Search Results - Control methods Deep Learning

Evaluating the effectiveness of the forest pests and diseases control methods on the industrial wood production using deep learning.

Authors: Sevinç V; Department of Statistics, Faculty of Science, Muğla Sıtkı Koçman University, Kötekli Kampüsü, 48000, Muğla, Turkey. vsevinc@mu.edu.tr.

Source: Scientific reports [Sci Rep] 2025 Oct 06; Vol. 15 (1), pp. 34679. Date of Electronic Publication: 2025 Oct 06.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

MeSH Terms: Wood* , Deep Learning* , Forests* , Pest Control*/methods , Forestry*/methods , Plant Diseases*/prevention & control, Animals

Research on Robotics Multi-Modal Perception and Adaptive Control Methods based on Deep Learning

Authors: Shengzhong Li

Source: 2025 7th International Conference on Information Science, Electrical and Automation Engineering (ISEAE). :1007-1010

Novel deep learning-based indentification methods for accurate, orientation-aware visual detection with application to inspection and quality control

Authors: Yao, Kai

Contributors: University/Department: Universitat de les Illes Balears. Doctorat en Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Ortiz Rodríguez, Alberto, Bonnín Pascual, Francisco

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Visió per Computador

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/674834

Potential of Deep Learning based quality control methods for soil moisture time series in an operational data service

Authors: Wolfgang Korres Tunde Olarinoye Fay Boehmer et al.

Subject Terms: 13. Climate action, 15. Life on land, 6. Clean water

Intelligent joint cutting strategy and method for roadheader combining LSTM deep learning and fuzzy inference control

Authors: Pengjiang WANG Yang SHEN Kai ZONG et al.

Source: Meitan xuebao, Vol 49, Iss S2, Pp 1195-1207 (2024)

Subject Terms: QE1-996.5, Mining engineering. Metallurgy, roadheader, intelligent cutting, lstm neural network algorithm, TN1-997, fuzzy control, Geology, joint control

Access URL: https://doaj.org/article/e7caf5ad89fa4557a7438cf3fed181d3

A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm

Authors: Runjin YAO Shuaibing CHENG Qianqian ZHAO et al.

Source: Gaoyuan qixiang, Vol 43, Iss 6, Pp 1630-1638 (2024)

Subject Terms: Meteorology. Climatology, convolutional neural network, gate recurrent unit, quality control, variational modal decomposition, QC851-999, wind speed

Access URL: https://doaj.org/article/fb28215d5682443fb1dcf71bcfedd103

Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

Authors: Qing Ma Changhong Deng

Source: Journal of Modern Power Systems and Clean Energy. 12:719-729

Phase control method for generating orbital angular momentum beams using coherent beam combining based on deep learning

Authors: Haoyu Zhao Jing Zuo Feng Li et al.

Source: Optics Express. 33:4151

Improving Energy Efficiency in Climatic Test Chambers with Deep Learning and Absolute Humidity Methods

Authors: Bekiroğlu, Erdal Karaca, Hakan Bolu Abant İzzet Baysal University Institutional Repository

Contributors: Bekiroğlu, Erdal Karaca, Hakan Bolu Abant İzzet Baysal University Institutional Repository

Source: 2023 11th International Conference on Smart Grid (icSmartGrid). :01-06

Subject Terms: Climatic Controlled Room, Absolute Humidity Control, Deep Learning, Efficiency

File Description: application/pdf

Access URL: https://hdl.handle.net/20.500.12491/12215
https://doi.org/10.1109/icsmartgrid58556.2023.10171071

Effectiveness of the Forest Pests and Diseases Control Methods on the Amount of Industrial Wood Production: A Deep Learning Analysis

Authors: Volkan Sevinç

Subject Terms: 13. Climate action, 9. Industry and infrastructure, 15. Life on land, 12. Responsible consumption

Deep Learning Methods for Vehicle Localization and Control under Challenging Conditions

Additional Titles: Deep-Learning-Methoden für die Fahrzeuglokalisierung und -steuerung unter anspruchsvollen Bedingungen

Authors: Cremers, Daniel (Prof. Dr.) Cremers, Daniel (Prof. Dr.);Burgard, Wolfram (Prof. Dr.) Wenzel, Patrick M.

