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  • Open Access

    ARTICLE

    Research on Crop Image Classification and Recognition Based on Improved HRNet

    Min Ji*, Shucheng Yang

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025

    Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… More >

  • Open Access

    ARTICLE

    An Improved Multi-Actor Hybrid Attention Critic Algorithm for Cooperative Navigation in Urban Low-Altitude Logistics Environments

    Chao Li1,3,#, Quanzhi Feng1,3,#, Caichang Ding2,*, Zhiwei Ye1,3

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025

    Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… More >

  • Open Access

    ARTICLE

    Deep Q-Learning Driven Protocol for Enhanced Border Surveillance with Extended Wireless Sensor Network Lifespan

    Nimisha Rajput1,#, Amit Kumar1, Raghavendra Pal1,#, Nishu Gupta2,*, Mikko Uitto2, Jukka Mäkelä2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3839-3859, 2025, DOI:10.32604/cmes.2025.065903 - 30 June 2025

    Abstract Wireless Sensor Networks (WSNs) play a critical role in automated border surveillance systems, where continuous monitoring is essential. However, limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time. To address this issue, this paper presents an innovative energy-efficient protocol based on deep Q-learning (DQN), specifically developed to prolong the operational lifespan of WSNs used in border surveillance. By harnessing the adaptive power of DQN, the proposed protocol dynamically adjusts node activity and communication patterns. This approach ensures optimal energy usage while maintaining high coverage, connectivity, and data accuracy. More >

  • Open Access

    ARTICLE

    An Advantage Actor-Critic Approach for Energy-Conscious Scheduling in Flexible Job Shops

    Saurabh Sanjay Singh*, Rahul Joshi, Deepak Gupta

    Journal on Artificial Intelligence, Vol.7, pp. 177-203, 2025, DOI:10.32604/jai.2025.065078 - 30 June 2025

    Abstract This paper addresses the challenge of energy-conscious scheduling in modern manufacturing by formulating and solving the Energy-Conscious Flexible Job Shop Scheduling Problem. In this problem, each job has a fixed sequence of operations to be performed on parallel machines, and each operation can be assigned to any capable machine. The problem statement aims to schedule every job in a way that minimizes the total energy consumption of the job shop. The paper’s primary objective is to develop a reinforcement learning-based scheduling framework using the Advantage Actor-Critic algorithm to generate energy-efficient schedules that are computationally fast… More >

  • Open Access

    ARTICLE

    Sensitive Analysis on the Compressive and Flexural Strength of Carbon Nanotube-Reinforced Cement Composites Using Machine Learning

    Ahed Habib1,*, Mohamed Maalej2, Samir Dirar3, M. Talha Junaid2, Salah Altoubat2

    Structural Durability & Health Monitoring, Vol.19, No.4, pp. 789-817, 2025, DOI:10.32604/sdhm.2025.064882 - 30 June 2025

    Abstract Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties, particularly in compressive and flexural strength. Despite extensive research, the influence of various parameters on these properties remains inadequately understood, primarily due to the complex interactions within the composites. This study addresses this gap by employing machine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites. It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal trade-offs between predictive accuracy and computational complexity, which has not previously been explored… More >

  • Open Access

    ARTICLE

    Simulation of Restraint Device Degradation of Long-Span Suspension Bridge Based on Finite Element Model

    Qiaowei Ye1, Ying Peng2, Zihang Wang2, Chao Deng2, Xiang Xu2, Yuan Ren2,*

    Structural Durability & Health Monitoring, Vol.19, No.4, pp. 851-868, 2025, DOI:10.32604/sdhm.2025.060906 - 30 June 2025

    Abstract The girder end restraint devices such as bearings and dampers on long span suspension bridge will deteriorate over time. However, it is difficult to achieve the quantitative assessment of the performance of the restraint device through existing detection methods in actual inspections, making it difficult to obtain the impact of changes in the performance of the restraint device on the bridge structure. In this paper, a random vehicle load model is firstly established based on the WIM data of Jiangyin Bridge, and the displacement of girder end under the actual traffic flow is simulated by… More >

