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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Jonathan, Anto Leoba Bamisile, Olusola Cai, Dongsheng Joseph, Ejiyi Chukwuebuka Junior Nkou Nkou, Joseph Huang, Qi Chengdu University of Technology School of Nuclear and Automation Engineering Chengdu China Chengdu University of Technology Sichuan Indust. Internet Intelligent Monitoring and Applic. Engineering Technology Research Center Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350347159

Knowledge graphs (KG) that express the structure of relations between entities have become an increasingly attractive area in research into cognition and intelligence at the human level. One of the most known challenges in KG, which is the fact that a large number of training examples are required for every relation to predict a new triple, has sparked increasing interest in the few-shot link prediction method. Existing work on few-shot link prediction does not fully exploit entity information such as symmetry. In this work, we made a prediction of new triples about relations by observing only a few associative triples. In order to make new predictions of triples, we concentrated on the transfer of the most important common information and the reconciliation of the entity between the set and query set support by using Intra-Graph. Our experiment are lead on two datasets and demonstrated that our results achieved the state-of-the-art in few-shot link prediction. © 2022 IEEE.

关键词： Knowledge graph

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans 7

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Addo, Daniel Al-Antari, Mugahed A. Zhou, Shijie Sarpong, Kwabena Butun, Ertan Talo, Muhammed Abualkebash, Humam Ukwuoma, Chiagoziem C. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Sejong University DaeyangAI Center Department of Artificial Intelligence Seoul Korea Republic of Firat University Department of Computer Engineering Elaziǧ Turkey Firat University Department of Software Engineering Elazig Turkey Kocaeli University Department of Mechatronics Engineering Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

ISBN: (纸本)9798350342154

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (ViT). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile ViT is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs. © 2023 IEEE.

关键词： COVID-19

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Multi-Dimensional Hybrid Quantitative Analysis of Teaching Evaluation by Big Data technology: A Case Study on Chinese Universities

Multi-Dimensional Hybrid Quantitative Analysis of Teaching E...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Laiying Long Yingzi Wen Fei Li Muhao Zhang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Department of Physics Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Students' feedback is adopted as a common method of evaluating teaching quality, which is crucial in monitoring teaching quality in colleges and universities. However, various factors such as area, gender, and age of the students can influence the model, content, and subjective bias of teaching quality evaluation. Therefore, we conducted a questionnaire survey for students at 90 universities in China and analyzed the data using Tableau and SPSS. The current teaching evaluation system lacks model diversity, discipline specificity, and objective standardization. Thus, improvement considering the relationship between students' cognition, attitude, and participation is proposed to provide a significant reference on the higher education reform system based on the result.

关键词： Surveys Statistical analysis Education Evaluation models Standardization Receivers Big Data

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Research on Neutron and Gamma Discrimination Method Based on Anti-Aliasing Pulse Shaping

SSRN

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SSRN 2023年

作者： Zhao, Yang Wang, Min Hu, Biao Tan, Liang Liang, Zhiyang Chen, Fengli Liang, Jiangtao Liu, Xianghe Faculty of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

The detectors commonly used in neutron measurement are sensitive to both neutrons and gamma, so it is necessary to distinguish neutrons from gamma rays. Traditional pulse shape discriminators (PSD) include charge comparison method and pulse gradient analysis method, which are representative and need less calculation. In addition, there are digital PSD techniques based on pulse shaping. In this paper, the anti-aliasing shaping method based on Z transform is presented, and the method is applied to the neutron and gamma pulses obtained from simulation and experiment, and the anti-aliasing shaping method is realized, which proves the correctness of the anti-aliasing shaping method. In addition, neutron and gamma pulses are simulated, and the discrimination of neutron and gamma is realized by pulse shaping ratio method, which further verifies the feasibility of the shaping method. At last, the neutron and gamma pulses from Am-Be neutron source detected by EJ276 are analyzed, and the discrimination effects of the three methods are compared: (1) charge comparison method, (2) pulsed gradient analysis method, (3) pulse shaping ratio method. The experimental results show that the pulse shaping ratio method is superior to the charge comparison method and the pulse gradient method. © 2023, The Authors. All rights reserved.

关键词： Neutrons

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Wearable synthetic leather-based high-performance X-ray shielding materials enabled by the plant polyphenol-and hierarchical structure-facilitated dispersion

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Collagen and Leather 2024年第1期5卷 113-126页

作者： Linping Yan Jibo Zhou Hao Li Rui Zhong Junxin Zhuang Xiaohui Xu Yaping Wang Xuepin Liao Bi Shi College of Biomass Science and Engineering Sichuan UniversityChengdu 610065People's Republic of China National Engineering Laboratory for Clean Technology of Leather Manufacture Sichuan UniversityChengdu 610065People's Republic of China Key Laboratory of Radiation Physics and Technology Ministry of Education Institute of Nuclear Science and TechnologySichuan UniversityChengdu 610065People's Republic of China Mingxin Automotive Leather Co. Ltd.Jiaxing 314006People's Republic of China Department of Chemistry Materials Innovation FactoryUniversity of LiverpoolLiverpool L73NYUK

Effective protection against X-ray is the premise of utilizing the X-ray,thus it is critical to develop novel X-ray shielding materials with both low density and high X-ray attenuation *** the even distribution of high-Z element components is of great significance for increasing the attenuation efficiency of X-ray shielding materials,in this study,the microfiber membrane(MFM),a type of synthetic leather featuring hierarchical structure was chosen to provide large surface area for the dispersion of rare earth(RE)***,plant polyphenol was utilized to achieve the stable loading and uniform dispersion of the Ce or Er into *** from the assistance of polyphenol and hierarchical structure of MFM,the even dispersion of RE element was successfully *** resultant shielding materials displayed approximately 10%superior X-ray attenuation efficiency compared to that without polyphenol,and an averagely 9%increment in X-ray attenuation efficiency than that without hierarchical ***,the obtained composite with a thickness of 2.8 mm displayed superior X-ray shielding performance compared to 0.25 mm lead sheet in 16-83 keV and retained an ultralow density of 1.4 g cm^(-3).Our research results would shed new light on the manufacture of high-performance X-ray shielding materials with excellent X-ray shielding performance.

