Relevant bibliographies by topics / Conventional learning method

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Conventional learning method'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Conventional learning method"

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Richard, Frimpong*1 Awal Mohammed2 Francis Ohene Boateng1. "A Comparative Study Between Experiential and Conventional Teaching Methods on Students' Retention of Mathematics Concepts." International Journal of Scientific Research and Technology 2, no. 5 (2025): 190–94. https://doi.org/10.5281/zenodo.15349652.

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The efficacy of several pedagogical approaches for teaching mathematics that improve student retention were examined in this study. Using a non-equivalent control group for both the pre- and post-test, the researcher employed a quasi-experimental design. Sixty seven (67) students in SHS 2 made up the population. A sample of 67 second-year students from Krobo Community Day Senior High Schools served as the study's subject conducted in 2022/2023. Experiential learning was the mode of intervention for the experimental group, whereas traditional/conventional teaching method was employed by the con
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Rohmawati, Lutfi. "PENGARUH METODE PEMBELAJARAN IOC (INSIDE OUTSIDE CIRCLE) TERHADAP KEAKTIFAN DAN PRESTASI BELAJAR SISWA (Studi Eksperimen Siswa Kelas X SMA NU Widasari pada Mata Pelajaran Ekonomi)." Equilibrium: Jurnal Penelitian Pendidikan dan Ekonomi 15, no. 02 (2019): 1–15. http://dx.doi.org/10.25134/equi.v15i02.1615.

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Abstract: The purpose of this study was to find out: (1) An overview of student learning activeness between the experimental classes using the IOC learning method (Inside Outside Circle) and the control class using conventional learning methods. (2) Differences in the initial test (pre-test) of learning between the experimental classes using the IOC learning method (Inside Outside Circle) with the control class using conventional learning methods. (3) Differences in the final (post-test) learning tests between the experimental classes using the IOC learning method (Inside Outside Circle) and t
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Golubinskiy, Andrey, and Andrey Tolstykh. "Hybrid method of conventional neural network training." Informatics and Automation 20, no. 2 (2021): 463–90. http://dx.doi.org/10.15622/ia.2021.20.2.8.

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The paper proposes a hybrid method for training convolutional neural networks. The method consists of combining second and first-order methods for different elements of the architecture of a convolutional neural network. The hybrid convolution neural network training method allows to achieve significantly better convergence compared to Adam; however, it requires fewer computational operations to implement. Using the proposed method, it is possible to train networks on which learning paralysis occurs when using first-order methods. Moreover, the proposed method could adjust its computational co
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Kusuma, Sumardiansyah Perdana. "Pengaruh Metode Pembelajaran dan Berpikir Kreatif Terhadap Hasil Belajar Sejarah Siswa SMA." Jurnal Pendidikan Sejarah 3, no. 2 (2014): 28. http://dx.doi.org/10.21009/jps.032.04.

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 This research aims to investigate the impact of learning methods and creative thinking skills toward history learning outcome of students at Al-Azhar Kelapa Gading Islamic Senior High School. The method used in this research is experimental research method with the treatment design by level 2 x 2. The instrumen used to assess students creative thinking’s skills is in quesionnaire form, while the instrument used to assess students achievement is in the multiple choice form. The result show that: 1) history learning outcome of students using mind mapping learning methods are highe
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Ito, Ryuji, Hajime Nobuhara, and Shigeru Kato. "Transfer Learning Method for Object Detection Model Using Genetic Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (2022): 776–83. http://dx.doi.org/10.20965/jaciii.2022.p0776.

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This paper proposes a transfer learning method for an object detection model using a genetic algorithm to solve the difficulty of the conventional transfer learning of deep learning-based object detection models. The genetic algorithm of the proposed method can select the re-learning layers automatically in the transfer learning process instead of a trial-and-error selection of the conventional method. Transfer learning was performed using the EfficientDet-d0 model pre-trained on the COCO dataset and the Global Wheat Head Detection (GWHD) dataset, and experiments were conducted to compare fine
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Li, Chengbo, Yu Zhang, and Charles C. Mosher. "A hybrid learning-based framework for seismic denoising." Leading Edge 38, no. 7 (2019): 542–49. http://dx.doi.org/10.1190/tle38070542.1.

