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191	Optimisation of hybrid MED-TVC and double reverse osmosis processes for producing different grades of water in a smart city Al-hotmani, Omer M.A., Al-Obaidi, Mudhar A.A.R., John, Yakubu M., Patel, Rajnikant, Mujtaba, Iqbal M. 07 April 2022 (has links) Yes / The integration of two or more processes in a hybrid system is one of the most desirable options to provide flexibility, interoperability and data sharing between the connected processes. Various examples of hybrid systems have been developed with coherent seawater desalination systems such as the combination of thermal and membrane technologies. This paper focuses on the simulation and optimisation of an integrated (hybrid) system of multi effect distillation and double Reverse Osmosis (RO) processes to produce different grades of water needed in a smart city from seawater resources. The optimisation-based model investigates five scenarios to obtain the highest productivity of drinking water, irrigation water, water for livestock and power plant water, whilst constraining the product water salinity to be within the required standards and with lowest specific energy consumption. For this purpose, multi objective optimisation problem was formulated using the gPROMS (general Process Modelling System) software. The results confirm the superiority of the developed hybrid system to sustain different grades of water in a smart city. Seawater desalination MED-TVC Double RO processes Multi-objective optimisation Grades of water Smart city
192	Virtual Modeling and Optimization of an Organic Rankine Cycle Chandrasekaran, Vetrivel January 2014 (has links) No description available. Automotive Engineering ORC PSO Multi-objective optimization Optimization GT-POWER Waste Heat Recovery
193	Temporal Clustering of Finite Metric Spaces and Spectral k-Clustering Rossi, Alfred Vincent, III 30 October 2017 (has links) No description available. Computer Science clustering multi-objective optimization dynamic metric spaces approximation algorithms hardness of approximation
194	THERMAL-ECONOMIC OPTIMIZATION AND STRUCTURAL EVALUATION FOR AN ADVANCED INTERMEDIATE HEAT EXCHANGER DESIGN Zhang, Xiaoqin 25 October 2016 (has links) No description available. Nuclear Engineering
195	Weight and Cost Multi-Objective Optimization of Hybrid Composite Sandwich Structures Salem, Adel I. January 2016 (has links) No description available. Mechanical Engineering Civil Engineering
196	Multi-Modal Smart Traffic Signal Control Using Connected Vehicles Rajvanshi, Kshitij January 2016 (has links) No description available. Computer Science Adaptive traffic control signal Connected vehicle Multi-objective Bat-Inspired algorithm Vehicle platoons
197	Development of a graphical decision aid for evaluation of multi-objective schedules in a job shop environment Deshpande, Abhijit A. January 1989 (has links) No description available. Engineering, Industrial Graphical Decision Aid Evaluation of Multi-Objective Schedules Job Shop Environment
198	Bayesian Multi-objective Design of Reliability Testing Ramadan, Saleem Z. 25 April 2011 (has links) No description available. Mechanical Engineering Reliability Testing GA Bayesian MLE ALT Multi-objective optimization
199	An Adaptive Dual-Optimal Path-Planning Technique for Unmanned Air Vehicles with Application to Solar-Regenerative High Altitude Long Endurance Flight Whitfield, Clifford A. 22 July 2009 (has links) No description available. Engineering Multi-objective Adaptive On-line Environmentally-influenced Solar Power High Altitude Long Endurance
200	REINFORCEMENT LEARNING FOR CONCAVE OBJECTIVES AND CONVEX CONSTRAINTS Mridul Agarwal (13171941) 29 July 2022 (has links) <p> </p> <p>Formulating RL with MDPs work typically works for a single objective, and hence, they are not readily applicable where the policies need to optimize multiple objectives or to satisfy certain constraints while maximizing one or multiple objectives, which can often be conflicting. Further, many applications such as robotics or autonomous driving do not allow for violating constraints even during the training process. Currently, existing algorithms do not simultaneously combine multiple objectives and zero-constraint violations, sample efficiency, and computational complexity. To this end, we study sample efficient Reinforcement Learning with concave objective and convex constraints, where an agent maximizes a concave, Lipschitz continuous function of multiple objectives while satisfying a convex cost objective. For this setup, we provide a posterior sampling algorithm which works with a convex optimization problem to solve for the stationary distribution of the states and actions. Further, using our Bellman error based analysis, we show that the algorithm obtains a near-optimal Bayesian regret bound for the number of interaction with the environment. Moreover, with an assumption of existence of slack policies, we design an algorithm that solves for conservative policies which does not violate constraints and still achieves the near-optimal regret bound. We also show that the algorithm performs significantly better than the existing algorithm for MDPs with finite states and finite actions.</p> Reinforcement learning reinforcement learning regret analysis concave utility RL constrained RL multi objective RL

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