Multi-Modeshape Reservoir Computing Using a Continuous MEMS Microbeam

2021 ◽  
Author(s):  
Mohammad H. Hasan ◽  
Fadi Alsaleem
Keyword(s):  
Time Series ◽  
Success Rate ◽  
Classification Task ◽  
Reservoir Computing ◽  
Computational Power ◽  
Virtual Node ◽  
Nonlinear Complexity ◽  
Novel Approach ◽  
Mems Device

Abstract Delay-based Reservoir computing (RC) offers great potential in time-series problems, especially when applied in hardware due to its low computational power and its compact nature. However, this approach suffers from a large computational delay because of the serial probing of virtual nodes. To address this disadvantage, this paper presents the use of a continuous MEMS arch for Delay-based RC. This novel approach reduces the computational delay by using fewer virtual nodes through maintaining sufficient virtual node coupling and nonlinear complexity. As a demonstration, we show that a single MEMS arch is capable of performing a binary waveform classification task of a multi-frequency square-and-triangle waveform problem with a success rate > 96% using only 10 virtual nodes compared to 40 virtual nodes in a typical implementation. The reduction in the number of virtual neurons is achieved by biasing the MEMS device using an AC source around its second modeshape.

A Novel Approach for the Treatment of Sialolithiasis that Preserves Salivary Duct Anatomy

2021 ◽  
pp. 000348942110189
Author(s):  
Gani Atilla Şengör ◽  
Ahmet Mert Bilgili
Keyword(s):  
Salivary Gland ◽  
Success Rate ◽  
Cost Effective ◽  
Complete Removal ◽  
Level Of Evidence ◽  
Invasive Approach ◽  
Pneumatic Lithotripsy ◽  
Novel Approach ◽  
Surgical Methods ◽  
Salivary Gland Stones

Objective: The sialendoscopy era in the treatment of salivary gland stones has reduced the use of classical surgical methods. However, the miniature ducts and tools may cause difficulties in removing large sialoliths. Therefore, invasive combined oral surgeries or gland resection may be considered. We searched for the most suitable method in order to stay in line with the minimally invasive approach that preserves the ductus anatomy, and that can reduce the surgical fears of patients. Materials and Methods: The study included 84 cases (23 parotid and 61 submandibular) in whom stones were fragmented by pneumatic lithotripsy and removed between January 2015 and January 2020. The parotid cases comprised 7 females and 16 males, and the submandibular cases comprised 25 females and 36 males. Intraductal lithotripsy was performed using pneumatic lithotripter. This study has fourth level of evidence. Results: Based on total number of cases (n = 84), success rate was 67/84 (79.7%) immediately after sialendoscopy, and overall success rate was 77/84 (91.6%). Based on number of stones treated (n = 111), our immediate success rate was 94/111 (84.6%), and overall success rate was 104/111 (93.7%). The success criteria were complete removal of the stone and fragments in a single sialendoscopy procedure and resolution of symptoms. Conclusions: We successfully treated salivary gland stones, including L3b stones, in our patient cohort with sialendoscopy combined with pneumatic lithotripsy. The lithotripsy method that we have adapted seems to be more useful and cost-effective compared to its alternatives. We were also able to preserve the ductus anatomy and relieve patients’ concerns. Level of Evidence: Level IV

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

Wi-Fi location fingerprinting using an intelligent checkpoint sequence

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Günther Retscher ◽  
Hannes Hofer
Keyword(s):  
Success Rate ◽  
Mobile Device ◽  
Processing Time ◽  
Signal Strength ◽  
Reference Points ◽  
Regular Grid ◽  
Correct Order ◽  
Location Fingerprinting ◽  
Novel Approach ◽  
Logical Sequence

AbstractFor Wi-Fi positioning location fingerprinting is very common but has the disadvantage that it is very labour consuming for the establishment of a database (DB) with received signal strength (RSS) scans measured on a large number of known reference points (RPs). To overcome this drawback a novel approach is developed which uses a logical sequence of intelligent checkpoints (iCPs) instead of RPs distributed in a regular grid. The iCPs are the selected RPs which have to be passed along the way for navigation from a start point A to the destination B. They are twofold intelligent because of the fact that they depend on their meaningful selection and because of their logical sequence in their correct order. Thus, always the following iCP is known due to a vector graph allocation in the DB and only a small limited number of iCPs needs to be tested when matching the current RSS scans. This reduces the required processing time significantly. It is proven that the iCP approach achieves a higher success rate than conventional approaches. In average correct matching results of 90.0% were achieved using a joint DB including RSS scans of all employed smartphones. An even higher success rate is achieved if the same mobile device is used in both the training and positioning phase.

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

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