Chenying Yi¹, Yangjun Zhou^1,2, Wei Zhang¹, Like Gao¹, Hongwen Wu³, Yuanchao Zhou^4,*, Ke Zhou¹, Weixiang Huang¹, Juntao Pan⁵, Shan Li¹, Bin Feng⁵

Energy Engineering, DOI:10.32604/ee.2025.073805

Abstract Ensuring reliability in distribution networks is essential under increasing operational and economic constraints. Traditional planning models rely on power flow calculations, leading to high computational costs and poor scalability. This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures, reliability parameters, and reliability indices, enabling fast and analytical reliability evaluation without power flow analysis. A bi-objective optimization model is developed to minimize both reliability indices (SAIDI) and investment costs, solved using Pareto-based multi-objective PSO combined with the TOPSIS method. Case studies on a 519-node distribution network demonstrate that the More >

Junmin Lyu¹, Feng Bao^2,*, Guangyu Xu³, Siyu Lu^4,*, Bo Yang⁵, Yuxin Liu⁵, Wenfeng Zheng⁵

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075178

Abstract Robust teleoperation in image-guided interventions faces critical challenges from latency, deformation, and the quasi-periodic nature of physiological motion. This paper presents a fully integrated, latency-aware visual servoing system leveraging stereo vision, hand–eye calibration, and learning-based prediction for motion-compensated teleoperation. The system combines a calibrated binocular camera setup, dual robotic arms, and a predictive control loop incorporating Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) models. Through experiments using both in vivo and phantom datasets, we quantitatively assess the prediction accuracy and motion-compensation performance of both models. Results show that TCNs deliver more stable and precise More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073533

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Shereen S. Jumaa¹, Mohsin H. Challoob², Amjad J. Humaidi^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074752

Abstract Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access. This paper proposes a new Tri-independent keying method for encrypting military images. The proposed encryption method is based on multilevel security stages of pixel-level scrambling, bit-level manipulation, and block-level shuffling operations. For having a vast key space, the input password is hashed by the Secure Hash Algorithm 256-bit (SHA-256) for generating independently deterministic keys used in the multilevel stages. A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive… More >

Wenhe Chen¹, Yue Wang¹, Shuonan Shen¹, Leer Hua¹, Caixia Zheng², Qi Pu^1,*, Xundiao Ma^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074682

Abstract In fire rescue scenarios, traditional manual operations are highly dangerous, as dense smoke, low visibility, extreme heat, and toxic gases not only hinder rescue efficiency but also endanger firefighters’ safety. Although intelligent rescue robots can enter hazardous environments in place of humans, smoke poses major challenges for human detection algorithms. These challenges include the attenuation of visible and infrared signals, complex thermal fields, and interference from background objects, all of which make it difficult to accurately identify trapped individuals. To address this problem, we propose VIF-YOLO, a visible–infrared fusion model for real-time human detection in… More >

Junnian Wang¹, Xiaoxia Wang¹, Zexin Luo¹, Qixiang Ouyang¹, Chao Zhou¹, Huanyu Wang^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074473

Abstract Internet of Things (IoTs) devices are bringing about a revolutionary change our society by enabling connectivity regardless of time and location. However, The extensive deployment of these devices also makes them attractive victims for the malicious actions of adversaries. Within the spectrum of existing threats, Side-Channel Attacks (SCAs) have established themselves as an effective way to compromise cryptographic implementations. These attacks exploit unintended, unintended physical leakage that occurs during the cryptographic execution of devices, bypassing the theoretical strength of the crypto design. In recent times, the advancement of deep learning has provided SCAs with a… More >

Yunhao Yu¹, Fuhua Luo¹, Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074277

Abstract This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control (LFC) systems integrated with Battery Energy Storage Systems (BESS). As renewable energy sources gain greater penetration, power grids are becoming increasingly vulnerable to cyber threats, potentially leading to frequency instability and widespread disruptions. We model two significant attack vectors: load-altering attacks (LAAs) and false data injection attacks (FDIAs) that corrupt frequency measurements. These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models, incorporating generation rate constraints and nonlinear loads. A coordinated attack strategy is… More >

Ye-Chan Park¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn³, Jaesung Lee^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074141

Abstract Legal case classification involves the categorization of legal documents into predefined categories, which facilitates legal information retrieval and case management. However, real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains. This leads to biased model performance, in the form of high accuracy for overrepresented categories and underperformance for minority classes. To address this issue, in this study, we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms from the perspective of the legal domain. This approach enhances More >

Hyunki Lim^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074138

Abstract High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. Moreover, an optimization problem that fully considers all dependencies between features and labels is difficult to solve. In this study, we propose a novel regression-based multi-label feature selection method that integrates mutual information to better exploit the underlying data structure. By incorporating mutual information into the regression formulation, the model captures not only linear relationships but also complex non-linear dependencies. The proposed… More >

Jinzheng Yu¹, Yang Xu², Haozhen Li², Junqi Li³, Ligu Zhu¹, Hao Shen^1,*, Lei Shi^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073771

Abstract Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises. While large language models (LLMs) enable automated report generation, this specific domain lacks formal task definitions and corresponding benchmarks. To bridge this gap, we define the Automated Online Public Opinion Report Generation (OPOR-Gen) task and construct OPOR-Bench, an event-centric dataset with 463 crisis events across 108 countries (comprising 8.8 K news articles and 185 K tweets). To evaluate report quality, we propose OPOR-Eval, a novel agent-based framework that simulates human expert evaluation. Validation experiments show OPOR-Eval achieves a More >