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11,108 results on '"Wang, Zhuo"'

312. Feasibility of the development of short videos in vocational education based on the context of deep learning

Author

Wang Dong, Xu Huaizhi, and Wang Zhuo

Subjects
deep learning, short video teaching, preference features, intelligent recommendation, similarity, 00a35, Mathematics, QA1-939
Abstract

The development of information technology has brought new opportunities for the development of vocational education teaching, and short video, as a product of information technology, breaks through the limitations brought by the traditional teaching mode and provides a new thinking direction for the transformation of the teaching mode. This paper proposes a recommendation system for short videos based on deep learning, where the scene and behavioral features of the target short video are first extracted to obtain the joint features. Then, user and video modeling is realized using deep learning technology. Finally, the similarity and preference are calculated by combining the joint features of the target short video and the user short video preference features. The TOP-N with the highest user preference is obtained according to the actual demand. The recommendation list is generated to complete the intelligent recommendation of short videos based on deep learning. The experimental analysis found that the recommendation system has high performance, with a maximum recommendation accuracy of 98.2%, and is capable of fully comprehending the students’ demand preferences for short videos. The use of short videos in course teaching can make a great positive change in students’ learning attitudes, and the short video recommendation algorithm based on deep learning allows students to more accurately find short videos suitable for their learning conditions, which further improves their learning efficiency.

Published
2024
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313. Innovation of Innovation and Entrepreneurship Teaching Content and Methods under the Internet+ Education Mode

Author

Liu Chao, Wang Xiaorui, Teng Zhiyuan, Guo Chence, Wang Zhuo, and Zhang Xingxing

Subjects
j2ee, pick-kx algorithm, genetic algorithm, k-means clustering, resource allocation, innovation and entrepreneurship, 68m11, Mathematics, QA1-939
Abstract

This paper proposes a way to innovate the teaching content and methods centered on building an online teaching system and information management system for innovation and entrepreneurship. In the J2EE-based innovation and entrepreneurship online teaching system, the genetic algorithm is used to improve the Pick-KX algorithm, and the resource allocation scheme is optimized and processed by the genetic algorithm, which effectively improves the efficiency of the Pick-KX algorithm on heterogeneous servers. In the innovation and entrepreneurship information management system, the TF-IDF algorithm and Word2vec are used to convert the text into document vectors, and K-Means clustering is used to realize document clustering, while the HyperLogLog algorithm is used to carry out the system website data statistics. The 2022 computer science and technology students of a teacher training school in Chongqing, China, were used as research subjects to carry out innovative and entrepreneurial teaching practices. The student’s performance on all dimensions of innovation and entrepreneurship practice ability was above 3.5, which was a good performance. In the evaluation of the online teaching system and information management system for innovation and entrepreneurship, the evaluation scores for each dimension are higher than 3.5, which meets the needs of students. After the practice, the average scores of satisfaction with the innovation and entrepreneurship practice course and their development were 9.2 and 9.02, which were improved by 0.42 and 0.68 compared to the pre-practice.

Published
2024
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314. yNet: a multi-input convolutional network for ultra-fast simulation of field evolvement

Author

Wang, Zhuo, Wang, Xiao, Yang, Wenhua, Xiao, Yaohong, Liu, Yucheng, and Chen, Lei

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

The capability of multi-input field-to-field regression, i.e. mapping the initial field and applied conditions to the evolved field, is appealing, enabling ultra-fast physics-free simulation of various field evolvements across many disciplines. We hereby propose a y-shaped multi-input deep convolutional network, yNet, which can effectively account for combined effects of multiple mixed inputs on output field. The proposed yNet is applied to the simulation of porosity evolution in selective lasering sintering (SLS). Upon testing, yNet can simulate nearly identical porosity evolution and development to the physics-based model, with a 99.13% morphological similarity for various SLS conditions. We then effortlessly boost the porosity simulation capability to the realistic, full-component level. yNet is generally applicable to simulating various structural/morphological evolutions and other condition-concerned, continuous field evolvements even with spatially and/or temporally non-uniform evolving kinetics. Once trained, the light-weight yNet can be distributed easily and ran with limited computational resource while reducing computation time to a split second. It thus may have a transformative impact by democratizing the capability of ultra-fast and extreme-scale simulation of various field evolvements.

Published
2020

315. An Effective Strategy to Enable Argyrodite Sulfides as Superb Solid-State Electrolytes: Safeguarding Remarkable Ionic Conductivity and Interfacial Stability with Electrodes

