Data Learning-based Frequency Risk Assessment in a High-penetrated Renewable Power System by Jiaxin WEN, Siqi BU, Qiyu CHEN, Bowen ZHOU

“…Planning method based on Monte Carlo simulation (MCS) is inefficient, while artificial neural network (ANN) can make fast and effective prediction by learning data. Therefore, this paper proposed an MCS-ANN algorithm to realize the rapid assessment of violation risk of regional maximum frequency deviation. …”
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Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks by Jian-Dong Yao, Wen-Bin Hao, Zhi-Gao Meng, Bo Xie, Jian-Hua Chen, Jia-Qi Wei

“…Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. …”
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Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN by LIU Yuhui, MAI Wenjie, LI Xiaoyong, ZHAO Yinzhong, HE Xinzhong, HUANG Mingzhi

“…To accurately predict the SS_eff (effluent SS) content and COD_eff (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wastewater effluent quality to obtain the main water quality technical parameters,COD_inf (influent COD),Q (influent flow),pH (influent pH),SS_inf (influent SS),T (influent temperature),DO (influent dissolved oxygen),COD_eff,and SS_eff, for predicting the quality of wastewater from the wastewater treatment plant.Among them,the prediction results of PSOGA-WNN are compared with the neural networks of PSO-WNN,GA-WNN,and PSOGA-BP.The results show that the PSOGA-WNN neural network has the highest prediction accuracy,which indicates that the PSOGA hybrid parameter optimization algorithm based on the genetic algorithm and particle swarm algorithm has obvious superiority in optimizing the prediction accuracy of the model.The WNN neural network has certain advantages over BP neural network in terms of fitting degree as well as error accuracy and is an effective means of simulation prediction.…”
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Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework by Gregorius Airlangga, Ronald Sukwadi, Widodo Widjaja Basuki, Lai Ferry Sugianto, Oskar Ika Adi Nugroho, Yoel Kristian, Radyan Rahmananta

“…This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. …”
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Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach by Doo-Seop Choi, Taeguen Kim, Boojoong Kang, Eul Gyu Im

“…This paper focused on feature engineering instead of AI algorithm selections, presenting an approach that uniquely balances detection accuracy with computational efficiency through strategic dimensionality reduction. …”
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Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer by I-Chen Wu, Yen-Chun Chen, Riya Karmakar, Arvind Mukundan, Gahiga Gabriel, Chih-Chiang Wang, Hsiang-Chen Wang

“…<b>Results:</b> The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. …”
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Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Kepribadian MBTI Menggunakan Naive Bayes Classifier by Mutiara Persada Pulungan, Andi Purnomo, Aliyah Kurniasih

“…Machine Learning models with Naive Bayes Classifier algorithms are often used to predict MBTI personalities from Twitter data. …”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. …”
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Spatial-temporal attention for video-based assessment of intraoperative surgical skill by Bohua Wan, Michael Peven, Gregory Hager, Shameema Sikder, S. Swaroop Vedula

“…The objective in this research is to develop and validate algorithms for video-based assessment of intraoperative surgical skill. …”
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Investigating Internet of Things and Artificial Intelligence-Based Approaches to Smart Irrigation System Optimisation for Agricultural Water Resource Management by Balassem Zaid Ajzan, Alkhafaij Mahdi Abdulkhudur, Raju Nadimpalli Venkata Ganapathi

“…This system leverages IoT sensors to monitor environmental variables and uses AI algorithms to analyze this data and make real-time irrigation decisions. …”
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Systematic Security Analysis of Sensors and Controls in PV Inverters: Threat Validation and Countermeasures by Fengchen Yang, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu

“…These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. …”
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Toward reliable fluorescence imaging: Optical prior-guided probabilistic reconstruction for structured illumination microscopy by Kun Lin, Junkang Dai, Huaian Chen, Yi Jin

“…Furthermore, by leveraging the strong cognition ability of GRL to precisely learn the hierarchical representations of the SIM imaging process, PG-SIM itself also achieves a significant uncertainty reduction compared to current methods, overcoming the constraints of unreliability on the practical application of SIM. …”
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Hybrid neural network method for damage localization in structural health monitoring by Fatahlla Moreh, Yusuf Hasan, Zarghaam Haider Rizvi, Sven Tomforde, Frank Wuttke

“…Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. …”
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Optimization Design Method of a 6-DOF Micromanipulation Mechanism for Extreme Ultraviolet Projection Lithography Objective Lens by Shiyu Li, Junbo Liu, Ji Zhou, Haifeng Sun, Chuan Jin, Xia Kang, Song Hu

“…The results demonstrate a 15.1% reduction in maximum driving force, significantly enhancing outrigger driving smoothness.…”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Hybrid Electric Propulsion Design and Analysis Based on Regional Aircraft Mission by Wenjuan Shan, Shengze Bao, Shixuan Lin, Le Kang

“…Hybrid propulsion systems have become a focal point of low-carbon aviation research due to their advantages in energy savings, emissions reduction, and noise abatement. This study develops an integrated design methodology for hybrid propulsion systems for aircraft, incorporating multidisciplinary algorithms to establish an overall performance model. …”
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Performance of 2D Compressive Sensing on Wide-Beam Through-the-Wall Imaging by Edison Cristofani, Mathias Becquaert, Marijke Vandewal

“…Hardware simplicity, data, and measurement time reduction and simplified imagery are some of its most attractive strengths. …”
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Solving differential‐algebraic equations in power system dynamic analysis with quantum computing by Huynh T. T. Tran, Hieu T. Nguyen, Long T. Vu, Samuel T. Ojetola

“…This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. …”
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An adaptive processing model for large superalloy pipeline components by LUO Ziyan, QIN Xiansheng, WANG Zhanxi, LIU Weiwei, LI Shulu

“…Precision machining of superalloy curved surface components is an important way to achieve the weight reduction of the aerospace engine’s gas pipeline system. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…Evaluating the results of image denoising algorithms in Computed Tomography (CT) scans typically involves several key metrics to assess noise reduction while preserving essential details. …”
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