scholarly journals Towards low-cost machine learning solutions for manufacturing SMEs

AI & Society ◽  
2021 ◽  
Author(s):  
Jan Kaiser ◽  
German Terrazas ◽  
Duncan McFarlane ◽  
Lavindra de Silva
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Low Cost ◽  
Parameter Tuning ◽  
Simple Solution ◽  
Data Preparation ◽  
Comprehensive Understanding ◽  
Manufacturing Environment ◽  
Extensive Data ◽  
Learning Capabilities

AbstractMachine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.

scholarly journals Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion

2019 ◽  
Vol 33 ◽  
pp. 3846-3853 ◽  
Author(s):  
Yi-Qi Hu ◽  
Yang Yu ◽  
Wei-Wei Tu ◽  
Qiang Yang ◽  
Yuqiang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Series Expansion ◽  
Low Cost ◽  
Parameter Tuning ◽  
Small Data ◽  
Full Dataset ◽  
Learning Tasks ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Full Power

Automatic machine learning (AutoML) aims at automatically choosing the best configuration for machine learning tasks. However, a configuration evaluation can be very time consuming particularly on learning tasks with large datasets. This limitation usually restrains derivative-free optimization from releasing its full power for a fine configuration search using many evaluations. To alleviate this limitation, in this paper, we propose a derivative-free optimization framework for AutoML using multi-fidelity evaluations. It uses many lowfidelity evaluations on small data subsets and very few highfidelity evaluations on the full dataset. However, the lowfidelity evaluations can be badly biased, and need to be corrected with only a very low cost. We thus propose the Transfer Series Expansion (TSE) that learns the low-fidelity correction predictor efficiently by linearly combining a set of base predictors. The base predictors can be obtained cheaply from down-scaled and experienced tasks. Experimental results on real-world AutoML problems verify that the proposed framework can accelerate derivative-free configuration search significantly by making use of the multi-fidelity evaluations.

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

Causes of gear pump fails and methods for their elimination

2020 ◽  
pp. 98-107
Author(s):  
Vitaliy A. Zuyevskiy ◽  
Daniil O. Klimyuk ◽  
Ivan A. Shemberev
Keyword(s):  
Adhesion Strength ◽  
Production Systems ◽  
Low Cost ◽  
High Strength ◽  
Strength Properties ◽  
Research Purpose ◽  
Working Fluid ◽  
Gear Pump ◽  
Repair Service ◽  
Recovery Method

Gear pumps are an important element of many production systems and their replacement in case of failure can be quite expensive, so it is important to have a modern and well-tuned technology for their recovery. There are many methods for restoring the pump's performance, depending on the reason that led to its failure. (Research purpose) The research purpose is in determining what causes most often lead to loss of pump performance, and developing a recovery method that provides the greatest post-repair service life of the pump and low cost of repair. (Materials and methods) Authors took into account that the applied coatings must have sufficient adhesion strength and resistance to mechanical, thermal and corrosion loads during operation. It was found that most often significant leaks of the working fluid, leading to failure, occur due to an increase in the gap between the inner surface of the housing and the gears due to active wear of the housing wells. Authors determined that the method of electric spark treatment of worn-out housing wells is best suited to perform the task (a large post-repair resource and low costs). (Results and discussion) It was found by laboratory studies of the adhesion strength of electric spark coatings with various electrodes that the best transfer of the material to the substrate is provided by bronze electrodes BrMKts3-1. It was noted that the coatings applied using the BrMKts3-1 electrode have high strength properties. (Conclusions) Research conducted in the center for collective use "Nano-Center" VIM confirmed the possibility of effective recovery of the gear pump by electric spark treatment.

RSSI Data Preparation for Machine Learning

2020 ◽  
Author(s):  
Dodo Zaenal Abidin ◽  
Siti Nurmaini ◽  
Reza Firsandava Malik ◽  
Erwin ◽  
Errissya Rasywir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Preparation

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

A novel 3D porous electrode of polyaniline and PEDOT:PSS coated SiNWs for low-cost and high-performance supercapacitors

2021 ◽  
Author(s):  
Xiaojuan Shen ◽  
Xuan Zhang ◽  
Tongfei Wang ◽  
Songjun Li ◽  
Zhaoqiang Li
Keyword(s):  
High Performance ◽  
Solution Method ◽  
In Situ Polymerization ◽  
Low Cost ◽  
Porous Electrode ◽  
Simple Solution ◽  
Porous Si ◽  
Supercapacitor Electrode

In this study, a novel 3D porous Si-based supercapacitor electrode was developed by the simple solution method, which involved firstly the in-situ polymerization of polyaniline particles (PANI) on the Si...

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