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2021 IEEE East-West Design and Test Symposium, EWDTS 2021

作者： Gokcul, Furkan Hahanov, Vladimir Gugul, Gul Nihal Olmez, Burak Behlul Kuru, Mustafa Kuveyt Turk Participation Bank Research and Development Center Department of corporate architecture Kocaeli Turkey Computer Engineering Faculty Kharkov National University of Radio Electronics Kharkov Ukraine Faculty of Technology Selcuk University Department of Computer Engineering Konya Turkey Akin Robotics Tatlicak Konya Karatay42030 Turkey Payolog LLC Primefor Technology J. S. C Konya Turkey

ISBN: (纸本)9781665445030

Data centers are estimated to be responsible for 13% of energy consumed in world by 2030. Rapid increase in data storage and server performance requirements are the main causes of increase in energy consumption especially for hyperscale data centers such as in financial and banking industry. However small-scale data centers accounted for 70% of total consumption in 2020, which indicates the energy saving potential due to the old systems and un-specially designed buildings. Unlike standard buildings, data centers have high indoor heat gain, therefore high thermal conductivity in cold climates, low conductivity in hot climates is *** systems analysis, air flow analysis, capacity management and network software have been developed to achieve energy savings in data centers in literature. However, a software that calculates the energy demand by taking into account the building details, location and renewable energy systems installed in the data center is not found. In this study a software is developed to calculate final energy demand of datacenters by taking into account the location, building details, IT devices and renewable energy system. For the verification of developed software, hourly energy consumption data of a data center of a private bank is used. © 2021 IEEE.

关键词： Energy efficiency

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A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors

arXiv

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arXiv 2024年

作者： Wu, Keshu Li, Pei Cheng, Yang Parker, Steven T. Ran, Bin Noyce, David A. Ye, Xinyue Department of Civil and Environmental Engineering The University of Wisconsin-Madison MadisonWI53706 United States Department of Landscape Architecture and Urban Planning The Zachry Department of Civil and Environmental Engineering Texas A&M University College StationTX77840 United States Center for Geospatial Sciences Applications and Technology The Department of Landscape Architecture and Urban Planning Department of Computer Science and Engineering Texas A&M University College StationTX77840 United States

Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, communications, and more remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the proposed DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT’s scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Furthermore, its ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the reliability and safety of transportation systems. © 2024, CC BY.

关键词： Vehicle to infrastructure

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StarTroper, a film trope rating optimizer using machine learning and evolutionary algorithms

StarTroper, a film trope rating optimizer using machine lear...

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作者： García-Ortega, Rubén Héctor García-Sánchez, Pablo Merelo-Guervós, Juan Julián Development Department Badger Maps Granada Spain Department of Computer Science and Engineering University of Cádiz Cádiz Spain Department of Computer Architecture and Technology University of Granada Granada Spain

Designing a story is widely considered a crafty yet critical task that requires deep specific human knowledge in order to reach a minimum quality and originality. This includes designing at a high level different elements of the film;these high-level elements are called tropes when they become patterns. The present paper proposes and evaluates a methodology to automatically synthesize sets of tropes in a way that they maximize the potential rating of a film that conforms to them. We use machine learning to create a surrogate model that maps film ratings from tropes, trained with the data extracted and processed from huge film databases in Internet, and then we use a genetic algorithm that uses that surrogate model as evaluator to optimize the combination of tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and their distributions in existing films, the performance of the models and the quality of the sets of tropes synthesized. The results of this proof of concept show that the methodology works and is able to build sets of tropes that maximize the rating and that these sets are genuine. The work has revealed that the methodology and tools developed are directly suitable for assisting in the plots generation as an authoring tool and, ultimately, for supporting the automatic generation of stories, for example, in massively populated videogames. © 2020 John Wiley & Sons, Ltd

关键词： Genetic algorithms

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Forty Years of Event Graphs in Research and Education 23

Forty Years of Event Graphs in Research and Education

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Proceedings of the Winter Simulation Conference

作者： Murat M. Gunal Yahya Ismail Osais Lee Schruben Gerd Wagner Enver Yücesan Faculty of Engineering and Architecture Fenerbahce University Istanbul Türkiye Computer Engineering Department Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Industrial Engineering and Operations Research University of California Berkeley USA Department of Informatics Brandenburg University of Technology Cottbus Germany Technology and Operations Management Area INSEAD Singapore

ISBN: (纸本)9798350369663

Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in computer Science is debated.

