Can Chen

IP and optical network collaboration under hierarchical control models using deep reinforcement learning

Yongyue Wei, Yang Li, Can Chen, Qiang Fu et autres

This paper studies the application of deep reinforcement learning in hierarchical control models, especially its advantages in the collaborative control of IP and optical networks. By combining deep reinforcement learning with hierarchical control models, an innovative network resource scheduling and allocation method …

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Design of Energy Capture of Heliostatic Field based on Geometric Method and Intelligent Optimization

Can Chen, Xinyu Deng, Ke Zou, Lei Huang et autres

In renewable energy, improving the design of heliostat mirrors, which help convert sunlight into electricity, is very important for making solar power systems work better. This study seeks to improve how heliostat fields are designed using better optimization methods to increase the …

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A Hub-Based Self-Organizing Algorithm for Feedforward Small-World Neural Network

Wenjing Li, Can Chen, Junfei Qiao

By integrating the small-world (SW) property into the design of feedforward neural networks, the network performance would be improved by well-documented evidence. To achieve the structural self-adaptation of the feedforward small-world neural networks (FSWNNs), a self-organizing FSWNN, namely SOFSWNN, is proposed based …

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Machine-learning-based bitter taste threshold prediction model for bitter substances: fusing molecular docking binding energy with molecular descriptor features

Can Chen, Haichao Deng, Huijie Wei, Yaqing Wang et autres

Abstract Establishing the bitterness threshold of molecules is vital for their application in healthy foods. Although numerous studies have utilized Mathematical algorithms to identify bitter chemicals, few models can accurately forecast the bitterness threshold. This study investigates the binding mode of bitter …

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Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation

Can Chen, Hao Liu, Zeming Liu, Xue Liu et autres

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Structure-aware protein self-supervised learning

Can Chen, Jingbo Zhou, Fan Wang, Xue Liu et autres

MOTIVATION: Protein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to …

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Decision Table-based Conformance Testing Research of AUTOSAR Operating System

Can Chen, Yang Xing Da, Ling Fang, Ke Wu

The Automotive Open Systems Architecture (AUTOSAR) operating system specification defines a series of abstract standard interfaces for automotive embedded operating systems and their related services. The traditional conformance test case design method suffers from high redundancy and poor reusability. This study designs …

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Grey-box Fuzzing With Deep Reinforcement Learning And Process Trace Back

Can Chen

Grey-box fuzzing, as a software testing technique, can find possible program bugs such as memory leaks, assertion failures and invalid input by generate random data then input it into a program, and monitor program exceptions. With program running, grey-box fuzzing collected the …

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