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Demand response strategy for coordinated optimization of furnaces in discrete production processes of steelmaking-continuous casting 4

Demand response strategy for coordinated optimization of fur...

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4th international conference on Energy, Power and Electrical engineering, EPEE 2024

作者： Zhao, Yuqi Ji, Yu Ma, Huan Meng, Nan Chen, Qun Liaoning Technical University School of Electrical and Control Engineering Huludao China Tsinghua University Department of Engineering Mechanics Beijing China North China University of Technology Department of Engineering Mechanics Tsinghua University Beijing China

ISBN: (纸本)9798331518066

Unlocking the inherent potential for flexible adjustment in steel enterprises is crucial for achieving green transformation in high energy-consuming industries and meeting national 'carbon reduction' goals. However, traditional research has focused on reducing process wait times, often neglecting electricity costs, thereby limiting demand response capabilities. To address this, this paper proposes a mixed-integer linear programming (MILP) model for the steelmaking-continuous casting (SCC) section. the model uses the big M method to introduce 0-1 decision variables to determine the processing order of furnace batch and analyze their operation times and corresponding power consumption across different time-of-use (TOU) tariff. By integrating the TOU tariff mechanism, the model aims to minimize electricity costs and reduce wait times between processes. To demonstrate the superiority of this method, it is compared with a genetic algorithm (GA) optimization model. the results show that, compared to the GA, this method reduces job completion time by 6.6%, lowers electricity costs by 10.02%, and decreases electricity consumption during high-price periods by 57%, thereby effectively enhancing the grid's regulatory capacity. © 2024 IEEE.

关键词： Continuous casting

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NFT minting and owner verification in an NFT E-commerce website 4th

NFT minting and owner verification in an NFT E-commerce webs...

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4th Annual international conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Shashwat Pandey, Ravi Shankar Department of Computer Science and Engineering Birla Institute of Technology Mesra Patna Campus India

ISBN: (纸本)9781032426853

NFTs or non-fungible tokens are unique digital assets with identifiable characteristics. NFTs can take the form of digital art, GIFs, videos, collectibles, video game merchandise, music, and even tweets. Uniqueness of a digital asset encompasses more than just visual properties, but also factors such as item IDs and hash codes. this means that even though the two NFTs are visually indistinguishable, they have different IDs and codes. In the digital space, these elements are an integral part of an asset’s identity as they contain important data about the object’s history. the popularity of the Non-Fungible Token (NFT) has grown tremendously since 2020, becoming one of the most popular applications in the Fintech field. However there is still lack of platforms which provide all the functionalities regarding NFTs (including minting, buying and selling) at a single place. this research paper introduces an algorithm for creation of NFTs along with ensuring owner verification which can be used for minting the NFTs in an E-commerce website using the MOTOKO language for creation of canisters consisting the owner id for owner verification and the principal id which serves as a unique token id for the NFT. © 2024 the Author(s).

关键词： Blockchain

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Exploring Student Perspectives on Generative AI in Requirements engineering Education 4

Exploring Student Perspectives on Generative AI in Requireme...

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4th international conference on AI Research, ICAIR 2024

作者： Mellqvist, Nicklas Mozelius, Peter Mid Sweden University Östersund Sweden

ISBN: (纸本)9798331309466

the rapid development of generative AI (GenAI) technologies in recent years has enabled new opportunities as well as new challenges in higher education. While many studies in computer science have focused on GenAI in programming education, fewer have examined its possibilities and challenges in requirements engineering (RE). this study aims to explore the impact of GenAI on the pedagogical aspects of RE in higher education, focusing on the student perspective, to analyse how GenAI might influence learning experiences, knowledge acquisition, and skill development. the main research question to answer was: "What are the students’ perspectives of the integration of GenAI in the educational practices of requirements engineering?" An Action research strategy was employed, with one of the authors also serving as teacher in the investigated course. A mixed-methods approach was used to collect both qualitative and quantitative data from workshops and surveys. During the workshops, students used ChatGPT to generate and evaluate software requirements and compared these to manually crafted requirements. thematic analysis of the qualitative data captured students’ perspectives, while survey data identified trends and preferences. Findings show that while students generally had a positive experience with GenAI, valuing its efficiency and the quality of generated requirements, they also recognized the need for human oversight to maintain accuracy. the study highlights both opportunities and challenges of using GenAI in RE education. While GenAI increased learning engagement and helped with brainstorming, students faced difficulties in creating effective prompts and found it time-consuming to refine AI-generated requirements. A hybrid approach, combining AI-generated and manually created requirements, proved most effective by balancing AI's advantages with human insights. Further research is needed on how GenAI could be effectively integrated into computer science education. © Pro

关键词： Requirements engineering

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Experimental Study on the Characteristics of Photovoltaic Modules Under Liquid Immersion Conditions 4

Experimental Study on the Characteristics of Photovoltaic Mo...

