Academic literature on the topic 'Optimization of network'

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Journal articles on the topic "Optimization of network"

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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provi
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Han Zengfu, Kong Jiankun, Wang Zhiguo, et al. "AI-based network topology optimization system." ITU Journal on Future and Evolving Technologies 2, no. 4 (2021): 81–90. http://dx.doi.org/10.52953/yxtb5085.

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Existing network topology planning does not fully consider the increasing network traffic and problem of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments in network construction. The AI-based network topology optimization system introduced in this paper builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge deletion or addition based on traffic over nodes, and ensures network load balancing when node traffi
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Le, Hoang Nghia. "FTTH Network Optimization." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2014): 88–99. http://dx.doi.org/10.26636/jtit.2014.4.1051.

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Fiber To The Home (FTTH) is the most ambitious among optical technologies applied in the access segment of telecommunications networks. The main issues of deploying FTTH are the device price and the installation cost. Whilst the costs of optical devices are gradually decreasing, the cost of optical cable installation remains challenging. In this paper, the problem of optimization that has practical application for FTTH networks is presented. Because the problem is Non-deterministic polynomial-time hard (NP-hard), an approximation algorithm to solve it is proposed. The author has developed the
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S V, Gumaste, Kharat M U, and V. M Thakare. "Network Optimization (Mobile Backbone) - MILP Approach." International Journal of Scientific Engineering and Research 1, no. 1 (2013): 84–88. https://doi.org/10.70729/1130921.

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Ganie, Aadil Gani. "Private network optimization." Multidiszciplináris tudományok 11, no. 4 (2021): 248–54. http://dx.doi.org/10.35925/j.multi.2021.4.29.

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The main challenging task in laying a network is not to define its topology but to calculate its cost-benefit analysis, which is a research-oriented work. In any network, be it intra or internetwork, the main focus is always on optimizing cost and bandwidth. PSWAN is a medium-sized network at the Directorate of information technology India; this networking project provides internet services to many governmental and private offices. Optimization was required as the number of devices connected to this network was more. Two parameters that were optimized are 1. Bandwidth, and 2. Cost. Cost optimi
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Todd, Brody, Abiose Ibigbami, and John Doucette. "Survivable Network Design and Optimization with Network Families." Journal of Computer Networks and Communications 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/940130.

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In modeling communication networks for simulation of survivability schemes, one goal is often to implement these schemes across varying degrees of nodal connectivity to get unbiased performance results. Abstractions of real networks, simple random networks, and families of networks are the most common categories of these sample networks. This paper looks at how using the network family concept provides a solid unbiased foundation to compare different network protection models. The network family provides an advantage over random networks by requiring one solution per average nodal degree, as o
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Susanta Kumar Sahoo. "Optimizing Graph Theory Algorithms for Social Network Analysis." Communications on Applied Nonlinear Analysis 31, no. 4s (2024): 164–81. http://dx.doi.org/10.52783/cana.v31.834.

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Social network analysis (SNA) leverages graph theory to understand and visualize the complex relationships and structures within social networks. This research paper explores the optimization of graph theory algorithms tailored for SNA, focusing on efficiency improvements in handling large-scale networks. The study reviews key graph theory concepts, identifies common challenges in SNA, and evaluates various optimization techniques. Practical applications and case studies are presented to demonstrate the impact of these optimizations in real-world scenarios.
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Đokić, Dragana, and Vladimir Đokić. "High Performance Network Optimization." Journal of UUNT: Informatics and Computer Sciences 2, no. 1 (2025): 9. https://doi.org/10.62907/juuntics250201009d.

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The term “big data” was coined to describe the vast amounts of information science and technology data that have been generated on a large scale over time. Based on existing research, the current state of the problem was observed, i.e. “Poor internet speed, network downtime, constant traffic jams, energy consumption due to increasing internet connectivity, which increases management overhead costs, network unavailability”. Therefore, this research provides a brief overview of the list of bandwidth optimization models applied through previous research works, indicating the optimal algorithms th
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Yang, Jing. "Optimization of Logistics Distribution Network based on Ant Colony Optimization Neural Network Algorithm." Scalable Computing: Practice and Experience 25, no. 5 (2024): 3641–50. http://dx.doi.org/10.12694/scpe.v25i5.3203.

