The Three-Volume-Set CCIS 323, 324, 325 (AsiaSim 2012) together with the Two-Volume-Set CCIS 326, 327 (ICSC 2012) constitutes the refereed proceedings of the Asia Simulation Conference, AsiaSim 2012, ...and the International Conference on System Simulation, ICSC 2012, held in Shanghai, China, in October 2012. The 267 revised full papers presented were carefully reviewed and selected from 906 submissions. The papers are organized in topical sections on modeling theory and technology; modeling and simulation technology on synthesized environment and virtual reality environment; pervasive computing and simulation technology; embedded computing and simulation technology; verification, validation and accreditation technology; networked modeling and simulation technology; modeling and simulation technology of continuous system, discrete system, hybrid system, and intelligent system; high performance computing and simulation technology; cloud simulation technology; modeling and simulation technology of complex system and open, complex, huge system; simulation based acquisition and virtual prototyping engineering technology; simulator; simulation language and intelligent simulation system; parallel and distributed software; CAD, CAE, CAM, CIMS, VP, VM, and VR; visualization; computing and simulation applications in science and engineering; computing and simulation applications in management, society and economics; computing and simulation applications in life and biomedical engineering; computing and simulation applications in energy and environment; computing and simulation applications in education; computing and simulation applications in military field; computing and simulation applications in medical field.
In recent years, using wireless sensor networks (WSNs) for structural health monitoring (SHM) has attracted increasing attention. Traditional centralized SHM algorithms developed by civil engineers ...can achieve the highest damage detection quality since they have the raw data from all the sensor nodes. However, directly implementing these algorithms in a typical WSN is impractical considering the large amount of data transmissions and extensive computations required. Correspondingly, many SHM algorithms have been tailored for WSNs to become distributed and less complicated. However, the modified algorithms usually cannot achieve the same damage detection quality of the original centralized counterparts. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a distributed version for WSNs. In this approach, the required computations in the ERA are updated incrementally along a path constructed from the deployed sensor nodes. This distributed version is able to achieve the same quality of the original ERA using much smaller wireless transmissions and computations. The efficacy of the proposed approach is demonstrated through both simulation and experiment.
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system, called Walle, as ...the foundation. Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration. Specifically, the compute container is based on Mobile Neural Network (MNN), a tensor compute engine along with the data processing and model execution libraries, which are exposed through a refined Python thread-level virtual machine (VM) to support diverse ML tasks and concurrent task execution. The core of MNN is the novel mechanisms of operator decomposition and semi-auto search, sharply reducing the workload in manually optimizing hundreds of operators for tens of hardware backends and further quickly identifying the best backend with runtime optimization for a computation graph. The data pipeline introduces an on-device stream processing framework to enable processing user behavior data at source. The deployment platform releases ML tasks with an efficient push-then-pull method and supports multi-granularity deployment policies. We evaluate Walle in practical e-commerce application scenarios to demonstrate its effectiveness, efficiency, and scalability. Extensive micro-benchmarks also highlight the superior performance of MNN and the Python thread-level VM. Walle has been in large-scale production use in Alibaba, while MNN has been open source with a broad impact in the community.
State Grid Jibei Electric Power Company (Jibei) has accumulated a large number of operation data through the maintenance of the equipment, devices in power grids. It is of great significance to study ...this data and extract the useful information by the data mining and statistics techniques. While the theories and methods of Naive Bayes have been widely studied, this paper utilizes such technique to explore the model of power grid diagnosis, and analyzes the real data of recent years. This work is expected to drive the correct decision on the malfunctions of power grid and increase the efficiency of the operations.
Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither ...accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks.
In this paper, a computer-based system is presented to measure the static electromagnetic characteristics (SECs) of switched reluctance machines (SRM), including magnetization characteristics (MCs) ...and torque characteristics (TCs). The MCs are measured indirectly based on phase voltage and current by digital integration technology with rotor mechanically locked, while the TCs are directly determined by static torque transducer when phase current maintains a certain value. A user interface is designed in computer by LabVIEW to manage the operation of the whole system.
A 16 kDa protein SPE16 was purified from the seeds of Pachyrrhizus erosus. Its N‐terminal amino‐acid sequence showed significant sequence homology to pathogenesis‐related proteins from the PR‐10 ...family. An activity assay indicated that SPE16 possesses ribonuclease activity as do some other PR‐10 proteins. SPE16 crystals were obtained by the hanging‐drop vapour‐diffusion method. The space group is P212121, with unit‐cell parameters a = 53.36, b = 63.70, c = 72.96 Å.
The proteins Spe31 and Spe32, named after their respective molecular weights of about 31 and 32 kDa, were purified simultaneously from the seeds of Pachyrrhizus erosus. They cannot be separated from ...each other by column chromatography. N‐terminal sequence analysis indicated that they belonged to the papain family of cysteine proteases. An in‐gel activity assay revealed that Spe31 possesses proteolytic activity while Spe32 only displays very weak activity for protein degradation. Both of them are glycoproteins as detected by the periodic acid and Schiff's reagent method. Crystals were obtained from the protein mixture by the hanging‐drop vapour‐diffusion method; they diffracted to a resolution of 2.61 Å on an in‐house X‐ray source. The crystals belong to space group P41(3)212, with unit‐cell parameters a = b = 61.96, c = 145.61 Å. Gel electrophoresis under non‐denaturing conditions showed that the protein crystallized was Spe31.
Quantum computing hardware has received world-wide attention and made considerable progress recently. YIG thin film have spin wave (magnon) modes with low dissipation and reliable control for quantum ...information processing. However, the coherent coupling between a quantum device and YIG thin film has yet been demonstrated. Here, we propose a scheme to achieve strong coupling between superconducting flux qubits and magnon modes in YIG thin film. Unlike the direct \(\sqrt{N}\) enhancement factor in coupling to the Kittel mode or other spin ensembles, with N the total number of spins, an additional spatial dependent phase factor needs to be considered when the qubits are magnetically coupled with the magnon modes of finite wavelength. To avoid undesirable cancelation of coupling caused by the symmetrical boundary condition, a CoFeB thin layer is added to one side of the YIG thin film to break the symmetry. Our numerical simulation demonstrates avoided crossing and coherent transfer of quantum information between the flux qubits and the standing spin waves in YIG thin films. We show that the YIG thin film can be used as a tunable switch between two flux qubits, which have modified shape with small direct inductive coupling between them. Our results manifest that it is possible to couple flux qubits while suppressing undesirable cross-talk.
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a ...particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