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4th international conference on Energy engineering and Power Systems, EEPS 2024

作者： Li, Yanqi Fu, Cikun Li, Fei School of Engineering Shandong Yingcai University Shandong China School of Physics and Technology University of Jinan Shandong China

ISBN: (纸本)9798350366914

In order to understand how the underwater working conditions influenced the photovoltaic modules, an experimental model for testing the efficiency of underwater photovoltaic systems was established. the research was conducted to investigate how the efficiency of photovoltaic modules was affected by various factors such as the depth of the underwater environment and the wind speed on the water surface. through analysis, optimal installation conditions for the underwater photovoltaic system were identified. It was also revealed that the increase in photovoltaic system efficiency was due to the effect of water surface perturbations. the findings showed that, as the water depth increased, the module efficiency gradually improved and reached stability within the liquid height range of 9cm to 15cm. Simultaneously, as the wind speed on the water surface increased, the system efficiency exhibited a significant increase, indicating that the water waves formed by the ruffling state of the water surface reduced the impact of water surface reflection. this research provided crucial insights into the working conditions and variation patterns of photovoltaic modules potentially operating underwater, which holds significant importance for both theoretical research and engineering applications. © 2024 IEEE.

关键词： liquid immersion condition underwater photovoltaics volt-ampere characterization experiments wind speed perturbation

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Design of Signal Processing System for Pipeline Pig Tracking and Positioning Receiver Based on Orthogonal Digital Lock-in Amplification Principle 4

Design of Signal Processing System for Pipeline Pig Tracking...

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4th international conference on Electronic Information engineering and Computer Technology, EIECT 2024

作者： Zhang, Xitong Shi, Haotian Zhu, Yuanchen Zhang, Tao University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom School of Manufacturing Science and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798331528850

the current signal processing system of pipeline pig tracking and positioning receiver based on bandpass filter is difficult to ensure high reliability and sensitivity when receiving weak signals from transmitters in complex environments due to limitations of Q value. Meanwhile, the frequency characteristics and overall sensitivity of the signal processing stages within the system are fixed and not adjustable, limiting the system's ability to adapt to different operating modes under varying environmental conditions. this paper presents a signal processing system based on orthogonal digital lock-in amplification principle. the system employs operational amplifier AD821 with low-noise for preamplification, specialized filter chips LTC10696 and LTC1068200 with adjustable frequency characteristics for low-pass filtering and band-pass filtering, then STM32F103RBT6 microcontroller is utilized to perform the A/D conversion and orthogonal digital lockin amplification (DLIA) processing. the system can reliably receive the 23 Hz sinusoidal signal from the transmitter with ultrahigh sensitivity, and its frequency characteristic and receiving sensitivity can be flexibly adjusted through programming. Experiment shows that the system can meet the diversified demands for detection sensitivity under different environmental conditions, with effective receiving sensitivity (up to 40 m in the highest sensitivity setting) much higher than current systems (less than 20 m). © 2024 IEEE.

关键词： Transmitters

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Legendre pseudo-spectral method for solving the optimal starting pendulum of a rotating inverted pendulum 4

Legendre pseudo-spectral method for solving the optimal star...

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2023 4th international conference on Computer engineering and Intelligent Control, ICCEIC 2023

作者： Qiu, Yacheng Wang, Xinzui Cao, Fucheng Su, Shichen Jihua Laboratory Jihua Medical Institute Foshan China South China Normal University College of Semiconductor Science and Technology Foshan China Chinese Academy of Sciences Suzhou Institute of Biomedical Engineering and Technology Suzhou China

ISBN: (纸本)9798350308877

the inverted pendulum is an underdriven system with multivariable, nonlinear and strong coupling. For the time-optimal swing problem of inverted pendulum, the mathematical model of inverted pendulum is established by generalized projection method. the optimal control problem (OCP) is transformed into a nonlinear programming problem (NLP) for solving by the pseudo-spectral method. And the steps of Legendre's pseudo-spectral method for transforming into a Bolza-type optimal control problem are summarized. the solution of this optimal control problem is realized by the MATLAB toolbox OptimTraj. the experimental results show that the pseudo-spectral method has better computational efficiency and speed, and has good results in solving complex optimal control problems. © 2023 IEEE.

关键词： Inverted pendulum

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Intelligent Models Equivalent to the Level of Radiologists for Multi-Label Lesion Detection in Chest X-Rays 4

Intelligent Models Equivalent to the Level of Radiologists f...

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4th international conference on Electronic Information engineering and Computer science, EIECS 2024

作者： Sun, Chenrui Chen, Chunyi Yao, Weixun Wu, Linghong College of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

In the field of radiology, the analysis of X-ray images serves as an essential tool for diagnosing thoracic diseases. With the rapid evolution of deep learning technologies, automated image analysis methods have been extensively integrated, aiming to significantly enhance the work efficiency and diagnostic precision of radiologists. Nonetheless, existing deep learning models still encounter a series of challenges when processing abnormal chest X-ray images, including the similarity of multiple categories of lesions, the diversity of scales, and the difficulty in detecting small target lesions. these issues severely hinder the advancement of model performance. To surmount these limitations, this study introduces an model for the detection and localization of multiple diseases from abnormal chest X-ray images. the model incorporates an advanced CNN-Swin Transformer module, which integrates global and local context information and employs a pixel-level self-attention mechanism through a moving window mechanism, effectively capturing multi-scale features of the images. Furthermore, the model introduces a channel and spatial permutation attention allocation mechanism, significantly enhancing the recognition of salient features in key areas and thereby improving detection accuracy. We evaluated the proposed model on the VinDr-CXR dataset, and compared with the state-of-the-art baselines, it achieved an increase of 2.3% in mean Average Precision (mAP), demonstrating the effectiveness of our approach in advancing the field of automated chest disease detection. © 2024 IEEE.

