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A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies
Published 2025-07-01Get full text
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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces
Published 2025-04-01“…Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. …”
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Interpretable Learning Method Based on Causal Interactive Attention
Published 2025-01-01“…In addition, to dynamically adapt to changes in the learning scenarios, we designed a loss function to adaptively control the strength of attention. …”
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Problem Based Learning Methods in the Digital Era: Measuring the Impact on Students Learning Motivation and Social Character
Published 2025-05-01“… This study examines the impact of the Problem-Based Learning (PBL) method on students’ learning motivation and social character development within the context of Islamic Religious Education (PAI). …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning
Published 2025-07-01“…In recent years, near-infrared (NIR) spectroscopy combined with algorithmic models has emerged as a popular method for identifying the tobacco leaf position. …”
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A Bluetooth Indoor Positioning System Based on Deep Learning with RSSI and AoA
Published 2025-04-01“…Traditional received signal strength indicator (RSSI)-based and angle of arrival (AoA)-based positioning methods are highly susceptible to multipath effects, signal attenuation, and noise interference in complex indoor environments, which significantly degrade positioning accuracy. …”
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Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images
Published 2024-10-01“…In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. …”
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Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
Published 2025-09-01“…The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.…”
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Self-supervised speech representation learning based on positive sample comparison and masking reconstruction
Published 2022-07-01“…To solve the problem that existing contrastive prediction based self-supervised speech representation learning methods need to construct a large number of negative samples, and their performance depends on large training batches, requiring a lot of computing resources, a new speech representation learning method based on contrastive learning using only positive samples was proposed.Combined with reconstruction loss, the proposed method could obtain better representation with lower training cost.The proposed method was inspired by the idea of the SimSiam method in image self-supervised representation learning.Using the siamese network architecture, two random augmentations of the input speech signals were processed by the same encoder network, then a feed-forward network was applied on one side, and a stop-gradient operation was applied on the other side.The model was trained to maximize the similarity between two sides.During training processing, negative samples were not required, so small batch size could be used and training efficiency was improved.Experimental results show that the representation model obtained by the new method achieves or exceeds the performance of existing mainstream speech representation learning models in multiple downstream tasks.…”
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Self-supervised speech representation learning based on positive sample comparison and masking reconstruction
Published 2022-07-01“…To solve the problem that existing contrastive prediction based self-supervised speech representation learning methods need to construct a large number of negative samples, and their performance depends on large training batches, requiring a lot of computing resources, a new speech representation learning method based on contrastive learning using only positive samples was proposed.Combined with reconstruction loss, the proposed method could obtain better representation with lower training cost.The proposed method was inspired by the idea of the SimSiam method in image self-supervised representation learning.Using the siamese network architecture, two random augmentations of the input speech signals were processed by the same encoder network, then a feed-forward network was applied on one side, and a stop-gradient operation was applied on the other side.The model was trained to maximize the similarity between two sides.During training processing, negative samples were not required, so small batch size could be used and training efficiency was improved.Experimental results show that the representation model obtained by the new method achieves or exceeds the performance of existing mainstream speech representation learning models in multiple downstream tasks.…”
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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer
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Deep Learning-Based Multimode Fiber Distributed Temperature Sensing
Published 2025-04-01“…This non-contact, high-precision MMF-based temperature measurement method, driven by deep learning, is suitable for applications in hazardous environments.…”
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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models
Published 2025-03-01“…The article introduces a technology for improving the accuracy of predicting a person’s position and speed on a running platform using machine learning and computer vision methods. …”
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An attack detection method based on deep learning for internet of things
Published 2025-08-01“…To address these issues, this paper proposes an attack detection method based on deep learning for IoT. Firstly, a genetic algorithm is used for feature selection; secondly, a cost-sensitive function is employed to address the scarcity of attack traffic in IoT; and finally, a combination of Convolutional Neural Networks and Long Short Term Memory Network is utilized to extract spatiotemporal information from the network. …”
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Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control
Published 2025-04-01“…To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. …”
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