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Sources of optical radiation based on ZnTe/ZnSe/ZnS heterostructures

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Materials Today: Proceedings 2022年 62卷 5763-5766页

作者： Slyotov, Mykhailo Mazur, Tetiana Prokopiv, Volodymyr Slyotov, Oleksii Mazur, Myroslav Department of Optics Physical Publishing and Printing/Technical and Computer Science Institute Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. Chernivtsi 58012 Ukraine Department of General and Applied Physics/Institute of Architecture Construction and Power Engineering Ivano-Frankisvsk National Technical University of Oil and Gas 15 Karpatska Str. Ivano-Frankivsk 76019 Ukraine Department of Solid State Physics and Chemistry/Faculty of Physics and Technology Vasyl Stefanyk Precarpathian National University 57 Shevchenko Str. Ivano-Frankivsk 76019 Ukraine

The heterostructures of zinc chalcogenides β-ZnTe/ZnSe/ZnS were obtained by isovalent substitution. The regimes for obtaining materials that ensured the stability of the cubic lattice of heterolayers of zinc chalcogenides β-ZnSe and β-ZnS were determined. The isovalent elements Se and S determined the solid-phase substitution and determined the formation of heterolayers in the lattice of the surface of the base crystal. The optical transmission, reflection, and photoluminescence spectra of the heterolayers are studied. The analysis of the results of studies of the energy structure of the obtained heterolayers was carried out, which indicates the correspondence of these heterolayers to the compounds of zinc selenide and zinc sulfide obtained by classical technological processes. They are also confirmed by the results of studies of the optical transmission of β-ZnSe and β-ZnS films specially obtained on transparent substrates under the same growing conditions as compared to isovalent substitution. © 2022

关键词： Light transmission

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Comparison of Two Microcontroller Boards for On-Device Model Training in a Keyword Spotting Task

Comparison of Two Microcontroller Boards for On-Device Model...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Nil Llisterri Gimé nez Felix Freitag JunKyu Lee Hans Vandierendonck Department of Computer Architecture. Technical University of Catalunya Barcelona Spain The Institute of Electronics Communications and Information Technology. Queen&#x0027 s University Belfast. UK

Machine learning applications on resource-constrained devices such as microcontroller units often use models trained externally on more powerful devices. This approach, however, limits a later adaptation of the machine learning model in the device to changing data. Differently, on-device training allows the model to be updated for new datasets, but the training process needs to take into account the resource limitations of the device. This paper compares on-device training performance for a keyword spotting task using two popular microcontroller boards, Arduino Nano 33 BLE Sense and Arduino Portenta H7, in terms of inference accuracy, training latency, and current consumption. We use feedforward neural networks having a single hidden layer for models. The inference accuracy has been significantly improved using the Portenta H7 board by employing more neurons fitted to its memory budget, compared to the Nano board. With a neural network having 25 neurons for a hidden layer, the 5.0 x inference and 4.2 x training speedups are achieved using the Arduino Portenta H7 board, compared to the Arduino Nano 33 BLE Sense. While the memory of the Arduino Nano 33 BLE Sense is capable to train a neural network for the keyword spotting task, the Arduino Portenta H7 gives new possibilities for exploring more complex models for more complex problems thanks to a larger memory budget and adapting a model to new data in lower latency.

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Inventory Management System based on IoT and Microservices architecture Design

Inventory Management System based on IoT and Microservices A...

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International Electrical Engineering Congress (iEECON)

作者： Paisan Sithiyopasakul Chavinkorn Piyatananugoon Vasutorn Chaowalittawin Woranidtha Krungseanmuang Posathip Sathaporn Isoon Kanjanasurat Boonchana Purahong Tunjai Archevapanich Attasit Lasakul Computer Engineering Department King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand College of Computing Khon Kean University KhonKean Thailand Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand

This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second.

关键词： Productivity Industries Protocols Microservice architectures QR codes Inventory management Manufacturing

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil Engineering 2025年 1-16页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T. C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah UAE Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency.

关键词： Artificial intelligence Construction schedule prediction Gradient boosting machine (GBM) Interpretability in machine learning Project performance analytics

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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

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Journal of Thermal Science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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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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