•We present TopoMatch.•TopoMatch is a tool and algorithm to perform process and thread mapping.•We Show that TopoMatch can use any type of topologies.•We show its versatility and usefulness for ...several use-cases.
Process mapping (or process placement) is a useful algorithmic technique to optimize the way applications are launched and executed onto a parallel machine. By taking into account the topology of the machine and the affinity between the processes, process mapping helps reducing the communication time of the whole parallel application. Here, we present TopoMatch, a generic and versatile library and algorithm to address the process placement problem. We describe its features and characteristics, and we report different use-cases that benefit from this tool. We also study the impact of different factors: sparsity of the input affinity matrix, trade-off between the speed and the quality of the mapping procedure as well as the impact of the uncertainty (noise) onto the input.
To address the well-known noise sensitivity problems associated with high-gain observers, we insert a low-pass filter on the measurement channel. Considering nonlinear plants in observability ...canonical form, we first motivate an architecture where the output error is filtered by a linear system parametrized by its arbitrary order and a scalar positive gain. Our main result establishes an exponential finite gain bound for the estimation error, from the measurement noise, this gain being dependent on the high-gain and filter parameters. We also prove bounds depending on the filter parameters characterizing improved high-frequency gains from the measurement noise to the estimation error. The proposed construction is shown to behave desirably in numerical simulations.
An accuracy improving method for composite grating phase measuring profilometry (CGPMP) based on mixed filtering window is proposed. When CGPMP is used to carry out three-dimensional (3D) ...measurement, the phase-shifting deformed patterns can be demodulated effectively from the captured composite deformed pattern by proper filters. So the selection of the filtering window is very important. By analyzing the characteristics of noises and their different effects on the spectrum components along different orientations, searching for the best filtering window of the spatial spectrum along the two mutually orthogonal directions, a mixed filtering window to improve measuring accuracy is established. Digital simulating results show that the designedly mixed filtering window has better noise suppression than rectangular, triangular, Blackman and Hanning filtering windows. The experimental results show that the measuring error using the mixed filtering window equalizing noise and spectrum leakage is the smallest, and the root mean square error (RMSE) is reduced by 9.64%, and the measuring accuracy is improved effectively.
The Taiji program is dedicated to the detection of middle and low-frequency gravitational waves, targeting the 0.1 mHz to 1 Hz frequency band. The project requires an acceleration residual ...sensitivity of 3 × 10−15 ms−2/Hz1/2, which necessitates a capacitance sensing resolution of 1 aF/Hz1/2 for the capacitive sensing system within the specified frequency range. The noise level of the resonant bridge significantly influences the resolution. Addressing the challenges in enhancing transformer performance parameters in existing resonant capacitance bridges and the constraints on improving the characteristics of resonant capacitance bridges, this study introduces a novel approach to reduce bridge thermal noise without optimizing existing parameters. The simulation results demonstrate that this scheme can reduce the noise to 0.7 times the original level and further reduce bridge thermal noise when other parameters affecting noise are optimized. This not only mitigates the demands for other performance parameters but also increases the range of maximum acceptable resonant frequency deviations and reduces its sensitivity to such variations. Experimental validation confirms that the proposed scheme effectively reduces noise by 0.7 times and improves the resolution of capacitance sensing to 0.6 aF/Hz1/2, thereby advancing the Taiji program gravitational wave detection capabilities.
•The vibro-acoustic noise analysis of SR motors in electric vehicles is investigated.•Elaborated the significance and role of transient model-based multiphysics analysis in SR motor.•Multiple ...vibro-acoustic noise sources of EM forces were extensively investigated and analyzed.•The maximal vibration and noise level is observed in the frequency range of 250 and 1250 Hz in SR motor.•The experimental results are validated through simulation using model analysis of SR motor.
The SR motor is acquiring popularity due to its dynamic characteristics such as higher power, torque, and efficiency. Even though, Vibro-acoustic noise is a significant issue in SR motors, which are caused by radial electromagnetic forces. This research adopted a novel methodology to investigate the various vibro-acoustic noise sources to enhance the performance of real-time SR motors. This methodology integrates transient model-based multiphysics and experimental analysis to predict the vibro-acoustic noise sources in SR motors. A multiphysics model incorporates not only the EM and acoustic fields, but also the structural geometry and variably distributed EM forces, to predict and analyse the EM vibro-acoustic noise in SR motor. The experimental results reveal that the maximum vibro-acoustic noise amplitudes are observed at 150, 240, 570, 920, 1210, and 1650 Hz due to uneven EM forces. To verify the presence of experimental vibro-acoustic noise levels at different frequencies, a transient model-based multiphysics analysis is investigated. The vibro-acoustic noise distribution trends seen in the simulation are consistent with those seen in the experiments. Finally, based on the simulation and experimental results revealed that EM forces, load-current changes, flux density, torque ripples are the significant reasons for greatest vibro-acoustic noise levels in the SR motor.
