Required System Resources Research Articles

Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

An Incentive-Based Demand Response (DR) Model Considering Composited DR Resources

Demand response (DR) has been recognized as a powerful tool to help mitigate power imbalances in a future smart energy system. This paper takes the point of view of a grid operator (GO) to establish an intraday resource trading framework, over which DR resources from different sectors (large industrial consumers and small- or middle-sized customers) are brought to the system level and are assessed by the GO along with the generators, with the aim of procuring the required system resources at minimal cost. Considering the distinct behaviors of the involved entities, Stackelberg game theory was adopted to analyze the coordination among the various decision makers. A unique Stackelberg equilibrium was identified that yields the optimal outcome for the resource trading game between the GO and demand-side participators. Simulation results demonstrated that the total procurement cost was minimized by applying the proposed approach.

Opportunistic Computing with Lobster: Lessons Learned from Scaling up to 25k Non-Dedicated Cores

We previously described Lobster, a workflow management tool for exploiting volatile opportunistic computing resources for computation in HEP. We will discuss the various challenges that have been encountered while scaling up the simultaneous CPU core utilization and the software improvements required to overcome these challenges.Categories: Workflows can now be divided into categories based on their required system resources. This allows the batch queueing system to optimize assignment of tasks to nodes with the appropriate capabilities. Within each category, limits can be specified for the number of running jobs to regulate the utilization of communication bandwidth. System resource specifications for a task category can now be modified while a project is running, avoiding the need to restart the project if resource requirements differ from the initial estimates. Lobster now implements time limits on each task category to voluntarily terminate tasks. This allows partially completed work to be recovered.Workflow dependency specification: One workflow often requires data from other workflows as input. Rather than waiting for earlier workflows to be completed before beginning later ones, Lobster now allows dependent tasks to begin as soon as sufficient input data has accumulated.Resource monitoring: Lobster utilizes a new capability in Work Queue to monitor the system resources each task requires in order to identify bottlenecks and optimally assign tasks.The capability of the Lobster opportunistic workflow management system for HEP computation has been significantly increased. We have demonstrated efficient utilization of 25 000 non-dedicated cores and achieved a data input rate of 30 Gb/s and an output rate of 500GB/h. This has required new capabilities in task categorization, workflow dependency specification, and resource monitoring.

GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets.

Selecting core subsets from plant genotype datasets is important for enhancing cost-effectiveness and to shorten the time required for analyses of genome-wide association studies (GWAS), and genomics-assisted breeding of crop species, etc. Recently, a large number of genetic markers (>100,000 single nucleotide polymorphisms) have been identified from high-density single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS) data. However, there is no software available for picking out the efficient and consistent core subset from such a huge dataset. It is necessary to develop software that can extract genetically important samples in a population with coherence. We here present a new program, GenoCore, which can find quickly and efficiently the core subset representing the entire population. We introduce simple measures of coverage and diversity scores, which reflect genotype errors and genetic variations, and can help to select a sample rapidly and accurately for crop genotype dataset. Comparison of our method to other core collection software using example datasets are performed to validate the performance according to genetic distance, diversity, coverage, required system resources, and the number of selected samples. GenoCore selects the smallest, most consistent, and most representative core collection from all samples, using less memory with more efficient scores, and shows greater genetic coverage compared to the other software tested. GenoCore was written in R language, and can be accessed online with an example dataset and test results at https://github.com/lovemun/Genocore.

Two-Way Location Estimation With Synchronized Base Stations

There are two main approaches for estimating a cooperative target location using signal propagation time between base stations at known positions and the target. In one approach, the base stations are synchronized and the location is obtained by one-way transmission from the target to the base stations, or by synchronized transmissions from the base stations to the target. In a second approach, the base stations are unsynchronized and the location is based on two-way transmissions between the target and the base stations. In this paper, we consider a third approach, in which two-way transmissions are used together with synchronized base stations. We develop a novel localization algorithm and analyze its performance. Obviously, base stations synchronization requires system resources, and two-way transmissions require additional resources with respect to one-way transmissions. This higher complexity may be justified by improved performance. Our analysis and simulations give insight into the performance advantage of two-way transmissions with synchronized base stations with respect to the other two approaches. It is shown that the performance advantage is significant only when the signal-to-noise ratio in the uplink and downlink differ, and also depends on the spatial topology of the target and the base stations as well as the uplink and downlink channels correlation.

Phase-Reference-Free Experiment of Measurement-Device-Independent Quantum Key Distribution

Measurement-device-independent quantum key distribution (MDI QKD) is a substantial step toward practical information-theoretic security for key sharing between remote legitimate users (Alice and Bob). As with other standard device-dependent quantum key distribution protocols, such as BB84, MDI QKD assumes that the reference frames have been shared between Alice and Bob. In practice, a nontrivial alignment procedure is often necessary, which requires system resources and may significantly reduce the secure key generation rate. Here, we propose a phase-coding reference-frame-independent MDI QKD scheme that requires no phase alignment between the interferometers of two distant legitimate parties. As a demonstration, a proof-of-principle experiment using Faraday-Michelson interferometers is presented. The experimental system worked at 1MHz, and an average secure key rate of 8.309bps was obtained at a fiber length of 20km between Alice and Bob. The system can maintain a positive key generation rate without phase compensation under normal conditions. The results exhibit the feasibility of our system for use in mature MDI QKD devices and its value for network scenarios.

