An Optimized Position Control via Reinforcement-Learning-Based Hybrid Structure Strategy by Nebiyeleul Daniel Amare, Sun Jick Yang, Young Ik Son

“…To address this limitation, this paper proposes a reinforcement learning (RL)-based hybrid control scheme that overcomes the trade-off in IMP controllers between achieving zero steady-state tracking error and a fast transient response. …”
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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces by Panos I. Philippopoulos, Kostas N. Koutrakis, Efstathios D. Tsafaras, Evangelia G. Papadopoulou, Dimitrios Sigalas, Nikolaos D. Tselikas, Stefanos Ougiaroglou, Costas Vassilakis

“…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 by Wu Song, Sheng Ren, Bin Hu

“…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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A Gesture Recognition Method Based on Deep Learning by DING Chi, LIN Jun, YOU Jun, YUAN Hao

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Problem Based Learning Methods in the Digital Era: Measuring the Impact on Students Learning Motivation and Social Character by Muhammad Fajrin Haikal, Saepul Anwar, Mohammad Rindu Fajar Islamy

“… 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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Development of a grape-harvesting robot using a multi-step detection method based on AI and a position-estimation algorithm by Weiguo Wang, Liangliang Yang, Noboru Noguchi

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A Bluetooth Indoor Positioning System Based on Deep Learning with RSSI and AoA by Yongjie Yang, Hao Yang, Fandi Meng

“…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 by Juil Jeon, Myungin Ji, Jungho Lee, Kyeong-Soo Han, Youngsu Cho

“…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 by Kulecki Bartłomiej, Belter Dominik

“…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 by Wenlin ZHANG, Xuepeng LIU, Tong NIU, Qi CHEN, Dan QU

“…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 by Wenlin ZHANG, Xuepeng LIU, Tong NIU, Qi CHEN, Dan QU

“…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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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…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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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer by Binjie Lu, Zongji Li, Xiaobing Zhang

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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

“…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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Protocol for a theory-based, mixed methods evaluation of Cynnau|Ignite: an active learning programme to foster positive research culture through leadership development at Cardiff University. by Charlotte Hennah, Maleeha Rizwan, Nicola Edwards, Hayley Beckett, Michele Convery, Heidi Dawson, Jonathan Morris, Mair Rigby, James Vilares, Karin Wahl-Jorgensen, Rebecca Williams, Sofia Gameiro

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Machine learning-based mathematical equations for dengue positivity detection using elementary laboratory parameters by Shirin Dasgupta, Shuvankar Das, Debarghya Chakraborty

“…Materials and Methods: As a replacement, two machine learning (ML)-based prediction models, specifically Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN), are utilized to predict dengue infection. …”
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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…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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Machine Learning‐Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo by Emre Soylemez, Sait Demir, Kasım Ozacar

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Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs by Inseo Park, Gwiseong Moon, Ji Young Hong, Jeongwon Heo, Hongseok Ko, Doohee Lee, Yoon Kim, Woo Jin Kim, Hyun-Soo Choi, Kyoung Min Moon

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Multimodal Contrastive Learning for Remote Sensing Image Feature Extraction Based on Relaxed Positive Samples by Zhenshi Zhang, Qiujun Li, Wenxuan Jing, Guangjun He, Lili Zhu, Shijuan Gao

“…To fully leverage the characteristics of remote sensing images, we propose a multimodal contrastive learning method for remote sensing image feature extraction, based on positive sample tripartite relaxation, where the model is relaxed in three aspects. …”
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