Research on Coordinating Cloud Service Supply Chain Considering Service Disruption

The risk of the implementation of cloud service and the worry about the failure of projects or strategies caused by service disruption is an important reason of low adoption rates of the cloud service. Service disruption not only directly affects the cloud service free trial results, but also leads to compensation to the consumers. The coordination problem between a CFP (cloud function provider) and a CIP (cloud integration provider) in a cloud supply chain is investigated, in which service demand is determined by the application free trial. Coordination Contracts are discussed in two kinds of situations, linked respectively to the information symmetry and information asymmetry. The results show that the cost and risk-sharing coordination contracts we proposed can realize optimal supply chain performance, and Pareto improvement of supply chain members’ profits. Reducing the service disruption probability and improving the level of service reliability are the key to the free trial. Besides, the compensation cost allocation enhances the scalability of cost allocation. Through numerical exploration analysis, effectiveness of the model is demonstrated and some managerial insights are obtained

Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance