Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge compu...
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Mobile edge computing is emerging as a novel ubiquitous computing platform to overcome the limit resources of mobile devices and bandwidth bottleneck of the core network in mobile cloud computing. In mobile edge computing, it is a significant issue for cost reduction and QoS improvement to place edge clouds at the edge network as a small data center to serve users. In this paper, we study the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Specifically, we formulate it as a multiobjective optimization problem with objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. To this end, we propose an approximate approach that adopted the K-means and mixed-integer quadratic programming. Furthermore, we conduct experiments based on Shanghai Telecom's base station data set and compare our approach with other representative approaches. The results show that our approach performs better to some extent in terms of workload balance and communication delay and validate the proposed approach.
The possible services of batteries are expanding within the scope of transforming electricity networks. In this study, the potential of batteries to increase transformer efficiency is revealed. It is aimed to maximize...
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ISBN:
(纸本)9781665469258
The possible services of batteries are expanding within the scope of transforming electricity networks. In this study, the potential of batteries to increase transformer efficiency is revealed. It is aimed to maximize the transformer efficiency depending on the demand and the installed power of the transformer. Based on this aim, optimization algorithms that can decide on the optimal battery size are developed. The daily charge-discharge cycle of the battery within the scope of transformer efficiency is explained. According to the pilot site studies carried out, up to 22% efficiency increase potential is discovered in a transformer with the battery. Considering this potential, the use of batteries to support transformer efficiency is recommended as a new local service concept.
The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We fi...
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ISBN:
(纸本)9798350399462
The increasing demand for air travel combined with uncertainties has put additional strain on airport infrastructure and ground handling resources. To improve the efficiency of airport operations, in this paper, We firstly perform an in-depth analysis of the A-SMGCS dataset for Singapore Changi Airport, and then propose taxiing routing solutions for both landing and departure aircraft with involved departure uncertainty, formulating the problem as a mixed-integer quadratic programming (MIQP) problem. The proposed model considers the waypoint-based conflict checking, as well as incorporates anti-overtaking constraints, and head-on constraints exclusive for bidirected graph, aiming to minimize the taxiing time as well as the gap with the scheduled in-block time for landing aircraft and scheduled take-off time for departure aircraft. The presence of departure uncertainty prompts us to build a stochastic model, where constraints with stochastic variables are converted into corresponding chance constraints under designated confidence levels. Incorporating stochastic factors enhances the resilience and dependability of our solution. To evaluate the efficiency of our proposed method, we have elaborately investigated the computational complexity under various test scales, and analyzed how changes in uncertainty and confidence levels impact routing and scheduling solutions on a simplified Singapore Changi Airport network, which could provide significant reference for other work.
'Phis paper proposes a new algorithm for solving mixed-integerquadratic Program =r=ing (MIQP) problems The algorithm is particularly tailored to solving small-scale \1IQPs such as those that arise in embedded hyb...
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'Phis paper proposes a new algorithm for solving mixed-integerquadratic Program =r=ing (MIQP) problems The algorithm is particularly tailored to solving small-scale \1IQPs such as those that arise in embedded hybrid Model Predictive Control (A/1PC) applications. The approach combines branch and bound (B&B) with nonnegative least squares (NNLS), that are used to solve quadraticprogramming (QP) relaxations. The QP algorithm extends a method recently proposed by the author for solvingstrictly convex. QP's, by (i) equality and bilateral inequality constraints, (ii,) warm starting, and (ii) exploiting easy-to-compute lower hounds 011 the optimal cost to reduce the 1'1 ibe r of QP iterations required to solve the relaxed problems. The proposed MIQP algorithm has a speed of execution that is comparable to stateof-the-art commercial MIQP solvers and is relatively simple to code, as it requires only basic arithmetic operations to solve least-square problems.
The execution of a hybrid model predictive controller (MPC) on an embedded platform requires solving a mixed-integer quadratic programming (MIQP) in real time. The MIQP problem is NP-hard, which poses a major challeng...
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The execution of a hybrid model predictive controller (MPC) on an embedded platform requires solving a mixed-integer quadratic programming (MIQP) in real time. The MIQP problem is NP-hard, which poses a major challenge in an environment where computational and memory resources are limited. To address this issue, we propose the use of accelerated dual gradient projection (GPAD) to find both the exact and an approximate solution of the MIQP problem. In particular, an existing GPAD algorithm is specialized to solve the relaxed quadraticprogramming (QP) subproblems that arise in a Branch and Bound (B&B) method for solving the MIQP to optimality. Furthermore, we present an approach to find a suboptimal integer feasible solution of a MIQP problem without using B&B. The GPAD algorithm is very simple to code and requires only basic arithmetic operations which makes it well suited for an embedded implementation. The performance of the proposed approaches is comparable with the state of the art MIQP solvers for small-scale problems. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
With the springing up of data centers (DCs) worldwide, their huge energy consumption presents a daunting challenge to the energy system. This paper considers DC's integration, especially DC's dynamic voltage f...
