Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study by Beomjoon Park, Minhye Kim, Dawoon Jung, Jinwook Kim, Kyung-Ryoul Mun

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Crop loss identification at field parcel scale using satellite remote sensing and machine learning. by Santosh Hiremath, Samantha Wittke, Taru Palosuo, Jere Kaivosoja, Fulu Tao, Maximilian Proll, Eetu Puttonen, Pirjo Peltonen-Sainio, Pekka Marttinen, Hiroshi Mamitsuka

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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

“…In the 3D reconstruction of coal-rock combinations fractures, in response to the problem that traditional threshold segmentation methods cannot accurately determine the threshold size between coal and rock, resulting in poor fracture segmentation performance, a new VRA-UNet coal-rock combinations fracture identification model based on deep learning theory is proposed, providing an optimized solution for accurate identification of coal-rock combinations fractures. …”
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Identification of leaves of wild Ussurian Pear (Pyrus ussuriensis) based on YOLOv10n-MCS by Niman Li, Niman Li, Xingguang Dong, Yongqing Wu, Luming Tian, Ying Zhang, Hongliang Huo, Dan Qi, Jiayu Xu, Chao Liu, Zhiyan Chen, Yulu Mou

“…The precision, recall, and mAP50 are significant improved of 2.9%, 2.3%, and 1.5% respectively over the YOLOv10n model (p<0.05). Comparative experiments confirmed its advantages in precision, model complexity, model size, and other aspects.DiscussionThis lightweight model enables real-time wild Ussurian Pear identification in natural environments, providing technical support for germplasm conservation and crop variety identification.…”
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CommRad RF: A dataset of communication radio signals for detection, identification and classificationZenodo by Muhammad Usama Zahid, Muhammad Usman Akram, Muhammad Danish Nisar, Fahd Maqsood, Syed Usman Ali, Muhammad Montaha

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Fault Diagnosis Systems for Robots: Acoustic Sensing-Based Identification of Detached Components for Fault Localization by Woonghee Yeo, Mitsuharu Matsumoto

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Identification of Critical Track Sections in a Railway Station Using a Multiplex Networks Approach by Pengfei Gao, Wei Zheng, Jintao Liu, Daohua Wu

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Loader Bucket Working Angle Identification Method Based on YOLOv5s and EMA Attention Mechanism by Xuedong Zhang, Bo Cui, Zhaoxu Wang, Wangting Zeng

“…In response to the issues of low recognition efficiency and large errors encountered in the process of identifying the working angle of the bucket during current automated loader construction operations, a method based on YOLOv5s and the EMA attention mechanism for loader bucket working angle identification is proposed. …”
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Identification of sweetpotato virus disease-infected leaves from field images using deep learning by Ziyu Ding, Fanguo Zeng, Haifeng Li, Jianyu Zheng, Junzhi Chen, Biao Chen, Wenshan Zhong, Xuantian Li, Zhangying Wang, Lifei Huang, Xuejun Yue, Xuejun Yue

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Weak Labeling-specific Emitter Identification Algorithm Based on the Weakly Supervised Wav-KAN Network by Kangsheng LIU, Qing LING, Wenjun YAN, Limin ZHANG, Keyuan YU, Hengyan LIU

“…The weakly labeled dataset is then divided into a small labeled dataset and a large unlabeled dataset, with the small labeled dataset used for initial model training. Finally, based on the pretrained model, Adaptive Pseudo-Label Weighted Selection (APLWS) is used to extract features from the unlabeled data using a contrast learning method, followed by iterative training, thereby effectively improving the generalization capability of the model. …”
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Identification, Geographical Traceability, and Thermal Oxidation and Photodegradation Studies of Camellia Oil Based on Raman Spectroscopy by Boxue Chang, Jingyue Huang, Qingli Xie, Yinlan Ruan, Rukuan Liu

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A Weight-Generating Approach of a Deep Neural Network for the Parameter Identification of Dynamic Systems by Weimeng Chu, Shunan Wu, Fangzhou Fu, Zhe Ye, Zhigang Wu

“…Unlike the traditional learning process, a weight-generating approach to quickly generate the weight vectors of a deep neural network model is proposed, which can be used for parameter identification of a dynamic system. …”
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Enhancing rice disease and insect-pest detection through augmented deep learning with transfer learning techniques by Amit Bijlwan, Rajeev Ranjan, Shweta Pokhariyal, Ajit Govind, Manendra Singh, Krishna Pratap Singh, Raj Kumar Singh, Ravindra Kumar Singh Rajput, Rajeev Kumar Srivastava

“…This research provides a thorough evaluation of various deep learning (DL) models focused on the classification and identification of rice diseases, as well as rice insect pests. …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large-scale structure. …”
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Conveyor belt deviation identification algorithm based on anchor point positioning and cross-layer correction by Zhe WANG, Zhe FU, Pengjun CAO, Qing LI, Gaoxiang ZHANG

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A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders by Jorge Magalhães, Tomás Jorge, Rúben Silva, António Guedes, Diogo Ribeiro, Andreia Meixedo, Araliya Mosleh, Cecília Vale, Pedro Montenegro, Alexandre Cury

“…The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.…”
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Identification method of canned food for production line sorting robot based on improved PSO-SVM by GAO Haiyan, GAO Jinyang, WANG Weicheng

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Improving person re-identification based on two-stage training of convolutional neural networks and augmentation by S. A. Ihnatsyeva, R. P. Bohush

“…At the first stage, training is carried out on augmented data, at the second stage, fine tuning of the CNN is performed on the original images, which allows minimizing the losses and increasing model efficiency. The use of different data at different training stages does not allow the CNN to remember training examples, thereby preventing overfitting.Proposed method as expanding the training sample differs as it combines an image pixels cyclic shift, color  exclusion and fragment replacement with a reduced copy of another image. …”
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Identification of the Parameters of the Highly Saturated Permanent Magnet Synchronous Motor (PMSM): Selected Problems of Accuracy by Michal Gierczynski, Rafal Jakubowski, Emil Kupiec, Lukasz Jan Niewiara, Tomasz Tarczewski, Lech M. Grzesiak

“…Machines with highly saturated magnetic circuits are utilized to maximize drive efficiency. However, their control is non-trivial due to highly non-linear characteristics, and therefore, an accurate parameter identification procedure is crucial. …”
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Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm by Suhendro Y. Irianto, Ryan Yunandar, M. S. Hasibuan, Deshinta Arrova Dewi, Nualyai Pitsachart

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