Fisheye omnidirectional stereo depth estimation assisted with edge-awareness by Junren Sun, Hao Xue, Shibo Guo, Xunqi Zheng

“…Subsequently, a spherical alignment method is used to map image features from different cameras to a unified spherical coordinate system. A cost volume is built for different depth hypotheses, which is then regularized using a 3D convolutional network. …”
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Machine Learning-Based Weld Classification for Quality Monitoring by Rojan Ghimire, Rajiv Selvam

“…Ensuringweld quality is critical when safety, reliability, performance, and the associated cost are taken into account. A ungsten inert gas (TIG) weld quality assessment can be a laborious and time-consuming process. …”
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Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning by Dinesh Kumar Saini, Anshul Rana, Jyotirmoy Halder, Mohammad Maruf Billah, Harsimardeep S. Gill, Jinfeng Zhang, Subash Thapa, Shaukat Ali, Brent Turnipseed, Karl Glover, Maitiniyazi Maimaitijiang, Sunish K. Sehgal

“…In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.…”
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Digital Melting Curve Analysis for Multiplex Quantification of Nucleic Acids on Droplet Digital PCR by Xiaoqing Dai, Meng Cao, Zunliang Wang

“…This approach eliminates the need for complex fluorescent probe design, reducing both costs and dependence on fluorescence channels. We developed a convolutional neighborhood search algorithm to correct droplet displacement during heating, ensuring precise tracking and accurate extraction of melting curves. …”
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. …”
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Enhancing classification efficiency in capsule networks through windowed routing: tackling gradient vanishing, dynamic routing, and computational complexity challenges by Gangqi Chen, Zhaoyong Mao, Junge Shen, Dongdong Hou

“…Abstract Capsule networks overcome the two drawbacks of convolutional neural networks: weak rotated object recognition and poor spatial discrimination. …”
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Just a Single-Layer CNN for Stochastic Modeling: A Discriminator-Free Approach by Evangelos Rozos

“…In this study, we propose a simpler stochastic scheme based on a single convolutional neural network (CNN) used as a generator, replacing the discriminator component of the GAN with a specifically designed cost function. …”
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Hierarchical Knowledge Transfer: Cross-Layer Distillation for Industrial Anomaly Detection by Junning Xu, Sanxin Jiang

“…By adding two additional convolutional layers to the teacher and student networks of HKT, HKT+ achieves enhanced detection capabilities at the cost of a relatively small increase in model parameters. …”
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An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries by Arun Jose, Sonam Shrivastava

“…This research specifically examines the potential of the convolutional neural network–long short-term memory algorithm to improve the precision of State of Health forecasts for the battery model. …”
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Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications by Adrian-Nicolae Buturache, Stelian Stancu

“…The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. …”
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Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

“…The proposed framework is shown to significantly reduce load demand, peak consumption, costs, and carbon emissions while improving thermal comfort and lighting adequacy. …”
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Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks by Ling Zhao, Huili Zhang, Xianda Sun, Zhaozhuo Ouyang, Chengwu Xu, Xudong Qin

“…The convolutional neural network (CNN) algorithm in deep learning has been widely applied in petroleum geology research both domestically and internationally. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

“…Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high. In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. …”
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Deep learning of the particulate and mineral-associated organic carbon fractions using a compositional transform and mid-infrared spectroscopy by Mingxi Zhang, Zefang Shen, Lewis Walden, Farid Sepanta, Zhongkui Luo, Lei Gao, Oscar Serrano, Raphael A. Viscarra Rossel

“…Our approach can complement conventional physical SOC fractionations and improve the cost-effectiveness of the measurements, especially when there are many samples to measure, thus enhancing our understanding of SOC dynamics.…”
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Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models by Franco Basso, Félix des Rotours, Tomás Maldonado, Raúl Pezoa, Mauricio Varas

“…Abstract This paper proposes an innovative methodology for detecting heavy trucks utilizing mobile phone data, addressing significant limitations inherent in traditional tracking methods, often characterized by high costs, intrusiveness, and incomplete data capture. …”
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Deep-Learning Techniques Applied for State-Variables Estimation of Two-Mass System by Grzegorz Kaczmarczyk, Radoslaw Stanislawski, Marcin Kaminski

“…The calculations are performed for three deep neural structures based on the Convolutional Neural Network (CNN) and the long short-term memory (LSTM). …”
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Digital Twin Network-Based 6G Self-Evolution by Yuhong Huang, Mancong Kang, Yanhong Zhu, Na Li, Guangyi Liu, Qixing Wang

“…To realize the future shots, we propose a long-term hierarchical convolutional graph attention model for cost-effective network predictions, a conditional hierarchical graph neural network for strategy generation, and methods for efficient small-to-large-scale interactions. …”
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Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance by Ren-Yi Kung, Nai-Hsin Pan, Charles C.N. Wang, Pin-Chan Lee

“…However, conventional inspection methods are time-, cost-, and labor-intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image-based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). …”
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A joint data and knowledge‐driven method for power system disturbance localisation by Zikang Li, Jiyang Tian, Hao Liu

“…A spatiotemporal graph convolutional network is proposed to effectively capture the spatiotemporal dependence with a limited number of PMU measurements. …”
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Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data by Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

“… Introduction: The rapid expansion of energy networks has significantly increased energy consumption, resulting in higher electricity costs. Abnormal energy usage in buildings and industries, often caused by system malfunctions, leads to substantial energy waste. …”
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