L
Liangzhi Li
Researcher at Osaka University
Publications - 41
Citations - 1162
Liangzhi Li is an academic researcher from Osaka University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 8, co-authored 23 publications receiving 580 citations. Previous affiliations of Liangzhi Li include Muroran Institute of Technology & South China University of Technology.
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Journal ArticleDOI
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
TL;DR: This paper proposes a deep learning based classification model, which can find the possible defective products in the manufacture inspection system with higher accuracy, and adapts the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency.
Journal ArticleDOI
Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles
TL;DR: A convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors, and a decision-making system calculates the specific commands to control the vehicles based on the abstractions.
Proceedings ArticleDOI
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
TL;DR: This work proposes IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image, to improve the performance of vessel segmentation.
Journal ArticleDOI
When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid
TL;DR: An IoTbased deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value and is able to quantitatively analyze the influences of some major factors.
Posted Content
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
TL;DR: IterNet as mentioned in this paper proposes IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image.