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Noise attenuation has been a long-standing problem in seismic data processing. It presents unique challenges on land due to a complex near surface coupled with unavoidable environmental noise sources. In many cases, weak signals are embedded in much stronger noise, which makes conventional methods less effective at extracting those signals. In addition, conventional methods may lack adaptability to various noise types and patterns. Machine learning has shown great promise in solving geophysical problems including seismic data processing and interpretation. Here, we propose a novel method that
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Saputra, Alexander Pratama, Yos Sudarman, and Marzam Marzam. "PENGGUNAAN METODE KONVENSIONAL OLEH GURU PADA PEMBELAJARAN SENI BUDAYA (MUSIK) DI SMP NEGERI 2 PAINAN." Jurnal Sendratasik 8, no. 4 (2019): 68. http://dx.doi.org/10.24036/jsu.v7i4.105110.

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Abstract The study aims to explain the using of conventional learning method in the learning of cultural art (music) at SMP 2 in Painan. Besides of the usage of the relevant studies as additional references, then theoretical framework is used in this study that related to study and learn, conventional learning method and cultural art learning. Based on theoretical framework, it is known that the method used is conventional learning method such as lecture, discussion, demonstration/excerice and assignment. The result of study and discussion shows that the teachers teach the cultural art (music)
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Elferida, Sormin. "Use of Practicum Learning Methods in Improving Learning Outcomes." International Journal of Social Science And Human Research 06, no. 07 (2023): 4183–90. https://doi.org/10.5281/zenodo.8153449.

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The purpose of this research is to find out whether the application of practicum methods can improve learning outcomes as well as to know how much the increase in chemistry is learning outcomes that are taught using the practicum method. The research method used in this study is a quasi-experimental design with two groups, a pretest and a post-test. Sample In this study, there were 56 students in class XI of 28 students as an experimental class and 28 students as a class control taken by purposive sampling technique. The research result shows that the significance value (Sig.) in the experimen
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Octavia, Vera. "EFEKTIFITAS PEMBELAJARAN STATISTIKA DENGAN METODE TEAM ASSISTED INDIVIDUALIZATION TERHADAP HASIL BELAJAR MAHASISWA." Jurnal Pendidikan Matematika Universitas Lampung 10, no. 2 (2022): 170–85. http://dx.doi.org/10.23960/mtk/v10i2.pp170-185.

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One of the causes of low learning outcomes is due to lecture activities that have not used appropriate learning methods. One alternative learning method that can be used in lectures related to arithmetic, such as statistics and mathematics, is by applying the Team Assisted Individualization method. The purpose of this study is to determine the differences in statistical learning outcomes between students who applied the Team Assisted Individualization learning method and those applied conventional learning methods and the effect of the learning methods used on statistical learning outcomes. Th
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Bankar, Akshay, and K. P. Wani. "Development of Non-Conventional Method of Lissajous pattern for gear fault diagnosis using Machine Learning Technique." Journal of Physics: Conference Series 2601, no. 1 (2023): 012035. http://dx.doi.org/10.1088/1742-6596/2601/1/012035.

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Abstract Gears are major elements in machine parts and automotive systems, to transmitting torque and energy between components. As gears are under severe loads and stresses, they can create different types of faults and defects over period, impacting their own performance and reliability. Fault detection is an essential part of gear maintenance because that allows potential issues to be detected before they induce failures or expensive breakdowns. Technicians can identify early indications of wear, imbalance, damage, or even other concerns that might compromise their performance or lead to pr
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Dissertations / Theses on the topic "Conventional learning method"

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Pizarchik, Mary. "The effects of experiential learning: An examination of three styles of experiential education programs and their implications for conventional classrooms." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3305.

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Using methodologies of interviews and observation, this study focuses on three distinctive and successful kinds of experiential education: a summer arts program, an outdoor science program and a wilderness education program. The project applies insights from the programs to the central question of this thesis: How can experiential learning be utilized within the traditional classroom given the constraints of the No Child Left Behind Law and standardized teaching?
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Owusu, James. "The impact of constructivist-based teaching method on secondary school lerners' errors in algebra." Diss., 2015. http://hdl.handle.net/10500/19207.

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The aim of this study was to investigate the comparative effects of Constructivist-Based Teaching Method (CBTM) and the Traditional Teaching Method (TTM) on Grade 11 Mathematics learners’ errors in algebra. The constructivist learning theory (CLT) was used to frame this study. Mainly, CLT was used to influence the design of CBTI to hone participants’ errors in algebra that militate against their performance in Mathematics. The study was conducted in the Mpumalanga Province of South Africa with a four-week intervention programme in each of the two participating secondary schools. Participants c
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Books on the topic "Conventional learning method"

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choi, shine, saara särmä, cristina masters, marysia zalewski, michelle lee brown, and swati parashar. Ripping, Cutting, Stitching. Lexington Books, 2023. https://doi.org/10.5040/9798881813505.

