Jiahao Chen

Wuhan University of Technology

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Liwen Huang

Center for Disease Control

Yixiong He

Wuhan University of Technology

Xingya Zhao

Wuhan University of Technology

Ke Zhang

Hefei University

Guozhu Hao

Wuhan University of Technology

Xiao Liu

Changsha University of Science and Technology

Deqing Yu

China Ocean Shipping (China)

Jinlai Liu

Wuhan University of Technology

Yaojie Chen

Wenzhou Medical University

Ke Zhang

University of South China

Cooperative Institutions

Wuhan University of Technology

China Ocean Shipping (China)

Zhejiang Ocean University

Changsha University of Science and Technology

Huazhong Agricultural University

China University of Geosciences

Nanjing Xiaozhuang University

Chongqing Medical University

Children's Hospital of Chongqing Medical University

Beijing University of Technology

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Multi-Granularity Spatio-Temporal Correlation Networks for Stock Trend Prediction

IEEE Access (2024)

Jiahao Chen Liang Xie Wenjing Lin Yuchen Wu Haijiao Xu

In recent years, time series forecasting has been widely used in various fields, especially in financial markets. Stock trend forecasting has become one of the most common and complex challenges faced by investors and researchers. However, much of the current research relies primarily on single-granularity stock data for forecasting, with relatively few studies on multi-granularity data and fewer studies on spatial correlation of multi-granularity data. This inherent limitation restricts the comprehensive extraction of valuable information. To address this challenge, we propose the Multi-Granularity Deep Spatio-Temporal Correlation Framework (MDSTCF). Our approach combines the strengths of a multi-granularity residual learning, gated recurrent units, and graph attention networks to extract spatio-temporal information specific to each granularity. Subsequently, predictions at each granularity are generated through the prediction layer. Finally, a soft attention mechanism is employed to assign weights to the predictions at each granularity to obtain the final result. Comprehensive experiments conducted on tow stock datasets show that the proposed forecasting model improves the F1 score by about 7.88% and 11.2%, and the cumulative relative returns are close to 80% and 40%, respectively, compared to the previously studied time series forecasting models. The results clearly indicates that fusing multi-granularity information can significantly improve the performance of time series forecasting.

Granularity

10.1109/access.2024.3393774

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Citations (1)

A Novel Decision Support Methodology for Autonomous Collision Avoidance Based on Deduction of Manoeuvring Process

Journal of Marine Science and Engineering (2022)

Ke Zhang Liwen Huang Xiao Liu Jiahao Chen Xingya Zhao

In the last few years, autonomous ships have attracted increasing attention in the maritime industry. Autonomous ships with an autonomous collision avoidance capability are the development trend for future ships. In this study, a ship manoeuvring process deduction-based dynamic adaptive autonomous collision avoidance decision support method for autonomous ships is presented. Firstly, the dynamic motion parameters of the own ship relative to the target ship are calculated by using the dynamic mathematical model. Then the fuzzy set theory is adopted to construct collision risk models, which combine the spatial collision risk index (SCRI) and time collision risk index (TCRI) in different encountered situations. After that, the ship movement model and fuzzy adaptive PID method are used to derive the ships’ manoeuvre motion process. On this basis, the feasible avoidance range and the optimal steering angle for ship collision avoidance are calculated by deducting the manoeuvring process and the modified velocity obstacle (VO) method. Moreover, to address the issue of resuming sailing after the ship collision avoidance is completed, the Line of Sight (LOS) guidance system is adopted to resume normal navigation for the own ship in this study. Finally, the dynamic adaptive autonomous collision avoidance model is developed by combining the ship movement model, the fuzzy adaptive PID control model, the modified VO method and the resume-sailing model. The results of the simulation show that the proposed methodology can effectively avoid collisions between the own ship and the moving TSs for situations involving two or multiple ships, and the own ship can resume its original route after collision avoidance is completed. Additionally, it is also proved that this method can be applied to complex situations with various encountered ships, and it exhibits excellent adaptability and effectiveness when encountering multiple objects and complex situations.

Obstacle avoidance

10.3390/jmse10060765

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Citations (19)

Research on the Markowitz Model-based Stock Investment Approach

BCP Business & Management (2023)

Jiahao Chen

In the world of financial investments, risk is one of the hottest subjects, and diversifying your investment portfolio is one of the best ways to do so. When choosing investments, consumers always aim to reduce risk while maintaining a specific rate of return. The risk and return on a portfolio are discussed using mathematical and statistical techniques. It is made clear that there are two types of risk: systematic risk and unsystematic risk, the latter of which can be diversified through a portfolio. Since 2019, the Covid-19 epidemic in the United States has had variable degrees of impact on different businesses. Others have been stimulated while many industries have stagnated. This article examines the effects of the pandemic on the global healthcare industry while concentrating on the medical stock market. Using the investing database, each medical company's data was examined. The Markowitz model is used to study two time periods before and after the outbreak. According to specific real-world circumstances, this conclusion can assist investors in making the best investment choices for risky investment portfolios.

Investment

Stock (firearms)

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Investment portfolio

10.54691/bcpbm.v38i.4042

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The Analysis of Intelligent Functions Required for Inland Ships

Journal of Marine Science and Engineering (2024)

Guozhu Hao Wenhui Xiao Liwen Huang Jiahao Chen Ke Zhang

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

10.3390/jmse12050836

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Citations (3)

Research of Methods of Collision Warning and Avoidance Assistant Decision Making for the Ship in Typical Inland TSS Waters

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering (2024)

Jiahao Chen Liwen Huang Ke Zhang Guozhu Hao Xingya Zhao

10.1061/ajrua6.rueng-1160

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Citations (1)

Dynamic adaptive autonomous navigation decision-making method in traffic separation scheme waters: A case study for Chengshanjiao waters

Ocean Engineering (2023)

Deqing Yu Yixiong He Xingya Zhao Jiahao Chen Jinlai Liu