Showing 361 - 380 results of 2,295 for search '((\ (source OR sources) selection function\ ) OR (( resources OR resources) detection functions\ ))', query time: 0.38s Refine Results
  1. 361
  2. 362

    Multi task detection method for operating status of belt conveyor based on DR-YOLOM by Yongan LI, Tengjie CHEN, Hongwei WANG, Zhihao ZHANG

    Published 2025-06-01
    “…A single network is used to simultaneously recognize large-sized coal blocks, detect belt edges, and detect coal flow status. Compared with using a separate model for each task, integrating three different necks and heads into a model with a shared backbone can save a lot of computing resources and inference time. …”
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  3. 363
  4. 364

    Optimizing Hyperspectral Desertification Monitoring Through Metaheuristic-Enhanced Wavelet Packet Noise Reduction and Feature Band Selection by Weichao Liu, Jiapeng Xiao, Rongyuan Liu, Yan Liu, Yunzhu Tao, Tian Zhang, Fuping Gan, Ping Zhou, Yuanbiao Dong, Qiang Zhou

    Published 2025-07-01
    “…Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. …”
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  5. 365

    A Hybrid and Modular Integration Concept for Anomaly Detection in Industrial Control Systems by Christian Goetz, Bernhard G. Humm

    Published 2025-04-01
    “…Therefore, in this paper, we present a modular and hybrid concept that enables the integration of efficient and effective anomaly detection while optimising the use of available resources under consideration of industrial requirements. …”
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  6. 366

    Venturing Into the Unknown: The Importance of Variable Selection When Modelling Alien Species Under Non‐Analogue Climatic Conditions by Tom Vorstenbosch, Franz Essl, Bernd Lenzner, Johannes Wessely, Stefan Dullinger

    Published 2024-10-01
    “…Here, we tested the assumption that this aspect of model design is a major source of uncertainty, especially when projections are made to non‐analogue climates. …”
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    Raw-Data Driven Functional Data Analysis with Multi-Adaptive Functional Neural Networks for Ergonomic Risk Classification Using Facial and Bio-Signal Time-Series Data by Suyeon Kim, Afrooz Shakeri, Seyed Shayan Darabi, Eunsik Kim, Kyongwon Kim

    Published 2025-07-01
    “…Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. …”
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  11. 371
  12. 372

    Phage display of cDNA libraries: enrichment of cDNA expression using open reading frame selection by Peggy Ho Faix, Michael A. Burg, Michelle Gonzales, Edward P. Ravey, Andrew Baird, David Larocca

    Published 2004-06-01
    “…The high level of cDNA expression obtained by ORF selection suggests that ORF-enriched phage cDNA libraries prepared by these methods will be useful as functional genomics tools for identifying natural ligands from various source tissues.…”
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  13. 373
  14. 374

    A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. by George Nicholson, Mattias Rantalainen, Jia V Li, Anthony D Maher, Daniel Malmodin, Kourosh R Ahmadi, Johan H Faber, Amy Barrett, Josine L Min, N William Rayner, Henrik Toft, Maria Krestyaninova, Juris Viksna, Sudeshna Guha Neogi, Marc-Emmanuel Dumas, Ugis Sarkans, MolPAGE Consortium, Peter Donnelly, Thomas Illig, Jerzy Adamski, Karsten Suhre, Maxine Allen, Krina T Zondervan, Tim D Spector, Jeremy K Nicholson, John C Lindon, Dorrit Baunsgaard, Elaine Holmes, Mark I McCarthy, Chris C Holmes

    Published 2011-09-01
    “…Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2) that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. …”
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  15. 375

    Metal and metal oxide nanomaterials for heavy metal remediation: novel approaches for selective, regenerative, and scalable water treatment by David B. Olawade, David B. Olawade, David B. Olawade, Ojima Z. Wada, Bamise I. Egbewole, Oluwaseun Fapohunda, Abimbola O. Ige, Sunday Oluwadamilola Usman, Olawale Ajisafe

    Published 2024-10-01
    “…The review identifies several promising nanomaterials, such as graphene oxide, carbon nanotubes, and metal-organic frameworks, which exhibit high surface areas, tunable surface chemistries, and excellent adsorption capacities. Surface functionalization with specific functional groups (e.g., carboxyl, amino, thiol) significantly enhances the selectivity for target heavy metal ions. …”
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  16. 376

    Real time wire rope detection method based on Rockchip RK3588 by Mengpeng Qian, Yong Wang, Shaoqing Liu, Zhanghou Xu, Zhenshan Ji, Ming Chen, Hailong Wu, Zuchao Zhang

    Published 2025-08-01
    “…To enhance non-destructive wire rope inspection, a Mini-YOLO model was developed by integrating MobileNetV3, the Coordinate Attention (CA) mechanism, and a novel loss function, Inner-IoU, into the YOLOv8 framework. This paper’s innovation lies not in creating algorithmic components from scratch, but in their synergistic integration and targeted optimization to solve the specific challenges of real-time defect detection on resource-constrained edge devices. …”
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  17. 377

    Object detection with afordable robustness for UAV aerial imagery: model and providing method by Viacheslav Moskalenko, Artem Korobov, Yuriy Moskalenko

    Published 2024-08-01
    “…The model and method for ensuring the robustness of resource-constrained neural network systems for object detection in aerial video surveillance. …”
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  18. 378

    An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

    Published 2025-03-01
    “…However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. To address the challenges of detecting small-target diseases on apple leaves in complex backgrounds and difficulty in mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high consumption of computational resources in real-time deployment of existing models, we integrate the Fasternet Block into the C2f of the backbone network and neck network, effectively reducing the parameter count and the computational load of the model. …”
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  19. 379

    SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

    Published 2024-12-01
    “…Furthermore, SGSNet has a computational cost of only 14.7 GFLOPs and a parameter count as low as 5.86 million, demonstrating an effective balance between high performance and resource efficiency.DiscussionLightweight deep learning model SGSNet not only exceeds the mainstream model in detection accuracy, but also greatly reduces the need for computing resources and is suitable for portable devices. …”
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  20. 380

    Detecting and routing of dust event using remote sensing and numerical modeling in Isfahan Province by Mehdii Jafari, Gholamreza Zehtabian, Hasan Ahmadi, Tayebeh Mesbahzadeh, Ali Akbar Norouzi

    Published 2020-03-01
    “…The purpose of this research was to analyze the statistical data and identify the days with dust, the source of dust entering the Isfahan area, and identify the route of its movement. …”
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