This article focuses on vibration suppression of an Euler-Bernoulli beam which is subject to external disturbance. By integrating backstepping technique, an adaptive boundary iterative learning control (ABILC) is put forward to suppressing vibration. The adaptive law is proposed for handing the parameter uncertainty and the iterative learning term is designed to deal with periodic disturbance. An auxiliary system is utilized to compensate the effect of input nonlinearity. In addition, a barrier Lyapunov function is adopted to deal with asymmetric output constraint. With the proposed control strategy, the stability of the closed-loop system is proven based on rigorous Lyapunov analysis. In the end, the effectiveness of the proposed control is illustrated through numerical simulation results.

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength-based (RSS-Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS-Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real-time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real-time navigation performance, lightens the computation load on the mobile phone, and allows cloud-based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas.

The multi-way join query plays a fundamental role in many big data analytic scenarios. Recently, the hybrid join query is becoming increasingly important. However, the existing one-round and multi-round algorithms have limitations in the process of the hybrid query. In this paper, we present a novel hybrid structure-aware multi-way join algorithm called HyMJ, which combines the one-round and multi-round algorithms to compute the hybrid query efficiently. First, we propose the query structure graph (QSG) to represent the internal query structure of a given join query and the query structure decomposition tree (QSDT) to represent the structure-aware query plan. Each internal node of the QSDT denotes a subquery with a cyclic or acyclic query structure. Then, we design a graph contraction based algorithm to construct QSDT from QSG. Furthermore, to select the optimal join strategy for each subquery in the QSDT, we introduce a heuristic strategy selection model. Experimental results on Apache Spark reveal that HyMJ outperforms both the one-round and multi-round algorithms for hybrid multi-way join queries on real-world datasets.

The multi-way join query has attracted considerable attention from research community for its importance in many big data analytic applications. For the multi-round multi-way join algorithm in distributed data-parallel platforms, the huge communication cost caused by shuffling large intermediate results over the network is the main bottleneck. The one-round multi-way join algorithm processes the join query in a single communication round, which can significantly reduce the communication cost in complex queries, including cyclic queries. However, the one-round method is not always superior to the multi-round method, because the intermediate result size of the multi-round method may the much smaller than the size of data shuffled in the one-round method. Therefore, it is challenging to choose the best multi-way join algorithm in practice. To solve this problem, in this paper, we present AutoMJ, an efficient framework for multi-way join queries. In AutoMJ, we propose a novel automatic join strategy selection model based on the size estimation of intermediate join results. AutoMJ chooses the multi-way join strategy with the minimal shuffle data size. In addition, we propose an optimized HyperCube algorithm for the one-round multi-way join. We have implemented the prototype of AutoMJ on the widely-used distributed data-parallel platform Apache Spark. Experiments show that for multi-way join queries with large intermediate results, the one-round join strategy can outperform the multi-round join strategy built in Spark SQL 1.2 - 159.3× faster. In contrast, the multi-round join strategy is 2.1 - 6.2× faster than the one-round method for the queries with small intermediate results. Experiments also show that the relative error of size estimation can be within 0.1 for the Twitter dataset and 0.25 for the Wikidata dataset. Furthermore, experiments verify that the automatic join strategy selection model is effective for choosing the optimal multi-way join algorithm.