Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous information network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

A Survey of Heterogeneous Information Network Analysis

Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years. The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible. Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved. First, this review starts with a brief introduction to the history of CNN. Second, we provide an overview of CNN. Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection. Fifth, the applications of one-dimensional, two-dimensional, and multi-dimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is also adopted to guarantee both object classification accuracy and minimal loss. Experimental results are presented and show the effectiveness and efficiency of object classification strategies.

Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment

A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work. 

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Challenges and issues facing lithium metal for solid-state rechargeable batteries

A study on key technologies of unmanned driving

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

Effects of Human Adaptation and Trust on Shared Control for Driver-Automation Cooperative Driving

Intellectualisation is one of the three reforming technologies in automotive industry, which is now changing the mobility mode and human society. High safety and intelligence are the pre-requisites for putting self-driving vehicles into markets. This study presents the hardware and software architecture for intelligent vehicles, as well as their road verification in typical traffic scenarios. The hardware system includes environmental sensors, computing platforms, vehicle actuators, and vehicle platforms, which is able to provide redundant protection against the main controller failure. The software system includes environmental perception module, scene cognition module, decision and control module, human–computer interaction module and public service support module. To evaluate the performance of the developed architecture, the road tests of automated driving system were carried out in two typical traffic scenarios, including: (i) closed road test in Yuanboyuan region; (ii) open road test on Beijing-Tianjin highway. The real road test shows that the designed hardware and software systems for intelligent vehicles have desirable robustness, which can realise accurate and reliable environment perception, decision-making and motion control.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2018.5351

Deyi Li

Papers

Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment

A study on key technologies of unmanned driving

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

Effects of Human Adaptation and Trust on Shared Control for Driver-Automation Cooperative Driving

Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios