Article
Computer Science, Artificial Intelligence
Jincheng Chen, Feiding Zhu, Yuge Han, Zhendao Xu, Qing Chen, Dengfeng Ren
Summary: This paper presents a temperature reconstruction generative adversarial network (TRe-GAN) that can predict the temperature field of all equipment surfaces through one observable temperature image. The network incorporates computer vision semantics and heat transfer semantics, and utilizes conditions inputs and an optimization output module to enhance vertex association and mitigate over-fitting. Experimental results show that TRe-GAN performs well in predicting the global temperature field and has great application potential.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Pramod Gaur
Summary: Image processing is a technique used to apply various operations to images to improve them or extract information, with facial recognition being a prominent application. This study examines the accuracy of categorizing human facial expressions using deep learning and transfer learning methods, proposing a deep hybrid learning approach that combines multiple deep learning models.
IMAGE AND VISION COMPUTING
(2022)
Article
Agronomy
Muhammad Mostafa Monowar, Md. Abdul Hamid, Faris A. Kateb, Abu Quwsar Ohi, M. F. Mridha
Summary: This paper introduces a self-supervised leaf disease clustering system for classifying plant diseases, which is cost-effective and applicable to various plants. It utilizes a deep convolutional neural network to generate clusterable embeddings and combines with k-means algorithm for classification.
Article
Computer Science, Information Systems
Shaohui Mei, Yunhao Geng, Junhui Hou, Qian Du
Summary: This paper proposes a method for reconstructing hyperspectral images (HSIs) using convolutional neural network (CNN) and easily acquired RGB images. The proposed SSR-Net can predict HSIs from RGB images without prior knowledge, and outperforms traditional methods in terms of HSI quality and classification performance.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Thermodynamics
Seyed Moein Rassoulinejad-Mousavi, Firas Al-Hindawi, Tejaswi Soori, Arif Rokoni, Hyunsoo Yoon, Han Hu, Teresa Wu, Ying Sun
Summary: This research explores how deep learning can adapt to new datasets with limited data, studying convolutional neural networks and transfer learning. The study found that transfer learning outperforms convolutional neural networks in cases of data scarcity, demonstrating higher accuracy and lower false negative rates.
APPLIED THERMAL ENGINEERING
(2021)
Article
Acoustics
Hengrong Lan, Changchun Yang, Fei Gao
Summary: In this paper, a jointed feature fusion framework (JEFF-Net) based on deep learning is proposed to reconstruct the PA image using limited-view data. Cross-domain features from limited-view position-wise data and the reconstructed image are fused by backtracked supervision to restrain the artifacts. Experimental results demonstrate superior performance with a 135% improvement in SSIM for simulation and a 40% improvement in gCNR for in-vivo cases compared to the ground-truth.
Article
Physics, Multidisciplinary
Abid Hussain, Heng-Chao Li, Muqadar Ali, Samad Wali, Mehboob Hussain, Amir Rehman
Summary: This paper presents a CNN-based multi-hashing method that achieves significant advantages in searching and retrieving images from large databases. By employing multiple nonlinear projections and designing a loss function to minimize quantization errors, the proposed method overcomes the limitations of existing hashing methods.
Review
Computer Science, Artificial Intelligence
Kriti Ohri, Mukesh Kumar
Summary: Self-supervised learning is a form of unsupervised learning that enables networks to learn rich visual features for downstream computer vision tasks. It has the potential to revolutionize the computer vision field and can be achieved using unlabeled data.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Shaleen Bengani, Angel Arul J. Jothi, S. Vadivel
Summary: This article proposes a novel deep learning model to automatically segment the optic disc in retinal fundus images using semi-supervised learning and transfer learning concepts. Experimental results show that the proposed method performs on par with the state-of-the-art methods on the test set of the DRISHTI GS1 and RIM-ONE datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Zexi Hu, Henry Wing Fung Yeung, Xiaoming Chen, Yuk Ying Chung, Haisheng Li
Summary: The article introduces an end-to-end spatio-angular dense network (SADenseNet) for light field reconstruction, incorporating correlation blocks and spatio-angular dense skip connections to address domain asymmetry and efficient information flow issues. Through extensive experiments on real-world and synthetic datasets, it is demonstrated that SADenseNet achieves state-of-the-art performance with significantly reduced memory and computation costs, resulting in sharp and detailed reconstructed light field images that can improve the accuracy of measurement applications.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Construction & Building Technology
Qiuhan Meng, Shiguang Wang, Songye Zhu
Summary: Monitoring construction-induced vibrations is crucial for mitigating their adverse impacts. However, the unavailability of recorded construction activities hinders the efficient utilization of the vibration data for empirical modeling. In this study, a semi-supervised deep learning approach combining a 1D convolutional neural network and a Ladder network is proposed to recognize construction activity types from vibration data. Experimental results show that the proposed method achieves high accuracy even with limited labeled data, outperforming other tested algorithms.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Chemistry, Multidisciplinary
Shaoxiong Zheng, Peng Gao, Weixing Wang, Xiangjun Zou
Summary: An improved dynamic convolutional neural network (DCNN) model, named DCN_Fire, was established based on the traditional DCNN model for accurately identifying the risk of a forest fire. Transfer learning and principal component analysis were used to enhance the model's accuracy and speed. The results showed that the improved DCNN model had excellent recognition speed and accuracy, providing a technical reference for preventing and tackling forest fires.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Mehedi Masud, Amr E. Eldin Rashed, M. Shamim Hossain
Summary: Breast cancer is a common and deadly disease affecting millions of women worldwide. Researchers have proposed various convolutional neural network models to assist in diagnostic process. However, the lack of standard models and large datasets for training and validation remains a challenge. This study explores the use of transfer learning and evaluates eight pre-trained models on ultrasound images of breast cancers, as well as introduces a custom convolutional neural network that outperforms the pre-trained models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang
Summary: The success of modern farming and plant breeding relies on accurate and efficient data collection. Deep learning methods, particularly the novel DeepCorn approach, show promise in addressing the bottleneck of accurately phenotyping crops. The proposed method achieves good performance in on-ear corn kernel counting in-field tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Environmental Sciences
Ignazio Gallo, Mirco Boschetti, Anwar Ur Rehman, Gabriele Candiani
Summary: This paper investigates a method for retrieving two crop traits, Chlorophyll and Nitrogen content, from hyperspectral images using self-supervised learning. The method combines Radiative Transfer Modelling and Machine Learning Regression Algorithm to estimate the crop traits from new generation spaceborne hyperspectral data. The results demonstrate high prediction accuracy for maize crops.
Article
Thermodynamics
Mahsa Taghavi, Swapnil Sharma, Vemuri Balakotaiah
Summary: This study investigates the natural convection effects in the insulation layers of spherical storage tanks and their impact on the tanks' performance. The permeability and Rayleigh number of the insulation material are considered as key factors. The results show that as the Rayleigh number increases, new convective cells emerge and cause the cold boundary to approach the external hot boundary. In the case of large temperature differences, multiple solutions may coexist.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Jinyang Xu, Fangjun Hong, Chaoyang Zhang
Summary: This study introduces a self-induced jet impingement device for enhancing pool boiling performance in high power electronic cooling. Through visualization and parametric investigations, the effects of this device on pool boiling performance are studied, revealing the promotion of additional liquid supply and vapor exhausting. The flow rate of the liquid jet is found to positively impact boiling performance.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Wenchao Ke, Yuan Liu, Fissha Biruke Teshome, Zhi Zeng
Summary: Underwater wet laser welding (UWLW) is a promising and labor-saving repair technique. A thermal multi-phase flow model was developed to study the heat transfer, fluid dynamics, and phase transitions during UWLW. The results show that UWLW creates a water keyhole, making the welding environment similar to in air laser welding.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Xingrong Lian, Lin Tian, Zengyao Li, Xinpeng Zhao
Summary: This study investigates the heat transfer mechanisms in natural fiber-derived porous structures and finds that thermal radiation has a significant impact on the thermal conductivity in low-density regions, while natural convection rarely occurs. Insulation materials derived from micron-sized natural fibers can achieve minimum thermal conductivity at specific densities. Strategies to lower the thermal conductivity include increasing porosity and incorporating nanoscale pores using nanosize fibers.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Yasir A. Malik, Kilian Koebschall, Stephan Bansmer, Cameron Tropea, Jeanette Hussong, Philippe Villedieu
Summary: Ice crystal icing is a significant hazard in aviation, and accurate modeling of sticking efficiency is essential. In this study, icing wind tunnel experiments were conducted to quantify the volumetric liquid water fraction, sticking efficiency, and maximum thickness of ice layers. Two measurement techniques, calorimetry and capacitive measurements, were used to measure the liquid water content and distribution in the ice layers. The experiments showed that increasing wet bulb temperatures and substrate heat flux significantly increased sticking efficiency and maximum ice layer thickness.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Jinqi Hu, Tongtong Geng, Kun Wang, Yuanhong Fan, Chunhua Min, Hsien Chin Su
