial-temporal dependence of stream workload, a new Storm scheduler with Advantage Actor-Critic is proposed to improve resource utilization for minimizing the completion time. A new weighted embedding with a Graph Neural Network is designed to depend on the features of a job comprehensively, which includes the dependence, the types and the positions of tasks in a job. An improved Advantage Actor-Critic integrating task chosen and executor assignment is proposed to schedule tasks to executors in order to better resource utilization. Then the status of tasks and executors are updated for the next scheduling. Compared to existing methods, experimental results show that the proposed Storm scheduler improves resource utilization. The completion time is reduced by almost 17\% on the TPC-H data set and reduced by almost 25\% on the Alibaba data set.

cs.DC↗

arXiv2023

The Morse Smale property for time-periodic scalar reaction-diffusion equation on the circle

\begin{abstract} We study the Morse-Smale property for the following scalar semilinear parabolic equation on the circle $S^1$, \begin{equation*} u_{t}=u_{xx}+f(t,u,u_{x}),\,\,t>0,\,x\in S^{1}=\mathbb{R}/2π\mathbb{Z}, \end{equation*} where $f$ is a $C^2$ function and $T$-periodic in $t$. Assume that the equation admits a compact global attractor $\mathcal{A}$ and let $P$ be the Poincaré map of this equation. We exclude homoclinic connection for hyperbolic fixed points of $P$ and prove that stable and unstable manifolds for any two heteroclinic hyperbolic fixed points of $P$ intersect transversely. Further, this equation admits the Morse-Smale property provided that all $ω$-limit sets (in the case $f(t,u,u_x)=f(t,u,-u_x)$, the $ω$-limit set is just a fixed point) of the corresponding Poincaré map are hyperbolic. \end{abstract}

math.DS↗

arXiv2021

Generative Steganography with Kerckhoffs' Principle

The distortion in steganography that usually comes from the modification or recoding on the cover image during the embedding process leaves the steganalyzer with possibility of discriminating. Faced with such a risk, we propose generative steganography with Kerckhoffs' principle (GSK) in this letter. In GSK, the secret messages are generated by a cover image using a generator rather than embedded into the cover, thus resulting in no modifications in the cover. To ensure the security, the generators are trained to meet Kerckhoffs' principle based on generative adversarial networks (GAN). Everything about the GSK system, except the extraction key, is public knowledge for the receivers. The secret messages can be outputted by the generator if and only if the extraction key and the cover image are both inputted. In the generator training procedures, there are two GANs, Message- GAN and Cover-GAN, designed to work jointly making the generated results under the control of the extraction key and the cover image. We provide experimental results on the training process and give an example of the working process by adopting a generator trained on MNIST, which demonstrate that GSK can use a cover image without any modification to generate messages, and without the extraction key or the cover image, only meaningless results would be obtained.

cs.MM↗

arXiv2020

A Reversible Data hiding Scheme in Encrypted Domain for Secret Image Sharing based on Chinese Remainder Theorem

Reversible data hiding in encrypted domain (RDH-ED) schemes based on symmetric or public key encryption are mainly applied to the security of end-to-end communication. Aimed at providing reliable technical supports for multi-party security scenarios, a separable RDH-ED scheme for secret image sharing based on Chinese remainder theorem (CRT) is presented. In the application of (t, n) secret image sharing, the image is first shared into n different shares of ciphertext. Only when not less than t shares obtained, can the original image be reconstructed. In our scheme, additional data could be embedded into the image shares. To realize data extraction from the image shares and the reconstructed image separably, two data hiding methods are proposed: one is homomorphic difference expansion in encrypted domain (HDE-ED) that supports data extraction from the reconstructed image by utilizing the addition homomorphism of CRT secret sharing; the other is difference expansion in image shares (DE-IS) that supports the data extraction from the marked shares before image reconstruction. Experimental results demonstrate that the proposed scheme could not only maintain the security and the threshold function of secret sharing system, but also obtain a better reversibility and efficiency compared with most existing RDH-ED algorithms. The maximum embedding rate of HDE-ED could reach 0.5000 bits per pixel and the average embedding rate of DE-IS is 0.0545 bits per bit of ciphertext.

cs.CR↗

arXiv2019

Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network

In order to identify and prevent tea leaf diseases effectively, convolution neural network (CNN) was used to realize the image recognition of tea disease leaves. Firstly, image segmentation and data enhancement are used to preprocess the images, and then these images were input into the network for training. Secondly, to reach a higher recognition accuracy of CNN, the learning rate and iteration numbers were adjusted frequently and the dropout was added properly in the case of over-fitting. Finally, the experimental results show that the recognition accuracy of CNN is 93.75%, while the accuracy of SVM and BP neural network is 89.36% and 87.69% respectively. Therefore, the recognition algorithm based on CNN is better in classification and can improve the recognition efficiency of tea leaf diseases effectively.

cs.CV↗