Index Terms: info:eu-repo/classification/ddc/000, Informatik, Wissen, Systeme, thesis, doc-type:doctoralThesis

URL: https://mediatum.ub.tum.de/1692819
https://mediatum.ub.tum.de/doc/1692819/document.pdf
http://nbn-resolving.de/urn:nbn:de:bvb:91-diss-20230725-1692819-1-9

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

Authors: Kim, Taehyo Shu, Hai Jia, Qiran et al.

Index Terms: Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2310.13349

Designing Intelligent, Data-Driven Motor Control Strategies for Electric Vehicles Using Advanced Deep Learning Architectures.

Authors: R., Vetrikani

Source: International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p2788-2793, 6p

Subject Terms: ELECTRIC vehicles, DEEP learning, ARTIFICIAL neural networks, ENERGY consumption, MOTOR ability, ADAPTIVE control systems, CONVOLUTIONAL neural networks, TORQUE control

Deep Learning‐Based Quality Control Using Subcellular RNA Spatial Distribution Patterns for Cell Segmentation in Spatial Transcriptomics Data.

Authors: Ding, Renpeng Celikay, Kerem Ni, Ming et al.

Source: Small Methods. Nov2025, p1. 15p. 8 Illustrations.

Subject Terms: *QUALITY control, *CELL segmentation, *ARTIFICIAL neural networks, *TRANSFORMER models, *DEEP learning, *RNA sequencing, *METADATA

Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review

Authors: Evans K. Wiafe Kelvin Betitame Billy G. Ram et al.

Source: Artificial Intelligence in Agriculture, Vol 15, Iss 4, Pp 622-641 (2025)

Subject Terms: Unmanned ground vehicles (UGVs), UGV-based weed control, Agriculture, Artificial intelligence (AI), Navigation, Weed detection

Access URL: https://doaj.org/article/278662e3f572439e937b9dfafb002ce3

Memory-based event-triggered prescribed-time performance control for underactuated ASV: An online deep learning method.

Authors: Lu, Wenlong Zhao, Ying Yu, Shuanghe et al.

Source: Ocean Engineering. Nov2025:Part 3, Vol. 340, pN.PAG-N.PAG. 1p.

Subject Terms: *DEEP learning, *REAL-time computing, *FEEDBACK control systems, *ADAPTIVE control systems, *BOATS & boating

Modeling timbre for neural singing synthesis: methods for data-efficient, reduced effort voice creation, and fast and stable inference

Authors: Blaauw, Merlijn

Contributors: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Gómez Gutiérrez, Emilia, Bonada, Jordi

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Singing synthesis, Deep learning, Autoregressive modeling, Fast inference, Reduced effort voice creation, Síntesis de canto, Aprendizaje profundo, Modelado autorregresivo, Inferencia rápida, Creación de voces con esfuerzo reducido, Síntesi de cant, Aprenentatge profund, Modelat autorregressiu, Inferència ràpida, Creació de veus amb esforç reduït

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/675120

Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.

Authors: Santos, Igor S. Tavares, Caio P. Klafke, Guilherme M. et al.

Source: Medical & Veterinary Entomology. Dec2023, Vol. 37 Issue 4, p665-674. 10p.

Subjects: Deep learning, Rhipicephalus, Embryology, Biological assay, Software validation, Artificial intelligence

Geographic Terms: Florianópolis (Santa Catarina, Brazil)

一种基于组合深度学习的复杂山地风电场 测风数据质量控制方法. (Chinese)

Alternate Title: A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm. (English)

Authors: 姚润进 程帅兵 赵乾乾 et al.

Source: Plateau Meteorology; Dec2024, Vol. 43 Issue 6, p1630-1638, 9p

Subject Terms: CONVOLUTIONAL neural networks, PARTICLE swarm optimization, QUALITY control, WIND power plants, WIND power, DEEP learning

Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review.

Authors: Wiafe, Evans K. Betitame, Kelvin Ram, Billy G. et al.

Source: Artificial Intelligence in Agriculture; Dec2025, Vol. 15 Issue 4, p622-641, 20p

Subject Terms: RECURRENT neural networks, WEED control, CONVOLUTIONAL neural networks, DETECTION algorithms, MACHINE learning, WEEDS