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This book presents a collective mediation on writing, methods, violences, and un/becomings in global politics. It combines narratives, fictional stories, academic discussions, passionate unwindings, imagined futures, and more. The editor's intention is to offer a theoretically creative work which engages extensively with the visual and affective to un-discipline knowledge and modes of expression. The book’s point of departure is a conventional academic conference and its peculiar academic concerns (which many readers will only be too familiar with), using this to open up to broader and deeper
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Sicari, Rosa, Edyta Płońska-Gościniak, and Jorge Lowenstein. Stress echocardiography: image acquisition and modalities. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198726012.003.0013.

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Stress echocardiography has evolved over the last 30 years but image interpretation remains subjective and burdened by the operator’s experience. The objective operator-independent assessment of myocardial ischaemia during stress echocardiography remains a technological challenge. Still, adequate quality of two-dimensional images remains a prerequisite to successful quantitative analysis, even using Doppler and non-Doppler based techniques. No new technology has proved to have a higher diagnostic accuracy than conventional visual wall motion analysis. Tissue Doppler imaging and derivatives may
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Book chapters on the topic "Conventional learning method"

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Basheer, Firdaus, Mohamed Saleem Nazmudeen, and Fadzliwati Mohiddin. "Comparative Analysis Between Conventional Method Versus Machine Learning Method for Pipeline Condition Prediction." In Materials Forming, Machining and Tribology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70009-6_6.

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Chang, Ruey-Feng, Yao-Sian Huang, and Yan-Wei Lee. "Texture Analysis for Breast Ultrasound Using Conventional Method and Deep Learning." In Handbook of Texture Analysis. CRC Press, 2024. http://dx.doi.org/10.1201/9780367486099-9.

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Blank, Andreas, Lukas Zikeli, Sebastian Reitelshöfer, Engin Karlidag, and Jörg Franke. "Augmented Virtuality Input Demonstration Refinement Improving Hybrid Manipulation Learning for Bin Picking." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_32.

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AbstractBeyond conventional automated tasks, autonomous robot capabilities aside human cognitive skills are gaining importance in industrial applications. Although machine learning is a major enabler of autonomous robots, system adaptation remains challenging and time-consuming. The objective of this research work is to propose and evaluate an augmented virtuality-based input demonstration refinement method improving hybrid manipulation learning for industrial bin picking. To this end, deep reinforcement and imitation learning are combined to shorten required adaptation timespans to new compon
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Verhaeghe, Jarne, Jeroen Van Der Donckt, Femke Ongenae, and Sofie Van Hoecke. "Powershap: A Power-Full Shapley Feature Selection Method." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26387-3_5.

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AbstractFeature selection is a crucial step in developing robust and powerful machine learning models. Feature selection techniques can be divided into two categories: filter and wrapper methods. While wrapper methods commonly result in strong predictive performances, they suffer from a large computational complexity and therefore take a significant amount of time to complete, especially when dealing with high-dimensional feature sets. Alternatively, filter methods are considerably faster, but suffer from several other disadvantages, such as (i) requiring a threshold value, (ii) many filter me
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Nair, Rashmi S., and Rohit Agrawal. "An Integrated Approach of Conventional and Deep Learning Method for Underwater Image Enhancement." In Soft Computing for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_14.

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Kawasaki, Yoshifumi, Akai Akihito, and Ryusuke Hirao. "Development of Vehicle State Estimation Method for Dedicated Sensor-Less Semi-active Suspension Using AI Technology." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_125.

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AbstractThis paper presents a sensor-less vehicle state estimation method using a neural network for semi-active suspensions. This method surpasses conventional mathematical models in performance and reduces calibration effort. The developed system, logic, and learning method are designed to address AI-specific challenges such as increased processing load and learning techniques, and their performance is validated through simulations and real-world tests. The results show that this system performs on par with those using dedicated sensors.
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Chorapalli, Jnanendra Vijay Kumar, and Soukat Kumar Das. "Sustainable Method for Determining Shear Strength Parameters by Machine Learning." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_120.

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AbstractThe conventional methods for determining the shear strength parameters of soil, namely cohesion (C) and angle of internal friction (φ), involve time-consuming and expensive machinery. Also, the extraction metal ore processing into metal and machine manufacturing involves a high level of carbon emission. During the operation of these machines a large quantity of electricity is generated in thermal power plants, leading to an indirect increase in the carbon footprint, thus suggesting a need for the adoption of more sustainable practices. This study is aimed at reducing net zero emission
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Chabalala, Shumile, Pius Owolawi, and Sunday Ojo. "CNN to BiLSTM: Enhancing Setswana Named Entity Recognition." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85856-7_8.