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In order to improve the timeliness of logistics distribution, based on the theory of road network smoothness and reliability, the author conducted a study on the optimization of urban logistics distribution and transportation networks based on smoothness and reliability. The concept of logistics distribution and transportation network smoothness and reliability was proposed, and a logistics distribution and transportation network optimization model was established. The solving process of ant colony algorithm was given, and finally, a comparative analysis of a case was conducted. The results sh
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Hinding, Nurdin, Amir Kamal Amir, Jusmawati Massalesse, et al. "Determination of the Grid-Shaped Transportation Network's Optimization Value via Graph Labelling." Journal of Advanced Research in Applied Sciences and Engineering Technology 57, no. 2 (2024): 96–105. https://doi.org/10.37934/araset.57.2.96105.

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In order to move people, commodities, and services in an efficient and effective manner, transportation networks are essential. To increase these networks' efficiency and save expenses, they must be optimised. In this research, we offer a unique method that makes use of graph labelling techniques to determine the optimization value of a transportation network. The goal is to give the network's constituent parts labels that accurately represent their potential for optimisations. We start by creating a graph model of the transportation network, with vertices standing in for important places and
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Dissertations / Theses on the topic "Optimization of network"

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Simmen, Martin Walter. "Neural network optimization." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/12942.

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Combinatorial optimization problems arise throughout science, industry, and commerce. The demonstration that analogue neural networks could, in principle, rapidly find near-optimal solutions to such problems - many of which appear computationally intractable - was important both for the novelty of the approach and because these networks are potentially implementable in parallel hardware. However, subsequent research, conducted largely on the travelling salesman problem, revealed problems regarding the original network's parameter sensitivity and tendency to give invalid states. Although this h
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Fan, Zihao, and Wei Zhao. "Network Coverage Optimization Strategy in Wireless Sensor Networks Based on Particle Swarm Optimization." Thesis, Högskolan i Gävle, Akademin för teknik och miljö, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9764.

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Wireless sensor network is an intelligent network system which has the self-monitoring functionality. It consists of many low-cost, low-power and small-sized sensor nodes that can communicate with each other to perform sensing and data processing. Acting as an important role in the system, network coverage usually has a huge effect on the system’s lifetime.In this thesis, particle swarm algorithm was used as a method to optimize the coverage in the coverage of wireless sensor network. A network coverage optimization strategy based on particle swarm optimization was proposed and MATLAB was used
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De, Bacco Caterina. "Decentralized network control, optimization and random walks on networks." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112164/document.

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Dans les dernières années, plusieurs problèmes ont été étudiés à l'interface entre la physique statistique et l'informatique. La raison étant que, souvent, ces problèmes peuvent être réinterprétés dans le langage de la physique des systèmes désordonnés, où un grand nombre de variables interagit à travers champs locales qui dépendent de l'état du quartier environnant. Parmi les nombreuses applications de l'optimisation combinatoire le routage optimal sur les réseaux de communication est l'objet de la première partie de la thèse. Nous allons exploiter la méthode de la cavité pour formuler des al
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Zhang, Xiaowen. "Resource Management and Optimization in Wireless Mesh Networks." FIU Digital Commons, 2009. http://digitalcommons.fiu.edu/etd/173.

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A wireless mesh network is a mesh network implemented over a wireless network system such as wireless LANs. Wireless Mesh Networks(WMNs) are promising for numerous applications such as broadband home networking, enterprise networking, transportation systems, health and medical systems, security surveillance systems, etc. Therefore, it has received considerable attention from both industrial and academic researchers. This dissertation explores schemes for resource management and optimization in WMNs by means of network routing and network coding. In this dissertation, we propose three optimizat
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Shim, Sangho. "Large scale group network optimization." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31737.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Ellis L. Johnson; Committee Member: Brady Hunsaker; Committee Member: George Nemhauser; Committee Member: Jozef Siran; Committee Member: Shabbir Ahmed; Committee Member: William Cook. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Khor, Cheng Seong. "Optimization of water network synthesis." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/39370.

