scholarly journals TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH

2021 ◽  
Author(s):  
Enrico Tabanelli ◽  
Davide Brunelli ◽  
Luca Benini ◽  
Andrea Acquaviva
Keyword(s):  
Cost Reduction ◽  
State Of The Art ◽  
Low Cost ◽  
Feature Space ◽  
Low Latency ◽  
Voltage Sensors ◽  
Global Power ◽  
Feature Spaces ◽  
Load Monitoring ◽  
Speed Up

Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline’s implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.

scholarly journals TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH

2021 ◽  
Author(s):  
Enrico Tabanelli ◽  
Davide Brunelli ◽  
Luca Benini ◽  
Andrea Acquaviva
Keyword(s):  
Cost Reduction ◽  
State Of The Art ◽  
Low Cost ◽  
Feature Space ◽  
Low Latency ◽  
Voltage Sensors ◽  
Global Power ◽  
Feature Spaces ◽  
Load Monitoring ◽  
Speed Up

Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline’s implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.

Redundancy-Aware Pruning of Convolutional Neural Networks

2020 ◽  
Vol 32 (12) ◽  
pp. 2532-2556
Author(s):  
Guotian Xie
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Orthogonal Transformation ◽  
Feature Space ◽  
Intermediate Space ◽  
Pruning Method ◽  
Input And Output ◽  
Speed Up ◽  
Original Feature

Pruning is an effective way to slim and speed up convolutional neural networks. Generally previous work directly pruned neural networks in the original feature space without considering the correlation of neurons. We argue that such a way of pruning still keeps some redundancy in the pruned networks. In this letter, we proposed to prune in the intermediate space in which the correlation of neurons is eliminated. To achieve this goal, the input and output of a convolutional layer are first mapped to an intermediate space by orthogonal transformation. Then neurons are evaluated and pruned in the intermediate space. Extensive experiments have shown that our redundancy-aware pruning method surpasses state-of-the-art pruning methods on both efficiency and accuracy. Notably, using our redundancy-aware pruning method, ResNet models with three times the speed-up could achieve competitive performance with fewer floating point operations per second even compared to DenseNet.

scholarly journals Star Topology Convolution for Graph Representation Learning

2020 ◽  
Author(s):  
Chong Wu ◽  
Zhenan Feng ◽  
Jiangbin Zheng ◽  
Houwang Zhang ◽  
Jiawang Cao ◽  
...  
Keyword(s):  
Protein Identification ◽  
State Of The Art ◽  
Feature Space ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolution Kernel ◽  
Star Topology ◽  
Identification Methods ◽  
Feature Spaces ◽  
Benchmark Datasets

<div><div><div><p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional in neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral convolution methods, this method learns subgraphs which have a star topology rather than a fixed graph. It has fewer parameters in its convolution kernel and is inductive so that it is more flexible and can be applied to large and evolving graphs. As for CNNs in Euclidean feature spaces, the convolution kernel is localized and maintains good sharing. By increasing the depth of a layer, the method can learn lobal features like a CNN. To validate the method, STC was compared to state-of-the-art spectral convolution and spatial convolution methods in a supervised learning setting on three benchmark datasets: Cora, Citeseer and Pubmed. The experimental results show that STC outperforms the other methods. STC was also applied to protein identification tasks and outperformed traditional and advanced protein identification methods.</p></div></div></div>

Low-Cost Pemfc Development at Siemens - Material Aspects

1999 ◽  
Vol 575 ◽  
Author(s):  
M. Waidhas ◽  
A. Datz ◽  
U. Gebhardt ◽  
R. vhelmolt ◽  
R. Hornung ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Mobile Applications ◽  
Cost Reduction ◽  
State Of The Art ◽  
Low Cost ◽  
Pem Fuel Cell ◽  
Cell Systems

ABSTRACTA state of the art review is given on conceivable concepts of cost reduction for PEM fuel cell systems with specific respect to mobile applications. Achieved results at Siemens are described and will be taken as a basis to assess how close this technology is to the market.

scholarly journals AN EFFICIENT METHOD TO DETECT MUTUAL OVERLAP OF A LARGE SET OF UNORDERED IMAGES FOR STRUCTURE-FROM-MOTION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 191-198 ◽  
Author(s):  
X. Wang ◽  
Z.Q. Zhan ◽  
C. Heipke
Keyword(s):  
Structure From Motion ◽  
Low Cost ◽  
Feature Space ◽  
New Method ◽  
Large Set ◽  
General Applicability ◽  
Nearest Neighbours ◽  
Speed Up ◽  
Common Block ◽  
Overlap Graphs

Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge.<br><br> We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.

Review on biomass feedstocks, pyrolysis mechanism and physicochemical properties of biochar: State-of-the-art framework to speed up vision of circular bioeconomy

2021 ◽  
Vol 297 ◽  
pp. 126645
Author(s):  
Gajanan Sampatrao Ghodake ◽  
Surendra Krushna Shinde ◽  
Avinash Ashok Kadam ◽  
Rijuta Ganesh Saratale ◽  
Ganesh Dattatraya Saratale ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
State Of The Art ◽  
Pyrolysis Mechanism ◽  
Biomass Feedstocks ◽  
Speed Up

scholarly journals UV Inscription and Pressure Induced Long-Period Gratings through 3D Printed Amplitude Masks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1977
Author(s):  
Ricardo Oliveira ◽  
Liliana M. Sousa ◽  
Ana M. Rocha ◽  
Rogério Nogueira ◽  
Lúcia Bilro
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Resonance Wavelength ◽  
Complex Structures ◽  
Pressing Method ◽  
Long Period ◽  
Long Period Gratings ◽  
3D Printed ◽  
Mechanical Pressing ◽  
First Time

In this work, we demonstrate for the first time the capability to inscribe long-period gratings (LPGs) with UV radiation using simple and low cost amplitude masks fabricated with a consumer grade 3D printer. The spectrum obtained for a grating with 690 µm period and 38 mm length presented good quality, showing sharp resonances (i.e., 3 dB bandwidth < 3 nm), low out-of-band loss (~0.2 dB), and dip losses up to 18 dB. Furthermore, the capability to select the resonance wavelength has been demonstrated using different amplitude mask periods. The customization of the masks makes it possible to fabricate gratings with complex structures. Additionally, the simplicity in 3D printing an amplitude mask solves the problem of the lack of amplitude masks on the market and avoids the use of high resolution motorized stages, as is the case of the point-by-point technique. Finally, the 3D printed masks were also used to induce LPGs using the mechanical pressing method. Due to the better resolution of these masks compared to ones described on the state of the art, we were able to induce gratings with higher quality, such as low out-of-band loss (0.6 dB), reduced spectral ripples, and narrow bandwidths (~3 nm).

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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