MIHST: A Hardware Technique for Embedded Microprocessor Functional On-Line Self-Test

Testing processor cores embedded in systems-on-chip (SoCs) is a major concern for industry nowadays. In this paper, we describe a novel solution which merges the SBST and BIST principles. The technique we propose forces the processor to execute a compact SBST-like test sequence by using a hardware module called MIcroprocessor Hardware Self-Test (MIHST) unit, which is intended to be connected to the system bus like a normal memory core, requesting no modification of the processor core internal structure. The benefit of using the MIHST approach is manifold: while guaranteeing the same or higher defect coverage of the traditional SBST approach, it reduces the time for test execution, better preserves the processor core Intellectual Property (IP), does not require the system memory to store the test program nor the test data, and can be easily adopted for non-concurrent on-line testing, since it minimizes the required system resources. The feasibility and effectiveness of the approach were evaluated on a couple of pipelined processors.

Multi-object tracking coprocessor for multi-channel embedded DVR systems

In this paper, the architecture of a video analytics coprocessor is proposed for multi-channel embedded digital video recorder (DVR) systems. A reference video analytics algorithm is proposed for multi-object tracking and is divided into independent processing steps based on data flow. Each step is designed in hardware or software considering its computational complexity and required system resources. Pixelwise processing requiring a large amount of computational resources, such as frame difference and background modeling, are designed as hardware with embedded direct memory access (DMA) controllers. A single-pass connected component labeling (CCL) is designed as a hardware targeting real-time processing of stream input. High-level tasks such as object filtering, frame-based control of hardware modules, and communication with an external host are designed with software on an embedded processor. Object tracking and event detection are designed with software on a host processor. Considering both the bandwidth required for frame processing and the bandwidth available by memory buses, the architecture of a 4-channel video analytics coprocessor is explored. It is finally implemented on a field-programmable gate arrays (FPGA) device, integrated into a conventional DVR system, and verified as to its functions and performance. It can provide video analysis functions to conventional DVR system-on-chip (SoC), and can lessen the cost of real-time video monitoring at remote monitoring centers.

ISO/IEEE 11073 PHD 표준 구현을 통한 복잡도 분석

In this paper, we perform a complexity analysis for implementation of the ISO/IEEE 11073 Personal Health Device (PHD) standards in order to check the required system resources when ISO/IEEE 11073 PHD standards are implemented on the embedded system. Base on the implemented programs complying the PHD standards for a weighing scale, a blood pressure monitor, and a glucose meter among the various personal health devices, we make a pseudo-code. And then from the two different points of view such as program memory space and data memory space, we make a complexity analysis model. Because system resources or capability are strongly restricted in the personal health devices, our research work is very useful to estimate the required system resources.

Simulation of Spouted Bed Using a Eulerian Multiphase Model

Spouted bed systems have emerged as very efficient fluid-particle contactors and find many applications in the chemical and biochemical industry. Some important applications of spouted beds include coal combustion, biochemical reactions, drying of solids, drying of solutions and suspensions, granulation, blending, grinding, and particle coating. An extensive overview can be found in Mathur and Epstein[1]. The pattern of solid and gas flows in a spouted bed was numerically simulated using a CFD modeling technique. The Eulerian-Eulerian multifluid modeling approach was applied to predict gas-solid flow behavior. A commercially available, control-volume-based code FLUENT 6.1 was chosen to carry out the computer simulations. In order to reduce computational times and required system resources, the 2D axisymmetric segregated solver was chosen. The typical flow pattern of the spouted bed was obtained in the present calculation. The simulated velocity and voidage profiles presented a good agreement qualitative and quantitative with the experimental results obtained by He et al. [4].

Application of CFD Modelling Technique in Engineering Calculations of Three-Phase Flow Hydrodynamics in a Jet-Loop Reactor

The hydrodynamics of a down flow jet-loop reactor with a gas-liquid-solid three-phase system in semi-industrial scale were investigated. The Eulerian-Eulerian modelling approach was applied to predict flow behaviour in the reactor. A commercially available, control-volume-based code FLUENT 6.1 was chosen to carry out the computer simulations. In order to reduce computational times and required system resources, the 2D axisymmetric segregated solver was chosen. The influence of different k-e turbulence models, as well as, different types of meshes on velocity profiles in each phase was analyzed. The unstructured mesh reduces discrepancies on the axis of symmetry caused by the axisymmetric solver and is more accurate. The prediction error of the water circulation rate ratio for a gas-liquid system was only 3.6 % and about 15 % for gas-liquid-solid system. Unfortunately the gas hold-up was not predicted properly.