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Sustainable energy planning and integration for district heating systems: A case study in Nineveh Province, Iraq

Journal of Building engineering 2024年 91卷

作者： Qusay Hassan Ali Khudhair Al-Jiboory Muna Al-Razgan Patrik Viktor Nouby M. Ghazaly Emad Mahrous Awwad Sameer Algburi Bashar Mahmood Ali I.B. Sapaev Imad Ibrahim Dawood Marek Jaszczur Aws Zuhair Sameen Maha Barakat Department of Mechanical Engineering University of Diyala Diyala Iraq Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 11495 Saudi Arabia Keleti Károly Faculty of Business and Management Óbuda University H-1084 Budapest Hungary Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Mechanical Engineering Faculty of Engineering South Valley University Egypt Department of Electrical Engineering College of Engineering King Saud University Riyadh 11421 Saudi Arabia College of Engineering Al-Kitab University Kirkuk 36015 Iraq Department of Construction Engineering and Project Management Al-Noor University College Nineveh Iraq Head of the Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Mazaya University College Dhi Qar Iraq Faculty of Energy and Fuels AGH University of Science and Technology Krakow Poland College of Medical Techniques Al-Farahidi University Baghdad Iraq Business Administration Department Al-Turath University College Baghdad Iraq

The study explores the feasibility and benefits of implementing solar-based district heating systems in Nineveh Province of Iraq, prompted by an anticipated 10 % rise in heating demand by 2030. Geographic analysis reveals that over 74 % of the region is well-suited for solar district heating, emphasizing the province considerable potential for solar energy utilization. Employing a methodological approach, we devised five scenarios varying in sustainability and efficiency for the district heating system. The results highlight a significant potential for reducing the Total Primary Energy Supply (TPE) by 23.5 % and CO 2 emissions by 39.96 % under the optimal Scenario 5, in contrast to a Business as Usual (BAU) scenario. A sensitivity analysis further examines the influence of different parameters on system performance, reinforcing the importance of adaptive and sustainable energy planning. The main contribution of this study lies in its comprehensive evaluation of solar-based district heating in the province, offering a viable strategy for reducing energy consumption and greenhouse gas emissions. This study not only addresses the urgent need for efficient energy solutions in response to rising heating demands but also sets a precedent for integrating renewable energy systems in urban planning. The novelty of this work resides in its detailed analysis of the province solar heating potential and the practical implications of its findings, which are crucial for policymakers and urban planners committed to advancing sustainable energy initiatives.

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Inequivalent multipartite coherence classes and two operational coherence monotones

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Physical Review A 2019年第4期99卷 042306-042306页

作者： Yu Luo Yongming Li Min-Hsiu Hsieh College of Computer Science Shaanxi Normal University Xi'an 710062 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

Quantum coherence has received significant attention in recent years, but the structure of multipartite coherent states is unclear. In this paper, we generalize important results in multipartite entanglement theory to their counterparts in quantum coherence theory. First, we give a necessary and sufficient condition for when two pure multipartite states are equivalent under local incoherent operations assisted by classical communications (LICC), i.e., two states can be deterministically transformed to each other under LICC operations. Next, we investigate and give the conditions in which such a transformation succeeds only stochastically. Different from the entanglement case for two-qubit states, we find that the stochastic LICC (sLICC) equivalence classes are infinite. Moreover, it is possible that there are some classes of states in multipartite entanglement that can convert into each other, whereas they cannot convert into each other in multipartite coherence. In order to show the difference among sLICC classes, we introduce two coherence monotones: accessible coherence and source coherence, following the logistics given in [Phys. Rev. Lett. 115, 150502 (2015)]. These coherence monotones have a straightforward operational interpretation, namely, the accessible coherence characterizes the proficiency of a state to generate other states via quantum incoherent operations, whereas the source coherence characterizes the set of states that can be reached via quantum incoherent operations acting on the given state of interest.

关键词： Quantum coherence & coherence measures Quantum entanglement Quantum protocols Resource theories

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Medical Data Acquisition Platform Based on Synthetic Intelligence 15th

Medical Data Acquisition Platform Based on Synthetic Intelli...

