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1.
Digital twin (DT) has garnered attention in both industry and academia. With advances in big data and internet of things (IoTs) technologies, the infrastructure for DT implementation is becoming more readily available. As an emerging technology, there are both potential and challenges. DT is a promising methodology to leverage the modern data explosion to aid engineers, managers, healthcare experts and politicians in managing production lines, patient health and smart cities by providing a comprehensive and high fidelity monitoring, prognostics and diagnostics tools. New research and surveys into the topic are published regularly, as interest in this technology is high although there is a lack of standardization to the definition of a DT. Due to the large amount of information present in a DT system and the dual cyber and physical nature of a DT, augmented reality (AR) is a suitable technology for data visualization and interaction with DTs. This paper seeks to classify different types of DT implementations that have been reported, highlights some researches that have used AR as data visualization tool in DT, and examines the more recent approaches to solve outstanding challenges in DT and the integration of DT and AR.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00375-w  相似文献   
2.
Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.  相似文献   
3.
With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.  相似文献   
4.
The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.  相似文献   
5.
针对非线性系统模型的辨识问题,通过引入正交匹配追踪(Orthogonal matching pursuit,OMP)算法实现快速非线性系统建模。该方法旨在解决非线性有源自回归(Nonlinear autoregressive with exogenous inputs,NARX)模型针对大型数据建模时效性差的问题。首先,说明了正交最小二乘(Orthogonal least squares,OLS)算法存在正交次数多、耗时长的问题,采用OMP算法可有效解决,通过与OLS算法对比正交差异性证明了OMP算法计算效率提升的理论基础,采用模型预报方法验证OMP算法所得NARX模型的动力学特性。其次,以单自由度非线性系统为例,说明了OMP算法系统建模的有效性。最后,利用OMP算法建立悬臂梁NARX模型,并分别将NARX模型预报输出与试验实测输出,NARX模型固有频率与悬臂梁实际固有频率进行对比。结果表明,与OLS算法相比,所提方法的建模效率平均提升了10倍,且模型可有效反应系统动力学特性。  相似文献   
6.
负荷惯量的准确、在线估计是电力系统电压和频率稳定分析的重要基础。为此,提出了一种基于类噪声的负荷惯量辨识方法,基于系统中时刻存在的类噪声信号跟踪等值负荷惯量的时变特性。考虑到类噪声条件下惯量参数可辨识性较差,采用两阶段辨识的思路,首先对负荷的电磁参数进行辨识,然后基于第一阶段辨识得到的状态变量估计出惯量、转矩系数等机电参数。仿真算例和实测数据分析表明,所提负荷惯量辨识方法能够适应不同变转矩情形下负荷惯量的类噪声辨识需求;相较于恒转矩辨识方法,该方法能够更加准确地反映实际负荷的机电暂态特性,得到稳定、可靠的惯量辨识结果。  相似文献   
7.
In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.  相似文献   
8.
强波动性分布式电源的大量接入给配电系统可靠安全运行带来了挑战。为此,提出了一种考虑储能与动态重构的柔性互联配电系统两阶段鲁棒优化调度方法。首先,建立了综合考虑柔性配电开关、储能与动态重构的柔性互联配电系统两阶段鲁棒优化数学模型,其中第一阶段考虑日前分布式电源出力预测的不确定性以及网络拓扑和储能的时序相关性进行全局优化,第二阶段基于第一阶段的决策方案和准确的超短期预测信息,在各时段对柔性配电开关进行实时调度;其次,采用改进的列与约束生成算法对模型进行求解,通过辅助变量和对偶变量交替迭代,显著提高子问题的求解效率;最后,通过33和69节点系统进行测试,验证了所提调度模型和求解算法的有效性。  相似文献   
9.
张驰  周骏  赵镔  李嘉乐  杨博 《电力建设》2022,43(8):1-12
随着氢气生产和储存技术的快速发展,开发氢气储能系统(hydrogen energy storage systems, HESSs)将给能源和电力系统结构带来根本性变化。HESSs和电池储能系统(battery energy storage systems, BESSs)相结合进行协调优化可以解决多种能源供需之间的不平衡,并提高能源效率。为确保BESSs和HESSs规划的有效性,以最小全生命周期成本(life cycle cost, LCC)、系统网损、联络线交换功率偏差、负荷波动以及电压波动为目标,采用带精英策略的非支配排序遗传算法(non-dominated sorting genetic algorithm-II, NSGA2)求解储能系统(energy storage systems, ESSs)选址定容规划方案的Pareto非支配解集。并利用基于熵权法(entropy weight method, EWM)的灰靶决策在Pareto非支配解集中选取最佳折中解。另外,通过模糊核C-均值(fuzzy kernel C-means, FKCM)聚类算法获取源荷典型运行场景集,并基于扩展的IEEE-33节点系统进行仿真分析。仿真结果表明:NSGA2算法不仅实现了电-氢混合储能系统LCC最小,且其电压质量、功率稳定性、网损与负荷波动也显著优于对比算法。  相似文献   
10.
淀粉和蛋白质是食品的重要组成成分,除能提供丰富的营养成分外,对食品的理化特性和食品品质也有显著影响。淀粉和蛋白质之间形成的混合体系(复合物、凝胶、混合物等)可改善淀粉及蛋白质的理化特性,已引起食品、生物、医药等行业的广泛关注。本文综述了国内外淀粉-蛋白混合体系的制备方法、混合体系的形成对食品理化特性的影响及其应用研究进展,为拓展淀粉及蛋白在食品工业中的应用提供理论依据。  相似文献   
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