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Water is a key component in most industries. It has become a crucial resource today particularly in the process and allied industries due to increasingly higher demand for water use, scarcities in water resources, and ever more stringent regulations on wastewater discharges. Hence, this thesis addresses water network synthesis with the goal of developing a systematic approach for optimizing water recovery through regeneration-reuse and regeneration-recycle schemes. A water network super structure is first develop ed that consists of three elements similar to a pooling problem formulation: sour
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Liang, Qingkai. "Network optimization in adversarial environments." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120420.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.<br>Cataloged from PDF version of thesis. "There are no tables included in this thesis. Please disregard the list of tables"--Disclaimer Notice page.<br>Includes bibliographical references (pages 193-197).<br>Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, an increasing number of real-world networked systems exhibit complex behaviors that cannot be captured by the simple stochastic models, such as networ
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Panigrahi, Debmalya. "Optimization problems in network connectivity." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/78362.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 115-120).<br>Besides being one of the principal driving forces behind research in algorithmic theory for more than five decades, network optimization has assumed increased significance in recent times with the advent and widespread
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Halilovic, Amar, and Nedim Zaimovic. "Multi-Constrained Network Occupancy Optimization." Thesis, Mälardalens högskola, Inbyggda system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48554.

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Increasingly different types of information are required in systems, resulting in the diverse and widespread use of networks. The increase in the number of devices on networks significantly increases network traffic and network load, limiting network propagation speed, and increasing the possibility of collisions and lost information. One way to identify the size of the network load is by measuring the network occupancy. Although speeds are constantly increasing, some parts of the network can still become the bottleneck. Therefore, it is always desirable to minimize network occupancy. In this
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Zhang, Wanping. "Power network analysis and optimization." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3369236.

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Thesis (Ph. D.)--University of California, San Diego, 2009.<br>Title from first page of PDF file (viewed September 17, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 91-94).
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Books on the topic "Optimization of network"

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Pardalos, Panos M., Donald W. Hearn, and William W. Hager, eds. Network Optimization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59179-2.

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Pahl, Julia, Torsten Reiners, and Stefan Voß, eds. Network Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21527-8.

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1954-, Pardalos P. M., Hearn D. W, and Hager William W. 1948-, eds. Network optimization. Springer, 1997.

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1959-, Nassar Daniel J., ed. Network optimization and troubleshooting. New Riders Pub., 1994.

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Núñez-Queija, Rudesindo, and Jacques Resing, eds. Network Control and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10406-0.

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Altman, Eitan, and Augustin Chaintreau, eds. Network Control and Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00393-6.

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Chahed, Tijani, and Bruno Tuffin, eds. Network Control and Optimization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72709-5.

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Shakkottai, Srinivas Govindaraju. Network optimization and control. Now Publishers, 2008.

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Otar, Cemil. Heat exchanger network optimization. Gulf Pub. Co., Book Division, 1986.

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Lasaulce, Samson, Panayotis Mertikopoulos, and Ariel Orda, eds. Network Games, Control and Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87473-5.

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Book chapters on the topic "Optimization of network"

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Magnanti, Thomas L. "Network optimization." In Encyclopedia of Operations Research and Management Science. Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_664.

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Meyer, R. R. "Network Optimization." In Computational Mathematical Programming. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82450-0_4.

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Buriol, Luciana S. "Network Optimization." In Handbook of Heuristics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07124-4_46.

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Buriol, Luciana S. "Network Optimization." In Handbook of Heuristics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07153-4_46-1.

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Magnanti, Thomas L. "Network Optimization." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_664.

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Khuller, Samir, and Balaji Raghavachari. "Network Optimization." In Handbook of Combinatorial Optimization. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_10.

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Wynants, Christelle. "Network Synthesis Problem." In Combinatorial Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3349-5_2.

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Osterhage, Wolfgang W. "Network Performance." In Computer Performance Optimization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29971-1_6.

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Chen, Chen, and Hanghang Tong. "Network Connectivity Optimization." In Network Connectivity. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03766-5_4.

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Hallale, Nick, Ian Moore, Dennis Vauk, and Paul R. Robinson. "Hydrogen Network Optimization." In Springer Handbook of Petroleum Technology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49347-3_25.

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Conference papers on the topic "Optimization of network"

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Lurdhumary, J., Yashwant Kumar Parte, Ramya M, Rajendra Prasad Ch, M. Jogendra Kumar, and A. N. Arularasan. "Neural Network Approaches to Wireless Network Optimization." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031533.

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Moreno-Palancas, Isabela Fons, Raquel Salcedo D�az, Rub�n Ruiz Femenia, and Jos� A. Caballero. "Handling discrete decisions in bilevel optimization via neural network embeddings." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.175350.