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15th Web Information Systems and Applications Conference, WISA 2018

作者： Gu, Zhao Liu, Yongjun Zhang, Mingxin College of Software Engineering Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China Department of Computer Science and Engineering Changshu Institute of Technology Changshu China

ISBN: (纸本)9783030029333

Nowadays, most medical device manufacturers are still using on-device data integrating and transmitting. However the real hospital situation is complex. Medical devices have different interfaces. Some of them are even outdated. This situation makes medical data can’t be automatically exported. Data can only be copied by hospital staff manually. In order to solve this data extraction problem caused by interface incompatibility and device version incompatibility, we implemented this medical data acquisition platform base on synthetic information. It uses OCR (Optical Character Recognition) technology to collect intuitive data directly from the screen interface. This platform also includes an embedded voice recognition module implemented on Raspberry Pi. The voice recognition system is used to solve the time consuming and inconvenience problem caused by manually data recording through transforming the voice signal into texts and instructions. Finally, we upload medical data to the server through the socket for effective data integration. The system hardware structure is simple;cost is under control. It has good stability and can be used in a wide range of applications. © 2018, Springer Nature Switzerland AG.

关键词： Data acquisition

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Spectral analysis network for deep representation learning and image clustering

arXiv

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arXiv 2020年

作者： Wang, Jinghua Hilton, Adrian Jiang, Jianmin Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network and require a large amount of labeled data in the training procedure. However, it is time consuming or even impossible to obtain the label information in some tasks due to cost limitation. Thus, it is necessary to develop unsupervised deep representation learning techniques. This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations. Compared with the existing spectral analysis methods, the proposed network structure has at least three advantages. Firstly, it can identify the local similarities among images in patch level and thus more robust against occlusion. Secondly, through multiple consecutive spectral analysis procedures, the proposed network can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples. Thirdly, it can elegantly integrate different spectral analysis procedures, so that each spectral analysis procedure can have their individual strengths in dealing with different data sample distributions. Extensive experimental results show the effectiveness of the proposed methods on various image clustering tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrum analysis

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A Canvas Based Multi-Program Assessment System for ABET CAC and EAC Accreditations

A Canvas Based Multi-Program Assessment System for ABET CAC ...

引用

Frontiers in Education (FIE) Conference

作者： Wen-Li Wang Mei-Huei Tang Computer Science and Software Engineering Penn State Erie The Behrend College Erie PA USA Computer Information Science Gannon University Erie PA USA

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This is an Innovative Practice Full Paper. This paper introduces a multi-program assessment system for a department offering multiple majors to seek accreditation from ABET CAC and EAC. The Canvas LMS is our mandated tool for faculty to set up course rubrics, assign grades, and communicate with students. However, it falls short on solving several of our data issues, including data retrieval, data separation, data sharing, data integration, and data security. The Canvas LMS provides no data download for individual rubric criteria of an instrument, hindering analysis to a more granular level. There is also no information to identify a student's major, making data separation for different academic programs difficult. Faculty are reluctant to share student data on Canvas for integration, worrying about a violation to FERPA. Moreover, data sharing and data integration can increase the risk of data security. Our assessment system is built atop of Canvas and is a service oriented web application for faculty to assess student performance and generate instrument reports. The reports reveal only computed statistics for data sharing, integration and security purposes. The provided web services can interact with another system to distinguish student majors and consolidate instruments whose rubric criteria cover the same performance indicators (PIs). The system is a safe and sound methodology to conquer our data problems and can facilitate both formative and summative analyses for the program outcomes evaluation.

关键词： Instruments Accreditation Web services Data integration Data security Data collection Systems architecture

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Some characterizations of coherent rings in terms of strongly FP-injective modules

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Communications in Algebra 2020年第7期48卷 2857-2871页

作者： Chen, Mingzhao Kim, Hwankoo Wang, Fanggui Zhang, Xiaolei College of Mathematics and Information Science Leshan Normal University Leshan China Division of Computer & Information Engineering Hoseo University Asan South Korea College of Mathematics and Software Science Sichuan Normal University Chengdu China Department of Basic Courses Chengdu Aeronautic Polytechnic Chengdu China

An R-module M is called strongly FP-injective if (Formula presented.) for any finitely presented R-module P and any i > 0. Denoted by (Formula presented.) the class of all strongly FP-injective R-modules and by (Formula presented.) the left orthogonal class with respect to (Formula presented.) Comparing with some classical results of Noetherian rings, we show that a ring is coherent if and only if (Formula presented.) is closed under pure submodules;if and only if any finitely generated module in (Formula presented.) is finitely presented;if and only if R is (Formula presented.) -coherent and (Formula presented.) is closed under direct sums;if and only if (Formula presented.) for any nonnegative integer n, any finitely presented left R-module X, any R-T-bimodule M and any injective right T-module E. In addition, we show that a module is injective if and only if it is a pure (Formula presented.) -periodic module, where (Formula presented.) denotes the class of all injective modules. © 2020, © 2020 Taylor & Francis Group, LLC.