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Abstract In order to handle Named entity recognition (NER) in the Setswana language, this study applies transfer learning from convolutional neural networks (CNN) to bidirectional long short-term memory (BiLSTM) networks. We propose a transfer learning approach that leverages an existing CNN model trained to detect features in Setswana text data. Then, utilizing these attributes, a BiLSTM model specifically designed for NER is updated with the goal of improving performance and overcoming Setswana's lack of annotated data. Our study on Setswana NER corpora shows the effectiveness of this transf
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Shimomura, Mitsuhiko, Masahiro Fujiwara, Yasutoshi Makino, and Hiroyuki Shinoda. "Estimation of Frictional Force Using the Thermal Images of Target Surface During Stroking." In Haptics: Science, Technology, Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06249-0_27.

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AbstractWe propose a method for estimating the frictional force between a contacted surface and the human touch using thermal video images captured using an infrared thermographic camera. Because this method can estimate force remotely, its application to various situations, in which the measurement is difficult to obtain using conventional contact-based methods, is expected. Furthermore, thermal images have the advantage of measuring physical quantities directly related to frictional force. As a result of machine learning using the measured data from multiple subjects and materials, we succee
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Lee, Sangkyu, and Issam El Naqa. "Conventional Machine Learning Methods." In Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83047-2_3.

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Conference papers on the topic "Conventional learning method"

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Agalya, D., and S. Kamalakkannan. "Detection of Brain Tumor Using Transfer Learning Using Conventional Autoencoder with Long Short Term Memory Method." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883552.

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Chen, Wenwu, Shijie Feng, and Chao Zuo. "Deep-learning-enabled Temporally Super-resolved Multiplexed Fringe Projection Profilometry: High-speed kHz 3D Imaging with Low-speed Camera." In Computational Optical Sensing and Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cosi.2024.cf4a.5.

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Deep learning-enabled multiplexed fringe projection profilometry allows to achieve high-resolution and high-speed 3D imaging at near-one-order of magnitude-higher 3D frame rate with conventional low-speed cameras. Then the method is demonstrated by measuring a transient scene of bullet fired from a toy gun, at 1,080 Hz using 120 Hz cameras.
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Xu, Chenyu, Zhouyu Jin, Bo Xiong, You Zhou, and Xun Cao. "3D Image Restoration using Implicit Neural Representations for Brightfield and Widefield Fluorescence Microscopy." In Computational Optical Sensing and Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cosi.2024.cth4b.5.

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3D stacks captured by conventional brightfield and widefield fluorescence microscopes suffer from inter-plane crosstalk, hindering high-quality 3D imaging. We present a physics-informed self-supervised machine learning method for 3D image stack restoration.
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Haghshenas, Majid, and Ranganathan Kumar. "Curvature Estimation Modeling Using Machine Learning for CLSVOF Method: Comparison With Conventional Methods." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-5415.

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Abstract Despite extensive progress in recent decades, curvature estimation in two-phase models remains a challenge. Well-established curvature computing techniques such as distance function, smoothed volume fraction and height-function directly estimate the interface curvature from the implicit representation of the interface. Most recently, machine learning approach has been incorporated in computational physics simulation. Machine learning is a set of algorithms that can be utilized for training a system which allows predicting the output in the future. In this work, we train a curvature es
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Permana, Julius I., Annisa Jusuf, Bambang K. Hadi, and Arief Yudhanto. "Stress analysis of flying V-like non-conventional aircraft structures using finite element method." In MACHINE LEARNING AND INFORMATION PROCESSING: PROCEEDINGS OF ICMLIP 2023. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0181672.

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Temizel, Cenk Temizel, Uchenna Odi, Nouf Al-Sulaiman, et al. "Production Forecasting in Conventional Oil Reservoirs Using Deep Learning." In SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209277-ms.

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Abstract Accurate estimation of the estimated ultimate recovery (EUR) is critical in decision making processes related to the development of conventional oil reservoirs. Existing methods have limitations when it comes to predicting such long-term production behaviors. This study analyzes the performance of deep learning methods such as long short-term memory (LSTM) neural networks on time-series data, and their effective application to accurately estimate the EUR in conventional reservoirs. Synthetic data that are realistic and representative of many major conventional oil reservoirs were gene
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Eaglin, Gerald, and Joshua Vaughan. "Leveraging Conventional Control to Improve Performance of Systems Using Reinforcement Learning." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3307.