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Bilevel optimization is an active area of research within the operations research community due to its ability to capture the interdependencies between two levels of decisions. This study introduces a metamodeling approach for addressing mixed-integer bilevel optimization problems, exploiting the approximation capabilities of neural networks. The proposed methodology employs neural network embeddings to approximate the optimal follower's response, bypassing the inner optimization problem by parametrizing it with continuous leader�s decisions. The use of Rectified Linear Unit (ReLU) activations
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Kádár, Péter, and Al-Mas Sendegeya. "Optimization of rural network development." In 2024 22nd International Conference on Intelligent Systems Applications to Power Systems (ISAP). IEEE, 2024. http://dx.doi.org/10.1109/isap63260.2024.10744310.

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Papan, Jozef, Slavomir Tatarka, Dominik Skvarna, and Ivana Bridova. "Network Recovery Optimization in SDN." In 2025 37th Conference of Open Innovations Association (FRUCT). IEEE, 2025. https://doi.org/10.23919/fruct65909.2025.11007962.

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Sunori, Sandeep Kumar, Shilpa Jain, Pradeep Juneja, and Amit Mittal. "Optimization of Wireless Sensor Network using Advanced Optimization Techniques." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760331.

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Naik, Sakshi, Miguel Zamarripa, Markus Drouven, and Lorenz T. Biegler. "Integrating the Design of Desalination Technologies into Produced Water Network Optimization." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.195308.

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The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from the oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought co
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Kalsch, Marcel T., and Katrin Tschirpke. "Access network optimization." In 2014 16th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2014. http://dx.doi.org/10.1109/netwks.2014.6959220.

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Yang, Liang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, and Yuanfang Guo. "Topology Optimization based Graph Convolutional Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/563.

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In the past few years, semi-supervised node classification in attributed network has been developed rapidly. Inspired by the success of deep learning, researchers adopt the convolutional neural network to develop the Graph Convolutional Networks (GCN), and they have achieved surprising classification accuracy by considering the topological information and employing the fully connected network (FCN). However, the given network topology may also induce a performance degradation if it is directly employed in classification, because it may possess high sparsity and certain noises. Besides, the lac
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Botros, K. K., D. Sennhauser, J. Stoffregen, K. J. Jungowski, and H. Golshan. "Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10007.

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Operation of large gas pipeline networks calls for fulfilling variation in contractual volume obligations, and maintaining a certain range of linepack with minimum fuel consumptions to drive compressor units. This is often achieved with either operational experience or by utilization of optimization tools, which results in reduced hydraulic analysis time as well as improved pipeline operation as a whole. The main objective is to accurately identify the optimum set points for all compressor stations, control and block valves in the network, subject to several system and operational constraints.
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Barreto, Priscila Solis, and Paulo H. P. de Carvalho. "Network Planning Optimization for Multimedia Networks." In 2008 7th IEEE International Symposium on Network Computing and Applications (NCA). IEEE, 2008. http://dx.doi.org/10.1109/nca.2008.30.

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Reports on the topic "Optimization of network"

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Chiang, Mung. A Wireless Network Testbed for Stochastic Network Optimization. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535199.

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Mesnier, M. P. The network-enabled optimization system server. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/125356.

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Ng, C., P. Thubert, M. Watari, and F. Zhao. Network Mobility Route Optimization Problem Statement. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4888.

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Ng, C., F. Zhao, M. Watari, and P. Thubert. Network Mobility Route Optimization Solution Space Analysis. RFC Editor, 2007. http://dx.doi.org/10.17487/rfc4889.

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Chalamalasetty, Ramcharan. COST OPTIMIZATION OF ALLOCATING VIRTUAL NETWORK FUNCTIONS WITH PRECEDENCE IN MULTICAST NETWORKS. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-432.

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Parekh, Ojas D., Jeremy D. Wendt, Luke Shulenburger, Andrew J. Landahl, Jonathan Edward Moussa, and John B. Aidun. Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1459086.

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Zenios, Stavros A. Parallel and Vector Computing for Nonlinear Network Optimization. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada250218.

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Zhang, Xinmiao. Sensor Network Optimization by Using Error-Correcting Codes. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada565196.

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Boguchwal, Louis. Sanction Effectiveness in Iran: A Network Optimization Approach. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada570106.

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Carley, Kathleen M., and Natalia Y. Kamneva. A Network Optimization Approach for Improving Organizational Design. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada460030.

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