Projects and Course Designs

MCM/ICM 2018

Contest, Wuhan University, 2018

In MCM/ICM 2018, we were awarded as Meritorious Winner. In this contest, we chose the problem D, organizing a charging network for electric vehicles. During our preperation, we proposed a top-down model for network design optimization. The following is the summary of our paper.

Optimizing network of charging stations: A top-down approach

Keywords: Electric Vehicle; Station Planning; Top-down Approach; Network

We construct a top-down model on planning the charging station for electric vehicles with the target of developing an all-electrical vehicle traffic system gradually.

The problem contains three main points: the position, optimal amount, and the sequence of building them. We separate it into three levels: Country-level: determine the maximum number of charging stations; Network-level: determine the sequence of edge (road) selection; City-level: determine the accurate position of each charging station within city scale. On country-level, we develop Charging Stations Amount Estimation Sub-model (CSAE), which is under ideal assumptions to estimate the number of destination charging stations needed in each city and average density of supercharging stations needed on each road if everyone switched to all-electric. As for network-level, depending partly on the Connectivity-Freedom Degree (CFD) to evaluate the benefit to graph of an edge, Top-level Network Sequence Selection, Sub-model (TNSS) can effectively and efficiently find out the sequence of roads along which we build charging stations. Finally, on City-level, City-scale Charging Station Planning Sub-model (CCSP) is designed to plan accurate positions for every charging stations, given the maximum number of charging stations within a city. CCSP takes population density, traffic flow within a district and the capacity of a charging station into account. In this paper, models are introduced in a top-down order: CSAE, TNSS and CCSP.

To evaluate the efficacy of every model, we perform experiments on both synthetic and real data. Results show that our model both meets the requirement of this problem and accurately generates an optimal solution. Additionally, the model we proposed is compatible with further extension and has a high execution efficiency.

National College Student Information Security Contest

Contest, Wuhan University, 2017

In recent years, with the vigorous development of the Internet, social networks have become a necessity for every one of us. We often use instant messaging tools such as WeChat to make video calls and use platforms such as Weibo and Friendship Circle to record our lives. But as we enjoy the fun and convenience of social networks and instant messaging devices, our personal information is slowly leaking through these modern communication tools. Because of the openness of social networks and video communication platforms, when we use videos or photos containing personal portrait information on the network, we cannot ensure that this facial information is not abused, nor can we rely entirely on the Internet platform to protect our information.

At present, many face-based authentication systems still use 2D devices to identify users, which means that our photos and videos and other 2D portrait information can be easily used in these authentication systems. Taking Android mobile phone as an example, there are few devices using 3D depth camera to obtain relatively safe three-dimensional facial data. Most of them still use ordinary cameras for facial recognition, which facilitates the abuse of portrait information.

In order to solve this problem, we are committed to propose a solution to protect the face information flooded in social networks and video communications. The face processing of real-time video calls or photos greatly reduces the possibility of the image being verified by the identity authentication system directly and does not affect the view of the photos or videos themselves. In view of the rapid development of mobile devices and applications, our solution will use Android applications as a platform to provide a facial information protection tool for the vast number of social media users.

In order to achieve this product, we choose to add noise to the video or photo to make the direct 2D face recognition fail, which cannot be verified by the face recognition system, and reduce the impact of noise to the unaware of the naked eye (see figure 2.6.2). The basis of face recognition is neural network training, as long as the face part of the photo cannot be recognized by the general neural network. On the other hand, for the noisy images needed in image processing, we train them by deep learning. By building the training model of noise image neural network and using the existing universal face data set as data input, the available noise image is generated according to the corresponding algorithm.

Embedded Smart Monitoring System

Project, Wuhan University, 2017

This project aimed at designing a smart monitoring system, providing basic functions and some advanced functions. Detailed functions are as follows.

  • Video recording
  • Picture recording
  • Online live
  • Family facial recognition
  • Unlisted face alarming
  • Gesture control

For the facial recognition and gesture recognition, local recognition programs and FACE++ APIs were applied to provide more accurate recognition result due to the performance limit of our embedded system – Raspberry Pi.

Patent Information Analyzing Platform

Project, Wuhan University, 2017

At present, China vigorously encourages the public to start businesses and innovate the future. As an important achievement of innovation, patent technology in intellectual property rights has been paid more and more attention by enterprises and individuals. The “Creative Spring” patent map is a more intuitive map by collecting and integrating patented technology data. It provides prediction and evaluation services for development teams, avoids the risk of waste of resources caused by repeated research and development, and provides relevant legal suggestions, such as the guidance of the state in this field. At present, the development of patent map in China is lagging behind, basically blank. By providing forecasting and analysis services for enterprises and government departments, “Creative Spring” patent map can collect service fees from it, and constantly improve data sources, expand profits, and achieve the purpose of entrepreneurship.

Crowdsourcing Service Platform

Project, Wuhan University, 2017

This is a national-level college student innovative project and I am the leader in it. This project plans to develop and operate a trading platform for on-campus services (crowdsourcing) and second-hand goods. Currently in our university, we do not have enough platform to share and acquire sufficient service provided by others. We aim at designing a platform to balance these resources. After much investigation through offline and online survey of what functions we should provide, we separate designed the application as follows. There are two main parts. In part one, users can post their request or need on our platform. The other part is specifically designed for second-hand trading. We have already implemented the application and trying to make it into real use.