关键词： (Strongly) FP-injective module coherent ring pure submodule von Neumann regular ring

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Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach

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Scientific reports 2025年第1期15卷 19349页

作者： Sunawar Khan Tehseen Mazhar Tariq Shahzad Tariq Ali Muhammad Ayaz Yazeed Yasin Ghadi El-Hadi M Aggoune Habib Hamam School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan. School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan. tehseenmazhar719@***. Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan. tehseenmazhar719@***. Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan. Artificial Intelligence and Sensing Technologies (AIST) Research Centre University of Tabuk Tabuk 71491 Saudi Arabia. teshaq@ut.edu.sa. Faculty of Computers and Information Technology University of Tabuk Tabuk 71491 Saudi Arabia. teshaq@ut.edu.sa. Artificial Intelligence and Sensing Technologies (AIST) Research Centre University of Tabuk Tabuk 71491 Saudi Arabia. Faculty of Computers and Information Technology University of Tabuk Tabuk 71491 Saudi Arabia. Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates. School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa. Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada. International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville 1989 BP Gabon. College of Computer Science and Eng. (Invited Prof) University of Ha'il Ha'il 55476 Saudi Arabia.

This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocation depend on accurate load forecasts. The study evaluates model performance using year-long load data from seasonal, daily, and hourly fluctuations. Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. The QPSO-optimized RNN outperformed traditional RNN and ARIMA models with the lowest MAE of 15.2, MSE of 520.15, and RMSE of 22.8. Comparative investigation shows the QPSO-RNN's capacity to capture complicated load data patterns, especially during peak demand. This study shows that hybrid optimization can improve forecasting accuracy, making it a powerful tool for energy management in dynamic contexts.

关键词： Educational buildings Energy management Load demand forecasting Seasonal adjustment Time series forecasting

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Multi-Kernel Fusion for RBF Neural Networks

arXiv

引用

arXiv 2020年

作者： Atif, Syed Muhammad Khan, Shujaat Naseem, Imran Togneri, Roberto Bennamoun, Mohammed Graduate School of Science and Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan Daejeon34141 Korea Republic of College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia School of Computer Science and Software Engineering University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia

A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using only a single kernel. In existing multi-kernel RBF algorithms, multi-kernel is formed by the convex combination of the base/primary kernels. In this paper, we propose a novel multi-kernel RBFNN in which every base kernel has its own (local) weight. This novel flexibility in the network provides better performance such as faster convergence rate, better local minima and resilience against stucking in poor local minima. These performance gains are achieved at a competitive computational complexity compared to the contemporary multi-kernel RBF algorithms. The proposed algorithm is thoroughly analysed for performance gain using mathematical and graphical illustrations and also evaluated on three different types of problems namely: (i) pattern classification, (ii) system identification and (iii) function approximation. Empirical results clearly show the superiority of the proposed algorithm compared to the existing state-of-the-art multi-kernel approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Radial basis function networks

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Towards a New Metric for Watermarked Image Assessment

Towards a New Metric for Watermarked Image Assessment

引用

International Conference on Electrical, Communication, computer, Power, and Control engineering (ICECCPCE)

作者： Dujan B. Taha Taha B. Taha Software Engineering Department College of Computer Science and Mathematics University of Mosul Mosul Iraq School of Computer and Communication Engineering University Malaysia Perlis Perlis Malaysia

ISBN: (数字)9781728107813

ISBN: (纸本)9781728107820

To evaluate the watermarking algorithm performance, one of the basic factors used for this purpose is the watermark imperceptibility, which reflects the degradation of watermarked image in compare to the original image. In this paper, the performance of the most commonly used image evaluation metrics, peak signal to noise ratio (PSNR), and structure similarity index (SSIM) are tested in high textured and low textured images, in addition a new metric which considers the texture mapping in assessing the watermarked images has been proposed. Experimental results show better estimation for the watermarked image quality when the texture masking is involved in image assessment in compare to traditional metrics.

关键词： Watermarking PSNR Control engineering Image quality Sensitivity Indexes

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