关键词： Radiology

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Review of Data Security Methods using Low-Earth Orbiters for High-Speed Encryption 4

Review of Data Security Methods using Low-Earth Orbiters for...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bakyt, Makhabbat Spada, Luigi La Moldamurat, Khuralay Kadirbek, Zhanserik Yermekov, Farabi L.N. Gumilyov Eurasian National University Department of Information Security Astana Kazakhstan School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom L.N. Gumilyov Eurasian National University Department of Space Engineering and Technology Astana Kazakhstan Radio broadcasting complex Llp 'KazMedia ortalygy' Astana Kazakhstan Scientific and Educational Technological Platform of the Kazakh National Agrarian Research University Astana Kazakhstan

ISBN: (纸本)9798331529635

this paper presents an overview of state-of-the-art data security techniques using low-orbit satellites (LOS) such as CubeSats for high-speed encryption. the paper compares various encryption techniques including quantum key distribution (QKD), chaotic mapping-based encryption algorithms, and genetic algorithm and random feature methods. Particular attention is paid to both the advantages and limitations of each approach and their potential applicability to LEO. the key challenges associated with the development and implementation of high-speed encryption on LEO are identified, such as limited resources, power requirements, and the need to ensure reliability in the space environment. Finally, future research directions are suggested, including the development of specialized encryption algorithms, optimization of hardware implementation, and integration of different techniques to achieve maximum efficiency and security. © 2024 IEEE.

关键词： Quantum cryptography

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Efficient Coupling of Energy Management Devices in Smart Sustainable Cities: A Case Study 4

Efficient Coupling of Energy Management Devices in Smart Sus...

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4th IEEE international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Kuhada, Ravindra B. Jha, Bablesh Kumar Pindoriya, Naran M. Ahuja, Abhinav Srivastava, Ankit K. Indian Institute of Technology Gandhinagar Dept. of Electrical Engineering Gujarat India Bhagalpur College of Engineering Dept. of Electrical Engineering Bhagalpur Sabour India Engineering Construction Gujarat India Indian Institute of Technology Jodhpur Dept. of Electrical Engineering Rajasthan India

ISBN: (纸本)9798350383997

Smart city has intensive and unmanageable electrical loads such as water treatment plant (WTP) and streetlighting. GIFT city - a smart city in India installed 10 kWh battery energy storage system (BESS) at each WTP and streetlighting feeder to address the energy demands locally. Additionally, 15 kWp solar PV has been installed on WTP feeder. this paper aims to reduce the dependency on grid, thereby lowering energy cost and optimally using each BESS, by shifting energy for combined load demand. this paper develops a day-ahead energy management system (EMS) based on linear programming for optimal BESS operation. the analysis is carried out considering solar generation data, electricity prices, WTP and streetlighting load data, grid parameters and BESS constraints. the proposed EMS algorithm reduces the cost of energy purchased from the grid and improves the BESS utilisation by switching its connection to another feeder in response to load demand variations. © 2024 IEEE.

关键词： Battery Energy Storage System Energy Management System Linear programming

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A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detection in Dermoscopy Images : Combining CNN and SEU-Net for Robust Melanoma Detection 4

A Hybrid CNN-SEU-Net Approach for Improved Melanoma Detectio...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Ahmed, Asaad Sun, Guangmin Li, Yu Saadeldin, Mohamed Beijing University of Technology School of Information Science and Technology Beijing China University College Dublin Dept. School of Computer Science Dublin Ireland

ISBN: (纸本)9798331534622

Melanoma is a highly aggressive form of skin cancer with high mortality rates if not diagnosed early. Accurate diagnosis poses significant challenges, particularly for nonspecialist healthcare providers, due to the complexity and variability of skin lesions. Current diagnostic methods often lack the precision needed for reliable early detection, highlighting the need for innovative solutions. this paper proposes a novel hybrid deep learning framework for melanoma detection that integrates SEU-Net and Convolutional Neural Networks (CNNs) to address these challenges. the framework begins with SEU-Net, an encoder-decoder segmentation model enhanced with skip connections and SE attention mechanisms, to generate a segmentation mask that identifies the region of interest (ROI) in dermoscopic images. the segmentation mask is multiplied with the original image to isolate the lesion area, creating a segmented image that minimizes irrelevant background information. this segmented image is then passed to a CNN, classifying the lesion as malignant or benign. the proposed approach was evaluated on the ISBI 2016 dataset, achieving an accuracy of 93.60\%, outperforming existing state-of-the-art methods. these results demonstrate the potential of the proposed framework to significantly improve the precision and reliability of melanoma diagnosis, enabling earlier detection and ultimately reducing mortality rates. © 2024 IEEE.

关键词： Convolutional neural networks

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