Summary: This study experimentally examined the heat dissipation of vibrating fans and demonstrated its inherent mechanism through numerical simulation. The results showed that the flow fields induced by the vibrating blades exhibited pulsating features and formed large-scale and small-scale vortical structures, significantly improving heat dissipation. The study also identified the impacts of different blade structures and developed a trapezoidal-folding blade, which effectively reduced the maximum temperature of the heat source and alleviated high-temperature failure crisis.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Dan-Dan Su, Xiao-Bin Li, Hong-Na Zhang, Feng-Chen Li
Summary: The boiling heat transfer of low-boiling-point working fluid is a common heat dissipation technology in electronic equipment cooling. This study analyzed the interfacial boiling behavior of R134a under different conditions and found that factors such as the initial thickness of the liquid film, solid-liquid interaction force, and initial temperature significantly affect the boiling mode and thermal resistance.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Jinyi Wu, Dongke Sun, Wei Chen, Zhenhua Chai
Summary: A unified lattice Boltzmann-phase field scheme is proposed to simulate dendrite growth of binary alloys in the presence of melt convection. The effects of various factors on the growth are investigated numerically, and the model is validated through comparisons and examinations.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Shaokun Ge, Ya Ni, Fubao Zhou, Wangzhaonan Shen, Jia Li, Fengqi Guo, Bobo Shi
Summary: This study investigated the temperature distribution of main cables in a suspension bridge during fire scenarios and proposed a prediction model for the maximum temperature of cables in different lane fires. The results showed that vehicle fires in the emergency lane posed a greater thermal threat to the cables.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Shuang-Ying Wu, Shi-Yao Zhou, Lan Xiao, Jia Luo
Summary: This paper investigates the two-phase flow and heat transfer characteristics of low-velocity jet impacting on a cylindrical surface. The study reveals that the heat transfer regimes are non-phase transition and nucleate boiling with the increase of heat transfer rate. The effects of jet impact height and outlet velocity on local surface temperatures are pronounced at the non-phase transition stage. The growth rates of heat transfer rate and liquid loss rate increase significantly from the non-phase transition to nucleate boiling stage.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Emad Hasani Malekshah, Wlodzimierz Wlodzimierz, Miros law Majkut
Summary: Cavitation has significant practical importance and can be controlled by air injection. This study investigates the natural to ventilated cavitation process around a hydrofoil through numerical and experimental methods. The results show that the location and rate of air injection have a meaningful impact on the characteristics of cavitation.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Feriel Yahiat, Pascale Bouvier, Antoine Beauvillier, Serge Russeil, Christophe Andre, Daniel Bougeard
Summary: This study explores the enhancement of mixing performance in laminar flow equipment by investigating the generation of chaotic advection using wall deformations in annular geometries. The findings demonstrate that the combined geometry can achieve perfect mixing at various Reynolds numbers.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Hui He, Ning Lyu, Caihua Liang, Feng Wang, Xiaosong Zhang
Summary: This study investigates the condensation, frosting, and defrosting processes on superhydrophobic surfaces with millimeter-scale structures. The results reveal that the structures can influence the growth and removal of frost crystals, with the bottom grooves creating a frost-free zone and conical edges promoting higher frost crystal heights. Two effective methods for defrosting are observed: hand-lifting the groove and airfoil retraction contraction on protruding structures. This research provides valuable insights into frost formation and defrosting on millimeter-structured superhydrophobic surfaces, with potential applications in anti-frost engineering.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Thiwanka Arepolage, Christophe Verdy, Thibaut Sylvestre, Aymeric Leray, Sebastien Euphrasie
Summary: This study developed two thermal concentrators, one with a 2D design of uniform thickness and another with a 3D design, using the coordinate transformation technique and metamaterials. By structuring the thermal conductor, the desired local density-heat capacity product and anisotropic thermal conductivities were achieved. The homogenized thermal conductivities were obtained from finite element simulations and cylindrical symmetry consideration. A 3D concentrator was fabricated using 3D metal printing and characterized using a thermal camera. Compared to devices that solely consider anisotropic conductivities, the time evolution characteristics of the metadevice designed with coordinate transformation were closer to those of an ideal concentrator.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)
Article
Thermodynamics
Liangyuan Cheng, Qingyang Wang, Jinliang Xu
Summary: In this study, we investigated the supercritical heat transfer of CO2 in a horizontal tube with a diameter of 10.0 mm, covering a wide range of pressures, mass fluxes, and heat fluxes. The study revealed a non-monotonic increase in wall temperatures along the flow direction and observed both positive and negative wall temperature differences between the bottom and top tube. The findings were explained by the thermal conduction in the solid wall interacting with the stratified-wavy flow in the tube.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2024)