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1. Dynamic Routing and Spectrum Assignment in Co-Existing Fixed/Flex-Grid Optical Networks

   https://doi.org/10.1109/ANTS.2018.8710140  

   Ahmed, Tanjila; Rahman, Sabidur; Tornatore, Massimo; Yu, Xiaosong; Kim, Kwangjoon; Mukherjee, Biswanath (December 2018, 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))

   A traditional wavelength-division multiplexed (WDM) backbone network with its rigid features is unsuitable for emerging diverse and high bitrate (400 Gb/s, 1 Tb/s) traffic needs. Flexible solutions employ new technologies such as bandwidth-variable optical cross connects (BV-OXC) with liquid crystal (LCoS) wavelength-selective switches (WSS), sliceable bandwidth-variable transponders (SBVT), etc. in a flex-grid network. Flex-grid network operates on variable spectral granularities (e.g., 12.5 GHz), and higher modulation formats (quadrature amplitude modulation). However, a greenfield deployment of flex-grid technologies may not be practical, due to cost of technology and usability. This leads to a brown-field network where both fixed-grid and flex-grid technologies co-exist with seamless interoperability. Thus traditional traffic routing and resource allocation techniques need to evolve in a mixed-grid infrastructure. Our study considers the dynamic routing and spectrum assignment (RSA) problem in a fixed/flex-grid co-existing optical network. It provisions routes for dynamic, heterogeneous traffic, ensuring maximum spectrum utilization and minimum blocking. 

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2. PRODIGY+: a robust progressive upgrade approach for elastic optical networks

   https://doi.org/10.1364/JOCN.525392  

   Petale, Shrinivas; Knapińska, Aleksandra; Erbayat, Egemen; Lechowicz, Piotr; Walkowiak, Krzysztof; Lin, Shih-Chun; Matsuura, Motoharu; Hasegawa, Hiroshi; Subramaniam, Suresh (August 2024, Journal of Optical Communications and Networking)

   Elastic optical networks (EONs) operating in the C-band have been widely deployed worldwide. However, two major technologies—multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs)—can significantly increase network capacity beyond traditional EONs. A one-time greenfield deployment of these flexible-grid technologies may not be practical, as existing investments in flexible-grid EONs need to be preserved and ongoing services must face minimal disruption. Therefore, we envision the coexistence of flexible-grid, multiband, and multicore technologies during the brownfield migration. Each technology represents a tradeoff between higher capacity and greater deployment overhead, directly impacting network performance. Moreover, as traffic demands continue rising, capacity exhaustion becomes inevitable. Considering the different characteristics of these technologies, we propose a robust network planning solution called Progressive Optics Deployment and Integration for Growing Yields (PRODIGY+) to gradually migrate current C-band EONs. PRODIGY+ employs proactive measures inspired by the Swiss Cheese Model, making the network robust to traffic peaks while meeting service level agreements. The upgrade strategy enables a gradual transition to minimize migration costs while continuously supporting increasing traffic demands. We provide a detailed comparison of our proposed PRODIGY+ strategy against baseline strategies, demonstrating its superior performance. 

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3. Data-driven adaptive dynamic coordination of damping controllers

   F. Zelaya-Arrazabal; J. Liu; J. Zhao; H. Pulgar-Painemal; F. Li; H. Silva-Saravia (October 2022, North American Power Symposium)

   Mostafa Sahraei-Ardakani; Mingxi Liu (Ed.)

   This paper proposes a data-driven adaptive coordination of damping controllers to enhance power system stability. The coordination uses wide-area frequency measurements to select the switching status (on/off) of damping controllers (DC) enabled in electronically-interfaced resources (EIR). This is done by using the total action (TA), a dynamic performance measure of the oscillation energy related to the synchronous generators; and deep neural networks (DNNs), a powerful learning algorithm capable of providing accurate model regression between the grid measurements and the TA. The concept is tested in the Western North America Power System (wNAPS) and compared with a model-based approach for coordination of damping controllers. These are the first results of an extensive research related to coordination of DC-EIR, showing good adaptability and performance to different fault locations across the grid. 

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4. Validating the OPA cascading blackout model on a 19402 bus transmission network with both mesh and tree structures

   https://doi.org/978-0-9981331-2-6  

   Carreras, Benjamin A; Reynolds-Barredo, Jose-Miguel; Dobson, Ian; Newman, David E (January 2019, 52th Hawaii International Conference on System Sciences)

   The OPA model calculates the long-term risk of cascading blackouts by simulating cascading outages and the slow process of network upgrade in response to blackouts. We validate OPA on a detailed 19402 bus network model of the Western Electricity Coordinating Council (WECC) interconnection with publicly available data. To do this, we examine scalings on a series of WECC interconnection models with increasing detail. The most detailed, 19402 bus network has more tree structures at the edges of the main mesh structure, and we extend the OPA model to account for this. The higher-risk cascading outages are the large cascades that extend across interconnections, so validating cascading models on large networks is crucial to understanding how the real grid behaves. Finally, exploring networks with mixed mesh and tree like structure has implications for the risk analysis for both the transmission grid and other network infrastructures. 

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5. Output Range Analysis for Deep Feedforward Neural Networks

   https://doi.org/10.1007/978-3-319-77935-5_9  

   Dutta, Souradeep; Jha, Susmit; Sankaranarayanan, Sriram; Tiwari, Ashish (January 2018, NASA Formal Methods)

   Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes. 

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