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The feasibility of using infrared (IR) spectroscopy of the neck muscles in controlling a cursor in a 2-dimensional screen was assessed. The proposed technique utilizes two IR photoplethysmography sensors (λ = 940nm) to monitor the morphological changes of the Scalene and Sternocleidomastoid muscles. Since the reflection of the light has valuable information about the type of contraction, the direction...
Total Cost of Ownership (TCO) is a comprehensive tool for cost estimation, provisioning, and decision making in a data center. The goal of this paper is to introduce an accurate yet simple model of TCO for data centers. TCO-estimation helps to clear the cost trade-offs, highlights the most impactful parameters on TCO in a datacenter which helps us to focus on research and development efforts to optimize...
Liquid cooling provides a feasible thermal management solution in the case of high power density cooling, in addition, it offers several advantages for improving data center energy efficiency. For example, liquid cooling solution may eliminate the utilization of conventional chiller in a data center cooling infrastructure. Since a large portion of heat can be extracted directly to the liquid, the...
An analysis of online learning for adaptive optimal control through value iteration is presented. Stability of the system operated using any single immature control policy during the learning stage is established. The contribution of this work is incorporating approximation errors present in the function approximation process in developing the results. This is done through establishing sufficient...
Optimal triggering of networked control systems is investigated in this study and the framework of approximate dynamic programming is selected for solving the problem. Different cases including Zero-Order-Hold, Generalized Zero-Order-Hold, and lossy networks are investigated in designing optimal triggering/scheduling laws. After analyzing convergence, optimality, and stability, the performance of...
The problem of optimal switching between different topologies in step-down DC-DC voltage converters is investigated. Different challenges including presence of line and load disturbances as well as constraint on the inductor current are incorporated. The objective is generating the desired voltage with low ripples and high robustness towards the disturbances. The selected tool is a previously developed...
An approximate solution for optimal switching problems with continuous-time dynamics and infinite horizon cost functions is developed in this paper. The proposed solution is a policy iteration-based algorithm and provides a feedback scheduling policy with a negligible real-time computational burden. The convergence to the optimal solution and stability of the system under the proposed solution are...
Optimal switching between different subsystems with discrete-time dynamics subject to a minimum dwell time constraint is investigated in this study. A feedback solution, using the framework of approximate dynamic programming, is proposed through tuning parameters of a function approximator. Once tuned, online control will be conducted with a computational load as low as evaluating a few scalar-valued...
Policy iteration, as an adaptive/approximate dynamic programming-based approach for optimal control is investigated. The context is optimal control of discrete-time nonlinear dynamics with undiscounted cost functions. Convergence of the learning iterations and uniqueness of the solution to the corresponding Bellman equation are established, leading to the optimality of the limit function, i.e., the...
Value iteration as an algorithm for ‘learning’ solutions to discrete-time optimal control problems is investigated in this paper. It is shown that if the iterations are initialized using a stabilizing initial guess, then the evolving control at each iteration will remain stabilizing. The novelty of this study is in providing rigorous theoretical analyses on a) continuity of the value function subject...
One important motivation of data center mechanical system R&D is to improve the energy efficiency and reliability. Many new cooling solutions have been successfully used in production data centers, such as hybrid/liquid cooling systems and free cooling systems, and a better Power Usage Effectiveness (PUE) has been achieved when compared with traditional air cooling data centers. Liquid cooling...
A reinforcement learning based scheme for optimal switching with an infinite-horizon cost function is briefly proposed in this paper. Several theoretical questions are shown to arise regarding its convergence, optimality of the result, and continuity of the limit function, to be uniformly approximated using parametric function approximators. The main contribution of the paper is providing rigorous...
The problem of global optimality analysis of approximate dynamic programming based solutions is investigated in this study. Sufficient conditions for global optimality is obtained without requiring the state penalizing terms in the cost function or the functions representing the dynamics to be convex functions. Afterwards, the theoretical results are confirmed through a qualitative analysis of an...
Approximate dynamic programming is utilized in this study to develop solutions for optimal switching problems. The order of the active subsystems and the number of switches are free and an online solution is developed that produces optimal performance. Motivated by the development in the adaptive critics literature, the proposed method uses a critic and as many actors as the number of subsystems in...
The problem of decentralized control of multi-agent nonlinear systems is solved by introducing the concept of virtual agents to generate reference trajectories to be tracked by the actual agents. The tracking problem as an optimal control problem is formulated in the framework of approximate dynamic programming. Solutions are obtained using ‘single network adaptive critics’ and network weight update...
The Hamilton-Jacobi-Bellman partial differential equation, which is needed to be solved for finite-horizon optimal control of nonlinear systems, is reduced to a state-dependent differential Riccati equation subject to a final condition through some approximations. Afterward, a method, called Finite-SDRE, is developed for finite-horizon near-optimal control synthesis. This technique allows for easier...
A single neural network based controller called the Finite-SNAC is developed in this study to synthesize finite-horizon optimal controllers for nonlinear control-affine systems. For satisfying the constraint on the input, a non-quadratic cost function is used. Inputs to the neural network are the current system states and the time-to-go and the network outputs are the costates which are used to compute...
Large-scale data centers consume megawatts in power and cost hundreds of millions of dollars to equip. Reducing the energy and cost footprint of servers can therefore have substantial impact. Web, Grid, and cloud servers in particular can be hard to optimize, since they are expected to operate under a wide range of workloads. For our upcoming data center, we set out to significantly improve its power...
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