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Abstract While many model-based methods have been proposed for optimal control, it is often difficult to generate model-based optimal controllers for nonlinear systems. One model-free method to solve for optimal control policies is reinforcement learning. Reinforcement learning iteratively trains an agent to optimize a reward function. However, agents often perform poorly at the beginning of training and require a large number of trials to converge to a successful policy. A method is proposed to incorporate domain knowledge of dynamics and control into the controllers using reinforcement learn
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Rumenapp, Joseph. "Memoing-as-Method-and-Data: Teaching and Learning Post-Qualitative Methods in a Conventional Humanist Context." In 2019 AERA Annual Meeting. AERA, 2019. http://dx.doi.org/10.3102/1433540.

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Rashid, Hori, Nawras Khudhur, Yusuke Hayashi, and Tsukasa Hirashima. "The Effect of Logical Argument Recomposition using Triangular Logic Model on Critical Thinking Compared to Conventional Method." In ICEEL 2022: 2022 6th International Conference on Education and E-Learning. ACM, 2022. http://dx.doi.org/10.1145/3578837.3578869.

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Firdos, Kauser, and Bhargavi Deshpande. "Security Analysis of Conventional Attack by Suitable RFID Based Deep Learning Method in Industrial IoT." In 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON). IEEE, 2023. http://dx.doi.org/10.1109/indiscon58499.2023.10270683.

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Reports on the topic "Conventional learning method"

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Porto, Stella C., Jacqueline Pinto Mota, and Andrea Attis Beltran. An Integrated Approach to Impact Evaluation and Recognition of Learning. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013075.

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MEiRA is a novel method for evaluating learning effectiveness, emphasizing practical knowledge application and learner achievement. It transcends traditional metrics by valuing the learning journey and its outcomes equally. Applicable in organizational training and broader learning contexts, its designed for scenarios where learners may not be part of a known organization. MEiRA follows the learning journey through five pillars: Engagement, Perception and Appreciation, Cognition and Knowledge Acquisition, Intention and Commitment, and Transfer and Impact Stories. It uses open badges as an intr
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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Kaffenberger, Michelle, Jason Silberstein, and Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.

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While conventional interventions and evaluations address the symptoms of the learning crisis, there is growing acknowledgement that widespread and sustained learning improvements will require systems approaches that diagnose and address the root causes of low learning. This paper presents and applies three methods to evaluate education systems and inform how to improve system coherence for learning. First, we use learning trajectories to evaluate the dynamics of children’s learning in 22 low- and middle-income countries. Second, we present a set of principles called the ALIGNS principles and s
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Voegeli, Sam. PR-317-17700-WEB Accuracy of Temperature Logging for Calculating Gas Inventory in Storage Caverns. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011606.

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Tuesday, August 8, 2019 PRESENTER: Sam Voegeli, M.Sc., RESPEC HOST: John Jackson, Enbridge MODERATOR: Laurie Perry, PRCI CLICK BUY/DOWNLOAD TO ACCESS WEBINAR REGISTRATION LINK The PRCI Underground Storage Technical Committee invites you to join them as they present the results from a research project that evaluated the accuracy of downhole wireline temperature logging, and the related calculation methods, to estimate gas inventory in storage caverns. Expected Benefits/Learning Outcomes: � Gain an understanding of how inaccurate wireline temperature logging impacts the estimated gas inventory i
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SECOND-ORDER ANALYSIS OF BEAM-COLUMNS BY MACHINE LEARNING-BASED STRUCTURAL ANALYSIS THROUGH PHYSICS-INFORMED NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.10.

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The second-order analysis of slender steel members could be challenging, especially when large deflection is involved. This paper proposes a novel machine learning-based structural analysis (MLSA) method for second-order analysis of beam-columns, which could be a promising alternative to the prevailing solutions using over-simplified analytical equations or traditional finite-element-based methods. The effectiveness of the conventional machine learning method heavily depends on both the qualitative and the quantitative of the provided data. However, such data are typically scarce and expensive
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DEEP LEARNING DAMAGE IDENTIFICATION METHOD FOR STEEL- FRAME BRACING STRUCTURES USING TIME–FREQUENCY ANALYSIS AND CONVOLUTIONAL NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.8.

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Lattice bracing, commonly used in steel construction systems, is vulnerable to damage and failure when subjected to horizontal seismic pressure. To identify damage, manual examination is the conventional method applied. However, this approach is time-consuming and typically unable to detect damage in its early stage. Determining the exact location of damage has been problematic for researchers. Nevertheless, detecting the failure of lateral supports in various parts of a structure using time–frequency analysis and deep learning methods, such as convolutional neural networks, is possible. Then,
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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting ther
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