Author

Xu, Hongjie, Yu, Yuran, Hu, Junhua, Wang, Zhuo, and Shao, Guosheng

Subjects
Condensed Matter - Materials Science
Abstract

The argyrodite sulfides are getting more and more attractive as highly promising solid-state electrolytes (SSEs) for high-performance all-solid-state batteries (ASSBs), owing to their high ionic conductivity, adequate plasticity, and decent mechanical strength. However, their poor incompatibility with Li metal anode and high voltage cathodes and as well as serious sensitivity to air significantly hinder their practical applications. Herein, we have devised an effective strategy to overcome these challenging shortcomings through modification of chalcogen chemistry under the guidance of theoretical modeling. The resultant Li6.25PS4O1.25Cl0.75 delivered excellent electrochemical compatibility with both pure Li anode and high-voltage LiCoO2 cathode, without detrimental impact upon the superb ionic conductivity of the pristine sulfide. Furthermore, the current SSE also exhibited highly improved stability to oxygen and moisture in air, with further advantage being more insulating to electrons. The remarkably enhanced compatibility with electrodes is attributed to in situ formation of solid anode electrolyte interphase (AEI) and cathode electrolyte interphase (CEI) layers. The formation of in situ AEI enabled ultra-stable Li plating/stripping at a record high current density up to 1 mAh cm-2 in Li|Li6.25PS4O1.25Cl0.75|Li symmetric cells over 1800 hours. The in situ CEI facilitated protection of the SSE from decomposition at elevated voltage. Consequently, the synergistic effect of AEI and CEI helped the LiCoO2|Li6.25PS4O1.25Cl0.75|Li battery cell to achieve markedly better cycling stability than that using the pristine Li6PS5Cl as SSE, at a high areal loading of the active cathode material (4 mg cm-2). This work adds a desirable SSE in the argyrodite sulfide family, so that high-performance solid battery cells could even be fabricated in ambient air.

Published
2020

316. Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning

Author

Huang, Wenhui, Braghin, Francesco, and Wang, Zhuo

Subjects
Computer Science - Robotics
Abstract

With the implementation of reinforcement learning (RL) algorithms, current state-of-art autonomous vehicle technology have the potential to get closer to full automation. However, most of the applications have been limited to game domains or discrete action space which are far from the real world driving. Moreover, it is very tough to tune the parameters of reward mechanism since the driving styles vary a lot among the different users. For instance, an aggressive driver may prefer driving with high acceleration whereas some conservative drivers prefer a safer driving style. Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions. We use gradient inverse reinforcement learning (GIRL) algorithm to recover the unknown reward function and employ REINFORCE as well as Deep Deterministic Policy Gradient algorithm (DDPG) to learn the optimal policy. The performance of our method is evaluated in simulation-based scenario and the results demonstrate that the agent performs human like driving and even better in some aspects after training., Comment: 7 pages, 11 figures, conference

Published
2020

317. Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Author

Ning, Caibo, Fan, Linyun, Jin, Meng, Wang, Wenji, Hu, Zhiqiang, Cai, Yimin, Chen, Liangkai, Lu, Zequn, Zhang, Ming, Chen, Can, Li, Yanmin, Zhang, Fuwei, Wang, Wenzhuo, Liu, Yizhuo, Chen, Shuoni, Jiang, Yuan, He, Chunyi, Wang, Zhuo, Chen, Xu, Li, Hanting, Li, Gaoyuan, Ma, Qianying, Geng, Hui, Tian, Wen, Zhang, Heng, Liu, Bo, Xia, Qing, Yang, Xiaojun, Liu, Zhongchun, Li, Bin, Zhu, Ying, Li, Xiangpan, Zhang, Shaoting, Tian, Jianbo, and Miao, Xiaoping

Published
2023
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333. Effect of Fungal Fermentation on the Structure, Functional Properties and Antioxidant Activity of Soluble Dietary Fiber from Sargassum hemiphyllum

Author

CAI Hongying, LI Rui, WANG Zhuo, CHEN Jianping, LIU Xiaofei, SONG Bingbing, JIA Xuejing, ZHONG Saiyi

Subjects
soluble dietary fiber, fermentation method, aspergillus oryzae, aspergillus niger, scagassum hemiphyllum, Food processing and manufacture, TP368-456
Abstract

Soluble dietary fiber (SDF) was extracted from Sargassum hemiphyllum fermented with Aspergillus niger or Aspergillus oryzae in order to investigate the effect of fungal fermentation on the yield, structural improvement, functional properties and antioxidant activity of SDF from S. hemiphyllum. The results showed that SDF prepared by fermentation with the two strains had advantages over each other. Compared with the untreated control group, A. oryzae fermentation increased the yield of SDF by 3.625 times, and the obtained SDF, which was bright in color and loose in structure, had higher purity and lower molecular mass (6.3 kDa), and consequently higher water-holding capacity (WHC) and sodium cholate adsorption capacity. A. niger fermentation increased the yield of SDF by 3.375 times, and the molecular mass of the obtained SDF was 39.4 kDa, which had a porous structure and significantly improved water-holding capacity, cholesterol and sodium cholate adsorption capacity. In vitro antioxidant experiments showed that fermentation improved the hydroxyl radical scavenging capacity of SDF, and decreased its scavenging capacity against 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical, 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cation radical and oxygen radical. In summary, this study can provide a reference for the preparation of SDF by fungal fermentation and its development and application in functional foods.

Published
2023
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338. Recommendations for improved tropical cyclone formation and position probabilistic Forecast products

Author

Dunion, Jason P., Davis, Chris, Titley, Helen, Greatrex, Helen, Yamaguchi, Munehiko, Methven, John, Ashrit, Raghavendra, Wang, Zhuo, Yu, Hui, Fontan, Anne-Claire, Brammer, Alan, Kucas, Matthew, Ford, Matthew, Papin, Philippe, Prates, Fernando, Mooney, Carla, Kruczkiewicz, Andrew, Chakraborty, Paromita, Burton, Andrew, DeMaria, Mark, Torn, Ryan, and Vigh, Jonathan L.

Published
2023
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