Topic Editors

Department of Software and Communications Energy, Hongik University, Sejongro 2639, Republic of Korea
Prof. Dr. Hyunsik Ahn
Department of Robot System Engineering, TongMyong University, Busan, Republic of Korea
Division of Information and Communication Engineering, Kongju National University, Cheonan 331717, Republic of Korea

Emerging AI+X Technologies including Selected Papers from ICGHIT 2024

Abstract submission deadline
31 August 2024
Manuscript submission deadline
31 October 2024
Viewed by
2028

Topic Information

Dear Colleagues,

The 12th International Conference on Green and Human Information Technology (ICGHIT 2024) will be held on 23–25 January, 2024, in Hanoi, Vietnam (http://icghit.org/).

The 12th International Conference on Green and Human Information Technology is a unique global conference for researchers, industry professionals, and academics who are interested in the latest green and human information technologies. The main theme of ICGHIT this year is “Towards of Artificial Super Intelligence”. The latest Artificial Super Intelligence technologies are used by the general population and present us with major challenges and great opportunities at the same time.

Centering around the main theme, ICGHIT offers an exciting program: hands-on experience-based tutorial sessions and special sessions covering research issues and directions with theoretical and practical applications. The conference will also include plenary, special, and technical sessions, and workshops.

Highly qualified papers selected from ICGHIT 2024 will be included in this Topic. However, this Topic also welcomes submissions from general researchers that fit within the scope of the SI, as shown below.

In this Topic, original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

Emerging AI+X technologies
Deep Neural Network;
Artificial Super Intelligence;
AI-based Applications in any area (Robotics, HCI, Smart City, Autonomous Driving, etc.)

Green Information Technology
Green Technology and Energy Saving;
Green Computing and Green IT Convergence and Applications;
Energy-Harvest-based Communications and Networking;
Technologies for Network Sustainability.

Communications and Networks for IoT and 5/6G-based network
Communications and Networks for Massive IoT and 5G;
Optical and Visual Light Communication;
Distributed/De-centralized Networks;
M2M/IoT and Ubiquitous Computing;
NFV, SDN, ICN, Network Slicing;
AI- and ML-based Technologies.

Network Security
Block-Chain-based Networking and Applications;
Distributed PKI;
Applied Cryptography;
Security in Big Data and Cloud Computing;
Security for Future Internet Architecture (SDN, ICN, etc.).

Network SW/HW Design, Architecture and Applications
Architecture and Protocols;
Sustainable Networks;
Information-centric Networks;
Blockchain-based Secure Networks;
AI-based Self-evolving networks;
Sensor/RFID Circuit Design;
System on Chip (SoC);
IC System for Communication.

We look forward to receiving your contributions.

Prof. Dr. Byung-Seo Kim
Prof. Dr. Hyunsik Ahn
Dr. Kyu-Tae Lee
Topic Editors

Keywords

  • AI
  • networking
  • computing
  • electronics
  • deep learning
  • WSN
  • 6G

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Computers
computers
2.8 4.7 2012 17.7 Days CHF 1800 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Journal of Sensor and Actuator Networks
jsan
3.5 7.6 2012 20.4 Days CHF 2000 Submit
Technologies
technologies
3.6 5.5 2013 19.7 Days CHF 1600 Submit

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Published Papers (1 paper)

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18 pages, 3587 KiB  
Article
Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems
by Fu-Cheng Wang and Hsiao-Tzu Huang
Technologies 2024, 12(1), 6; https://doi.org/10.3390/technologies12010006 - 05 Jan 2024
Viewed by 1468
Abstract
This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of [...] Read more.
This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction. Full article
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