  • Open Access

    ARTICLE

    Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions

    Qiang Ma1,2,3,4,5, Zhuopei Wei1,2, Kai Yang1,2,*, Long Tian1,2, Zepeng Li1,2

    Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1011-1035, 2025, DOI:10.32604/sdhm.2025.060596 - 30 June 2025

    Abstract An intelligent diagnosis method based on self-adaptive Wasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction, which are commonly faced by rolling bearings and lead to low diagnostic accuracy. Initially, dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty (1D-2DWDCGAN) are constructed to augment the original dataset. A self-adaptive loss threshold control training strategy is introduced, and establishing a self-adaptive balancing mechanism for stable model training. Subsequently, a diagnostic model based on multidimensional feature fusion is designed,… More >

  • Open Access

    ARTICLE

    Experimental Investigation into the Impact of a Viscosity Reducer on the Crude Oil Recovery Rate in a Low-Permeability Reservoir

    Baoyu Chen1,2, Meina Li3, Jicheng Zhang1, Wenguo Ma1,*, Yueqi Wang1, Tianchen Pan1, Xuan Liu1

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1459-1471, 2025, DOI:10.32604/fdmp.2025.060255 - 30 June 2025

    Abstract The relative permeability of oil and water is a key factor in assessing the production performance of a reservoir. This study analyzed the impact of injecting a viscosity reducer solution into low-viscosity crude oil to enhance fluid flow within a low-permeability reservoir. At 72°C, the oil-water dispersion solution achieved a viscosity reduction rate (f) of 92.42%, formulated with a viscosity reducer agent concentration (CVR) of 0.1% and an oil-water ratio of 5:5. The interfacial tension between the viscosity reducer solution and the crude oil remained stable at approximately 1.0 mN/m across different concentrations, with the minimum More >

  • Open Access

    ARTICLE

    Enhanced Flow Boiling Heat Transfer of HFE-7100 in Open Microchannels Using Micro-Nano Composite Structures

    Liaofei Yin1,*, Kexin Zhang1, Tianjun Qin1, Wenhao Ma1, Yi Ding1, Yawei Xu2,*

    Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 751-764, 2025, DOI:10.32604/fhmt.2025.067385 - 30 June 2025

    Abstract Flow boiling in open microchannels offers highly efficient heat transfer performance and has attracted increasing attention in the fields of heat transfer and thermal management of electronic devices in recent years. However, the continuous rise in power density of electronic components imposes more stringent requirements on the heat transfer capability of microchannel flow boiling. HFE-7100, a dielectric coolant with favorable thermophysical properties, has become a focal point of research for enhancing flow boiling performance in open microchannels. The flow boiling heat transfer performance of HFE-7100 was investigated in this study by fabricating micro-nano composite structures… More >

  • Open Access

    ARTICLE

    Effect of Trapezoidal Obstacle Height and Arrangement Density on the Performance Enhancement of Tri-Serpentine PEMFCs

    Hongen Li, Hongjuan Ren*, Cong Li, Yecui Yan

    Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 921-941, 2025, DOI:10.32604/fhmt.2025.066512 - 30 June 2025

    Abstract The flow field architecture of the proton exchange membrane fuel cell (PEMFC) cathode critically determines its performance. To enhance PEMFC operation through structural optimization, trapezoidal obstacles were implemented in the cathode flow channels. The height dependence of these obstacles was systematically investigated, revealing that a 0.7 mm obstacle height enhanced mass transfer from channels to the gas diffusion layer (GDL) compared to conventional triple-serpentine designs. This configuration achieved a 12.08% increase in limiting current density alongside improved water management. Subsequent studies on obstacle distribution density identified 75% density as optimal, delivering maximum net power density More >

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