Contaminant classification using cosine distances based on multiple conventional sensors

2015 ◽  
Vol 17 (2) ◽  
pp. 343-350 ◽  
Author(s):  
Shuming Liu ◽  
Han Che ◽  
Kate Smith ◽  
Tian Chang
Keyword(s):  
Real Time ◽  
Euclidean Distance ◽  
Classification Method ◽  
Contaminant Concentration ◽  
Different Types ◽  
Using Data

This paper proposes a new contaminant classification method to discriminate contaminants in a real time manner, independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from injection experiments and compared with a Euclidean distance-based method.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Real-time personalized cardiovascular monitoring system with arrhythmia classification method

2020 ◽  
Author(s):  
Ying-Xian Fu ◽  
Zhi-Min Yu ◽  
Ming-Hui Fan ◽  
Pao-Cheng Huang ◽  
Liang-Hung Wang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Classification Method ◽  
Cardiovascular Monitoring

scholarly journals Digital Twin: Origin to Future

2021 ◽  
Vol 4 (2) ◽  
pp. 36
Author(s):  
Maulshree Singh ◽  
Evert Fuenmayor ◽  
Eoin Hinchy ◽  
Yuansong Qiao ◽  
Niall Murray ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Access ◽  
Physical Entity ◽  
Industrial Systems ◽  
Digital Twin ◽  
Planning Optimization ◽  
Different Types ◽  
Pros And Cons ◽  
Number Of Publications ◽  
Digital Shadow

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

Real Time Evaluation of Different Types of Bio-neurological Signals

2007 ◽  
Author(s):  
Tao Zhang ◽  
Takenao Sugi ◽  
Hiroshi Shibasaki ◽  
Masatoshi Nakamura
Keyword(s):  
Real Time ◽  
Different Types ◽  
Time Evaluation

Advanced Driver Assists and Safety Management System

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Dipti Chavan ◽  
Aniket Kamble ◽  
Aditya Khadsare ◽  
Vaibhav Chougule ◽  
Vaibhav Chougule
Keyword(s):  
Real Time ◽  
Automobile Industry ◽  
Safety Management ◽  
Public Utilities ◽  
Sensor Platform ◽  
Key Points ◽  
Different Types ◽  
Important Field ◽  
And Control ◽  
Web Platform

Electronics and communication is the most important field. In this paper, we can describe how much safety is in the Automobile industry. In this paper, we are using uno-Arduino. The different types of sensors facilities are also provided using key points. The different sensors are provided to check visitor count. In this system, we can monitor and control all the safety precautions their one IoT web platform. This helps in the proper utilization of drivers and helps in avoiding accidents. This paper can be implemented in any two-wheelers, heavily loaded trucks, small SUVs, compact cars. In our paper, the electronics machine/components will be automatically working with using of Arduino program. The proposed wireless sensor platform is an attempt to develop more safety devices that can be used in multiple areas such as homes, schools, and public utilities to reduce accidents. This Advanced Driver Assists system will provide real-time accident detections and monitoring usage information that helps in real-time by using GSM, GPS, and sensors.

Association between different types of plant-based diet and dyslipidemia in Korean adults

2021 ◽  
pp. 1-17
Author(s):  
Nara Shin ◽  
Jihye Kim
Keyword(s):  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Multivariable Logistic Regression Analysis ◽  
Nutrition Survey ◽  
Korean Adults ◽  
Different Types ◽  
Low Hdl ◽  
Nationally Representative ◽  
Using Data

Abstract This study investigated the association between the different types of plant-based diets and dyslipidemia in Korean adults using data from the nationally representative sample. Using the 2012-2016 Korea National Health and Nutrition Survey data, a total of 14,167 adults (≥19 years old) participated in this study. Dietary intake was assessed by a semi-quantitative food frequency questionnaire. Three different plant-based diet indices (overall plant-based diet index (PDI), healthful plant-based diet index (hPDI), unhealthful plant-based diet index (uPDI)), were calculated. Dyslipidemia and its components (hypertriglyceridemia, hypercholesterolemia, low high-density lipoprotein cholesterol (HDL-C), high low-density lipoprotein cholesterol (LDL-C), use of anti-hyperlipidemia agent) were measured. Multivariable logistic regression analysis was used to examine the associations between plant-based diet and dyslipidemia and individual lipid disorders. Totally, 47% of overall population had dyslipidemia. Individual in the highest quintile of uPDI had 22% greater odds of dyslipidemia (95% CI: 1·05, 1·41) and 48 % higher odds of hypertriglyceridemia (95% CI: 1·21, 1·81) and 16% higher odds of low HDL-C (OR: 1·16, 95% CI: 1·00, 1·35) than those in the lowest quintile of uPDI. PDI was associated with 16 % higher odds of low HDL-C and hPDI were associated with 25% lower odds of high LDL-C. However, Neither PDI nor hPDI was significantly associated with the prevalence of dyslipidemia. Greater adherence to unhealthful plant-based diets was associated with greater odds of the dyslipidemia and its components suggesting the importance of the quality of plant-based diet in South Korean adults for dyslipidemia prevention.

Programming in Restrictive Housing: Considerations for Improving Outcome Evaluations

2018 ◽  
Vol 45 (8) ◽  
pp. 1174-1191 ◽  
Author(s):  
H. Daniel Butler ◽  
Starr Solomon ◽  
Ryan Spohn
Keyword(s):  
Treatment Effect ◽  
Successful Implementation ◽  
Inmate Misconduct ◽  
The Past ◽  
What Works ◽  
Different Types ◽  
Time Served ◽  
Using Data ◽  
Outcome Evaluations ◽  
Behavioral Programming

A number of studies have identified “what works” in regard to the successful implementation of correctional programming over the past several decades. Few studies, however, have examined the complexities associated with programming in restrictive housing. Using data from a Midwestern department of corrections, we examined whether the provision of programming in restrictive housing achieved desired outcomes (e.g., reductions in inmate misconduct). The findings revealed the amount of time served in restrictive housing and confinement in different types of restrictive housing may influence estimations of a treatment effect. As a growing number of states seek to reform the use of restrictive housing, the proper implementation of cognitive-behavioral programming may increase institutional security and safety.

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