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Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models

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Neural Computing and Applications 2024年第23期36卷 14119-14150页

作者： Bhagat, Suraj Kumar Tiyasha, Tiyasha Shather, A.H. Jamei, Mehdi Kumar, Adarsh Al-Khafaji, Zainab Goliatt, Leonardo Shafik, Shafik S. Alawi, Omer A. Yaseen, Zaher Mundher Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Engineering Technology Al Kitab University Altun Kopru Kirkuk00964 Iraq Faculty of Civil Engineering and Architecture Shahid Chamran University of Ahvaz Ahvaz Iran Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St PetersPE Canada Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Hillah Babylon51001 Iraq Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora Brazil Experimental Nuclear Radiation Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Johor Bahru Skudai81310 Malaysia Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Dept of Environmental Science Gitam School of Science GITAM University Visakhapatnam India

Wind speed (WS) has played a vital role in local urban and sub-urban weather, agriculture, and ecosystem. Several meteorological parameters are influencing WS such as relative humidity (at 2 m, %), surface pressure (kPa), maximum temperature (at 2 m, °C), minimum temperature (at 2 m, °C), average temperature (at 2 m, °C), and all sky insolation incident on a horizontal surface (kW-h/m2/day). The current research was conducted to predict WS at different locations at Vietnam using the feasibility of computer aid models (i.e., multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost) and random forest generator (Ranger)). Pearson correlation (PC) was investigated to select the high significant predictors to predict the WS at 10 m high. All inputs (maximum number, 6) are chosen by the PC approach for PhuongNinh, DaNang, and HaNoi;and for minimum number of inputs i.e four, are selected for PhuongHung, CanTho, and SaPa city;that exhibit the relationship with WS, citywise. The sequence selection of input parameters differed in each station as per the PC analysis. Based on the statistical evaluation and graphical presentation, MARS model attained the best prediction results, followed by XGBoost and Ranger. MARS predictive model remains at the top performance among others based on 95% confidence interval. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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A Simulation-Assisted Teaching Practice: Teaching AC Variable Speed Systems Using Simulation Models Library

A Simulation-Assisted Teaching Practice: Teaching AC Variabl...

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2021第五届教育与创新国际会议

作者： Yang Qian Gao Jinzhu School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Department of Computer Science University of the Pacific

Different modulation modes of PWM technology and inverters can be selected for different types of the motors, a variety of AC variable frequency variable speed system structures can also be derived based on the same control principle. So it brings a great challenge to teaching AC variable speed systems. We have identified four factors that hinder students' learning in this topic: 1) the abstraction of the mathematical logic behind the control theory;2) the complexity of system structures;3) the diversity of implementation methods;4) lack of motivation to explore more engineering applications due to boring textbook contents. To facilitate and inspire student learning, we developed an innovative way to teach the AC Variable Speed Systems course using the simulation models library. The library was implemented based on MATLAB/Simulink to assist students in studying five different types of AC variable speed systems. We assessed the effectiveness of our new teaching practice by evaluating students' cognitive and affective behavior under both the traditional lecture-based teaching environment and the new simulation-assisted teaching environments. The practice assessment tools that take into accounts the in-class performance(attendance accounts for 10% and the experimental reports account for 20%), the lab assignments computer operation score(30%) includes software operation(10%) and system constitution and design(20%), and the final exam(40%) are designed to assess the students' understanding of AC variable speed systems. The student group learning under the simulation-assisted teaching environment delivered a superior performance in all measures including the noticeable improved scores on the final exam. We also developed a survey to evaluate the students' affective behavior by measuring their motivation for learning and their perceptions of effectiveness of practice. Ninety percent of students reported that the new teaching practice using the simulation models library is

关键词： AC Variable Speed Systems Simulation-assisted Teaching Methodology MATLAB/GUI Simulation Models Library Vector Control Practice Training

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A Comprehensive Review on Big Data Challenges 2

A Comprehensive Review on Big Data Challenges

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Bharany, Salil Taleb, Nasser Sadiq, M. Tariq Kanwal, Nayab Abdelhakim, Mohamed Ghazal, Taher M. Pradhan, Manas Rehman, Ateeq Ur Tech Amritsar India CET Department United Kingdom Canadian University Dubai Faculty of Management Dubai United Arab Emirates University of Brighton School of Architecture Engineering United Kingdom NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan University of Dubai College of Engineering and IT United Arab Emirates Skyline University College School of Information Technology Sharjah United Arab Emirates Department of Electrical Engineering GCU Lahore Pakistan

ISBN: (纸本)9798350335644

Increased hardware capability has allowed many businesses to store massive amounts of data in the previous few years. Streams of data may go on forever, so they are used in so many different applications. Analyzing and querying these streams is challenging for a stream of dynamic information. The term "big data"refers to a collection of methods and tools that must be combined in novel ways to unearth the vast amounts of previously unseen information buried within vast, diverse, and intricate databases. There are several ways to obtain, organize and analyze data in the big data world. In order to deal with streaming data, efficient data mining procedures must be in place. In this article, we examine a variety of Big Data-related concerns and challenges. This paper introduces the features, and challenges of managing big data. © 2023 IEEE.

关键词： Big data

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The New Concept of Guaranteeing Confidence in the E-Voting System

The New Concept of Guaranteeing Confidence in the E-Voting S...

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2020 IEEE International Conference on Problems of Infocommunications Science and technology, PIC S and T 2020

作者： Khlaponin, Yurii Vyshniakov, Volodymyr Prygara, Mykhailo Poltorak, Vadym Kyiv National University of Construction and Architecture Cybersecurity and Computer Engineering Department Kyiv Ukraine National University Machine Industry Technology Department Uzhhorod Uzhhorod Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Technical Systems Automation and Control Kyiv Ukraine

ISBN: (纸本)9781728191775

There is no doubt about the urgency of ensuring trust in e-voting systems since elections without trust cannot be acceptable in a democratic society. To solve this problem, it was proposed to use blockchain technology, however, in voting systems this leads to significant delays in the execution of transactions, and the software solution is accessible only to a narrow circle of specialists, which clearly does not help to show confidence on the part of voters. This paper proposes a different approach, which is based on the widely available verification of all means and processes that may cause distrust in the accuracy of the vote count or in ensuring their confidentiality. We offer a solution to the problem of transparency of all hardware and software that cause distrust. Voting systems that use this approach are implemented in three leading Ukrainian universities for student elections to self-government bodies. The conceptual model of the e-voting system for state elections on the example of Ukraine is presented. Links to Internet resources are provided where you can not only get acquainted with the principles of building the system, but also conduct an experimental vote in Ukrainian, English or Russian. You can also copy and test e-voting server software on your computers. © 2020 IEEE.

关键词： Transparency

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BEKG: A Built Environment Knowledge Graph

arXiv

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arXiv 2022年

作者： Yang, Xiaojun Zhong, Haoyu Du, Penglin Zhou, Keyi Lai, Xingjin Wang, Zhengdong Lau, Yik Lun Song, Yangqiu Tang, Liyaning School of Information Engineering Guangdong University of Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Architecture and Built Environment The University of Newcastle NSW Australia

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://***/HKUST-KnowComp/BEKG. © 2022, CC BY.

关键词： Knowledge graph

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Meta-Learning Based Infrared Ship Object Detection Model for Generalization to Unknown Domains

SSRN

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SSRN 2023年

作者： Feng, Hui Tang, Wei Xu, Haixiang Jiang, Chengxin Ge, Shuzhi Sam He, Jianhua Key Laboratory of High-Performance Ship Technology of Ministry of Education Wuhan University of Technology Wuhan430063 China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore School of Computer Science and Electronic Engineering University of Essex CO4 3SQ United Kingdom

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploition of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its su

关键词： Object detection

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of computer Science & technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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Deep Learning Techniques for Overcoming Interoperability Challenges in Smart Grid Systems

Deep Learning Techniques for Overcoming Interoperability Cha...

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IoT, Communication and Automation technology (ICICAT), International Conference on

作者： M Devi Anjani Kumar Rai Avala Raji Reddy Gogineni Neelima Saif Obaid Senthilnathan Chidambaranathan Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Engineering & Applications GLA University Mathura Department of Mechanical Engineering CMR Technical Campus Hyderabad Telangana India Department of CSE GRIET Bachupally Hyderabad Telangana India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of Architecture and Design Virtusa Corporation New Jersey USA

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as; predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature; Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency.

关键词： Deep learning Renewable energy sources Energy consumption Protocols Predictive models Data models Smart grids computer security Interoperability Anomaly detection

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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International Conference on Information Management and technology (ICIMTech)

作者： Andi Pramono Satrio Arif Budiman Muchammad Farchan Andi Baso Mappaturi Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Information System Computer Science Faculty Brawijaya University Malang Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Architecture Department Faculty of Science and Technology Islamic State University of Maulana Malik Ibrahim Malang Indonesia

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development.

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DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

arXiv

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arXiv 2022年

作者： Zheng, Yu Gao, Chen Ding, Jingtao Yi, Lingling Jin, Depeng Li, Yong Wang, Meng Department of Electronic Engineering Tsinghua University Beijing China WeChat Technical Architecture Department Tencent Inc. Shenzhen China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://***/tsinghua-fib-lab/WTG-DVR. © 2022, CC BY.

关键词： Watches

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