Collection Series on Applied System Innovation

Topic Information

Dear Colleagues,

The International Conference on Applied System Innovation 2025 (ICASI 2025) will be held in Tokyo, Japan, from 22 to 25 April 2025, and the International Conference on Innovation, Communication and Engineering 2025 (ICICE 2025) will be held in November 2025. The aforementioned conferences will provide a unified communication platform for a wide range of topics. As part of the Topic “Applied System Innovation”, not only will excellent papers presented at ICASI 2025 and ICICE 2025 be selected, but we will also welcome other submissions related to novel materials, electronics, electrical engineering, mechanical engineering, and biomedical engineering. We welcome studies from both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation through electrical/mechanical engineering includes IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanosciences and nanotechnology. These new technologies that embed intelligence in machine systems are an interdisciplinary area combining conventional mechanical technology and new information technology. The main goal of this Topic is to uncover new scientific knowledge related to the aforementioned areas. We invite investigators interested in applied system innovation to contribute original research articles to this Topic. Potential topics include, but are not limited to, the following:

• Intelligent electronic/electrical engineering, including novel materials, device fabrication, mechanics and design, and related applications
• Intelligent mechanical manufacturing systems
• Mathematical problems in mechanical system design
• Smart electromechanical system analysis and design
• Optical system design and optoelectronic engineering
• Sustainability, green technology, and biomedical technology
• Computer-aided methods for electrical/mechanical design procedures and manufacturing
• Artificial intelligence, computers, virtual reality, entertainment, and human–machine interactions
• The impact of Internet technology on mechanical system innovation and the IoT
• Machine diagnostics and reliability
• Information systems, computer networking, and the Internet

Prof. Dr. Sheng-Joue Young
Prof. Dr. Shoou-Jinn Chang
Dr. Stephen D. Prior
Prof. Dr. Liang-Wen Ji
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Applied System Innovation asi	3.7	9.9	2018	22 Days	CHF 1600	Submit
Micromachines micromachines	3.0	6.0	2010	16.8 Days	CHF 2100	Submit

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Published Papers (3 papers)

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All Applied Sciences Computers Electronics Sensors ASI Micromachines

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32 pages, 2264 KB  

Open AccessArticle

Hybrid Fuzzy–Rough MCDM Framework and Decision Support Application for Sustainable Evaluation of Virtualization Technologies

by Seren Başaran

Appl. Syst. Innov. 2026, 9(2), 34; https://doi.org/10.3390/asi9020034 - 30 Jan 2026

Viewed by 222

Abstract

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies [...] Read more.

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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23 pages, 2136 KB  

Open AccessArticle

Coarse-to-Fine Contrast Maximization for Energy-Efficient Motion Estimation in Edge-Deployed Event-Based SLAM

by Kyeongpil Min, Jongin Choi and Woojoo Lee

Micromachines 2026, 17(2), 176; https://doi.org/10.3390/mi17020176 - 28 Jan 2026

Viewed by 239

Abstract

Event-based vision sensors offer microsecond temporal resolution and low power consumption, making them attractive for edge robotics and simultaneous localization and mapping (SLAM). Contrast maximization (CMAX) is a widely used direct geometric framework for rotational ego-motion estimation that aligns events by warping them [...] Read more.

Event-based vision sensors offer microsecond temporal resolution and low power consumption, making them attractive for edge robotics and simultaneous localization and mapping (SLAM). Contrast maximization (CMAX) is a widely used direct geometric framework for rotational ego-motion estimation that aligns events by warping them and maximizing the spatial contrast of the resulting image of warped events (IWE). However, conventional CMAX is computationally inefficient because it repeatedly processes the full event set and a full-resolution IWE at every optimization iteration, including late-stage refinement, incurring both event-domain and image-domain costs. We propose coarse-to-fine contrast maximization (CCMAX), a computation-aware CMAX variant that aligns computational fidelity with the optimizer’s coarse-to-fine convergence behavior. CCMAX progressively increases IWE resolution across stages and applies coarse-grid event subsampling to remove spatially redundant events in early stages, while retaining a final full-resolution refinement. On standard event-camera benchmarks with IMU ground truth, CCMAX achieves accuracy comparable to a full-resolution baseline while reducing floating-point operations (FLOPs) by up to 42%. Energy measurements on a custom RISC-V–based edge SoC further show up to 87% lower energy consumption for the iterative CMAX pipeline. These results demonstrate an energy-efficient motion-estimation front-end suitable for real-time edge SLAM on resource- and power-constrained platforms. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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20 pages, 3417 KB  

Open AccessArticle

Autonomous Frequency–Voltage Regulation Strategy for Weak-Grid Renewable-Energy Stations Based on Hybrid Supercapacitors and Cascaded H-Bridge Converters

by Geng Niu, Yu Ji, Ming Wu, Nan Zheng, Yongmei Liu, Xiangwu Yan and Yibo Gan

Appl. Syst. Innov. 2026, 9(1), 23; https://doi.org/10.3390/asi9010023 - 21 Jan 2026

Viewed by 212

Abstract

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable [...] Read more.

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. Full article

(This article belongs to the Topic Collection Series on Applied System Innovation)

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