关键词： X-ray shielding Plant polyphenol Microfiber membrane Synthetic leather Hierarchical structure Dispersity

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Esther Stacy E.B Aggrey Qin Zhen Seth Larweh Kodjiku Delali Linda Fiasem Collins Sey Chukwuebuka Joseph Ejiyi Nsikakabasi Williamson Ntia School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications.

关键词： Accuracy Magnetic resonance imaging Computational modeling Transfer learning Brain tumors Brain modeling Data models Resonance Computational efficiency Positron emission tomography

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Seismic landslide detection based on Transformer and RNN algorithm

Seismic landslide detection based on Transformer and RNN alg...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE International Conference on

作者： Qingru Yang Xin Zhang Yuxuan Xie Shuhong Huang Junfeng Kong Xiaoyu Zhang College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

This paper proposes a seismic landslide detection method based on Transformer and RNN algorithm. This method uses seismic data and topographic data as inputs, encodes the data through the Transformer model, and then inputs the encoded data into the RNN model for classification. In the training process, we use the cross entropy loss function and Adam optimizer. To verify the effectiveness of the method, we used public data sets for experiments, and the results show that our proposed method can significantly improve the accuracy of seismic landslide detection. In addition, we interpret the results by visualization method and analyze the performance of the model under different parameters. Our method is expected to provide effective support for earthquake landslide prediction and early warning.

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Research on SWCNT non-contact strain sensor based on photoluminescence principle 4

Research on SWCNT non-contact strain sensor based on photolu...

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4th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2023

作者： Zhang, Jie Zheng, Fuzhong Du, Junjie Lu, Yuting Li, Zhonghao Wu, Ying Intelligent Manufacturing and Automobile School Chongqing College of Electronic Engineering Chongqing China School of Automation Chengdu University of Information Technology Chengdu China PetroChina Changqing Oilfield Company NO.7 Oil Production Plant Qingyang China College of Mechanical Engineering Sichuan University of Science and Engineering Chengdu China School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798350314526

Developing an optical full field non-contact strain sensor based on the spectral shift characteristics of single walled carbon nanotube (SWCNT), combining with the principle of photoluminescence is an emerging application field of SWCNT material that has important application prospects. The current paper analyzed the photoluminescence principle of SWCNT material, and discussed the optical path for the fluorescence excitation and acquisition of the SWCNT. Selection of the optical components in the optical path are completed. Then, based on the theory of polarize light, the fluorescence excitation of SWCNT and the controllable adjustment of polarize light in fluorescence spectra are studied. Experimental results indicate that the controllable adjustment accuracy of the polarize light is high, which conforms to Malus's law. © 2023 IEEE.

关键词： Photoluminescence

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Temperature Field Simulation and Experimental Study of Anti‑backlash Single‑Roller Enveloping Hourglass Worm Gear

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Chinese Journal of Mechanical engineering 2020年第4期33卷 77-86页

作者： Shisong Wang Shike Wang Jie Wang Xingqiao Deng School of Mechanical Engineering Xihua UniversityChengdu 611730China College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

In gear transmission,temperature rise has a non-negligible impact on the accuracy,noise and transmission ***,there is no relevant research on the temperature rise of the anti-backlash single-roller enveloping hourglass worm(ASEHW)*** solve this problem,based on tribology principle and Hertz contact theory,the thermal power calculation method of the ASEHW gear was proposed for the first time and thermal analysis was carried out by Ansys *** bulk temperature of the ASEHW gear under four different rotating speed(300 r/min,600 r/min,900 r/min,1200 r/min)is *** main factors causing temperature rise of the ASEHW gear are analyzed ***,an experimental study is performed to verify the simulation results and validate the theory *** theory presented in this paper provides a solution for the thermal power calculation of ASEHW *** research provides a theoretical basis for further optimization of ASEHW gear.

关键词： Roller enveloping hourglass worm gear Finite element analysis Temperature field

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Evaluation model construction of automobile appearance design based on random forest algorithm

Evaluation model construction of automobile appearance desig...

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2021 3rd International Conference on applied Machine Learning and Data Science, ICAMLDS 2021

作者： Zheng, Xundong Yi, Shanshan Deng, Xiaotong College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Random forest algorithm is a nonlinear supervised classification algorithm, which is based on the idea of ensemble learning and consists of multiple decision tree classifiers. The algorithm has strong generalization ability, fault tolerance ability and nonlinear fitting ability, and can learn the deep-seated laws in the sample system according to a small number of samples using information entropy as the criterion. In the form of questionnaire survey, this paper collects the main evaluation factors of automobile appearance modeling design for 100 consumers or potential consumers. After selecting the evaluation factors, 50 consumers or potential consumers score a car according to these evaluation factors, to collect the original data. Experimental results show that the accuracy of the algorithm is as high as 84%, which shows that the model has such a high accuracy that it can be applied to the evaluation of automobile appearance modeling design. At the same time, this method can also be transferred to the appearance modeling design of other models. © Published under licence by IOP Publishing Ltd.

关键词： Automobiles

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