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ISBN:
(纸本)9781728171951
With the springing up of data centers (DCs) worldwide, their huge energy consumption presents a daunting challenge to the energy system. This paper considers DC's integration, especially DC's dynamic voltage frequency scaling (DVFS) and thermal inertia, proposing a novel optimal sizing method for the energy station in the multi-energy system (MES), which can realize the co-optimization of energy flow and information flow. Firstly, the MES integrated with DC is modeled. Based on DVFS and thermal inertia, the deep coupling relationship of DC's computing workload, electricity load and cooling load is delved. Then, the optimal sizing model aimed to minimize the total cost is established. Constraints include power balance, DC's computing workload balance and thermal balance. The problem is simplified to a mixed-integer quadratic programming one. Case studies based on a test system illustrate that the co-optimization considering DC's integration can reduce the total cost of the energy station by 7.2%.
Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes...
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ISBN:
(纸本)9781538672204
Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership is unknown. One of the most successful methods for binary classification is the support vector machine classifier that aims at finding a hyperplane in the feature space separating the training objects of the two classes. However, the accuracy of this classifier in predicting the correct classes strongly depends on the features selected for determining the hyperplane. In this paper, we propose the first exact approach, which is based on mixed-integer quadratic programming and delayed constraint generation, to identify an optimal set of relevant features for determining the hyperplane. The results of a computational experiment demonstrate that the proposed approach is able to successfully select an optimal set of relevant features in a short running time even for classification tasks with over 10,000 objects and 100 features.
High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill ...
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High-precision motion industrial systems must satisfy tight performance requirements. Both positioning accuracy and throughput demands are typically achieved through improvements in hardware, thereby raising the bill of materials. A cost saving alternative could be to strive for a reduction in the hardware components needed, in combination with advanced motion control, to still meet the desired specifications. Particularly, in this paper, the possibility is analyzed to allow for resource sharing among several actuators. This results in a switched system, for which we develop a real-time MPC algorithm for optimization of both the input and the switching signals. This implementation applies to a fairly general class of nonlinear systems and uses a novel offset-free formulation in velocity form for LTV prediction models, to realize good tracking performance under the resource sharing constraints. We provide a proof of concept for this MPC solution on a high fidelity model of an industrial SCARA robot, where it is proposed to use a single amplifier to serve two actuators. The MPC solution is compared to heuristically switched LTI controllers, and the potential of the proposed approach is shown in simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Buttazzo et al.'s elastic scheduling model allows task utilizations to be "compressed" to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an &qu...
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ISBN:
(纸本)9798331540265;9798331540272
Buttazzo et al.'s elastic scheduling model allows task utilizations to be "compressed" to ensure schedulability atop limited resources. Each task is assigned a range of acceptable utilizations and an "elastic constant" representing the relative adaptability of its utilization. In this paper, we consider federated scheduling, under which each high-utilization parallel task is assigned dedicated processor cores. We propose a new model of elastic workload compression for parallel DAG tasks that assigns each subtask its own elastic constant and continuous range of acceptable workloads. We show that the problem can be solved offline as a mixed-integerquadratic program, or online using a pseudo-polynomial dynamic programming algorithm. We also consider joint core allocation and compression of low-utilization sequential tasks and present a mixed-integer linear program for optimal elastic compression of tasks under partitioned EDF scheduling. We show empirical improvements in schedulability over the prior work and present a case study for the Fast Integrated Mobility Spectrometer (FIMS).
In this paper, by exploiting the equivalence between hybrid systems modeled in the mixed Logic Dynamical form and Piece-Wise Affine systems, we propose a state smoothing algorithm based on Moving Horizon Estimation (M...
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ISBN:
(纸本)0780355202
In this paper, by exploiting the equivalence between hybrid systems modeled in the mixed Logic Dynamical form and Piece-Wise Affine systems, we propose a state smoothing algorithm based on Moving Horizon Estimation (MHE). We provide sufficient conditions on the time horizon and the initial penalties to guarantee asymptotic convergence of the MHE scheme. Moreover we propose an algorithm for the computation of the initial penalties that allows to implement MHE by solving mixed-integerquadratic Programs.
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