Corrosion inhibition performance of a newly synthesized ionic liquid Benzyltributylammonium tetrachloroaluminate BTBA+AlCl4−on carbon steel has been studied using electrochemical impedance and noise ...analysis in 2 N HCl medium. The synthesized product was characterized by ATR-FTIR and1H NMR spectroscopic studies. The investigation revealed that the synthesized ionic liquid, BTBA+AlCl4−showed a remarkable noise and charge transfer resistance against corrosion. The adsorption behaviour of BTBA+AlCl4- on metal surface was found to follow Langmuir adsorption isotherm. The inhibition efficiency is measured as a function of immersion time and exhibited prolonged protection against acidic corrosion. Results derived from UV–Vis spectra explained the complex formation between the metal surface and ionic liquid in acid medium. SEM/EDAX has been used to examine the surface protection offered by the ionic liquid. BTBA+AlCl4−ionic liquid exhibited good corrosion inhibitor property with an efficiency of 97% at the optimum concentration. Quantum chemical analysis and molecular simulation studies were performed to support the experimental data.
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•A novel ionic liquid with metal complex anion (Lewis acid) was synthesized and characterized.•BTBA+AlCl4- has governed 97% of inhibition efficiency in 2 N HCl acid medium.•Extent inhibition efficiency of ionic liquid with respect to immersion time (12 h) was determined using both impedance and noise analysis.•Prolonged surface protection effect of ionic liquid is captured from SEM/EDAX analysis.•Monte Carlo analysis is employed to support the interaction behaviour of ionic liquid in acid medium.
Abstract
White light interferometry is a highly precise non-contact measurement technology. However, in practical applications, certain noise sources such as thermal noise, shot noise, and redundant ...intensity noise, can introduce signal distortion. In this paper, we propose a mathematical formulation for the key metric, Quality Factor, which serves as an evaluation criterion for the quality of interference signals. The influence of different factors on the measurement is quantitatively analyzed using the signal-to-noise ratio, and various filtering methods are employed to address different types of noise. Finally, experimental results highlight the primary factors and optimization techniques that significantly impact the quality of interference signals, thereby verifing the effectiveness of the method on measurement results.
INTRODUCTION: Power grid blackouts occur frequently, which significantly impacts social impact. Because these accidents are dynamic and random, predicting and evaluating them is challenging.
...OBJECTIVES: To explore the complexity of the power grid itself, analyzes the critical changes of the self-organizing model during power grid fault, extracts the data characteristics related to the steady-state maintenance of abnormal systems, and puts forward an effective outage prediction model.
METHODS: Starting with cluster analysis, The authors can reduce data fluctuation and eliminate noise interference to optimize data. The evaluation indexes of initial fault occurrence possibility and fault propagation speed in the power grid are constructed.
RESULTS: The validation of the outage forecasting model has produced promising results, achieving 96.4% forecasting accuracy and a meager error rate. In addition, the evaluation index developed in this study accurately reflects the possibility and spread speed of power outage accidents.
CONCLUSION: The research proves the feasibility of establishing an outage prediction model based on the power grid system data characteristics. The model has high accuracy and reliability and is a valuable tool for power outage research and judgment.
This paper presents a two-stage mesh denoising algorithm. Unlike other traditional averaging approaches, our approach uses an element-based normal voting tensor to compute smooth surfaces. By ...introducing a binary optimization on the proposed tensor together with a local binary neighborhood concept, our algorithm better retains sharp features and produces smoother umbilical regions than previous approaches. On top of that, we provide a stochastic analysis on the different kinds of noise based on the average edge length. The quantitative results demonstrate that the performance of our method is better compared to state-of-the-art smoothing approaches.
Emerging non-volatile memory (NVM)-based Computing-in-Memory (CiM) architectures show substantial promise in accelerating deep neural networks (DNNs) due to their exceptional energy efficiency. ...However, NVM devices are prone to device variations. Consequently, the actual DNN weights mapped to NVM devices can differ considerably from their targeted values, inducing significant performance degradation. Many existing solutions aim to optimize average performance amidst device variations, which is a suitable strategy for general-purpose conditions. However, the worst-case performance that is crucial for safety-critical applications is largely overlooked in current research. In this study, we define the problem of pinpointing the worst-case performance of CiM DNN accelerators affected by device variations. Additionally, we introduce a strategy to identify a specific pattern of the device value deviations in the complex, high-dimensional value deviation space, responsible for this worst-case outcome. Our findings reveal that even subtle device variations can precipitate a dramatic decline in DNN accuracy, posing risks for CiM-based platforms in supporting safety-critical applications. Notably, we observe that prevailing techniques to bolster average DNN performance in CiM accelerators fall short in enhancing worst-case scenarios. In light of this issue, we propose a novel worst-case-aware training technique named A-TRICE that efficiently combines adversarial training and noise-injection training with right-censored Gaussian noise to improve the DNN accuracy in the worst-case scenarios. Our experimental results demonstrate that A-TRICE improves the worst-case accuracy under device variations by up to 33%.
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