scholarly journals Physical Distancing Device with Edge Computing for COVID-19 (PADDIE-C19)

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 279
Author(s):  
Chun Hoe Loke ◽  
Mohammed Sani Adam ◽  
Rosdiadee Nordin ◽  
Nor Fadzilah Abdullah ◽  
Asma Abu-Samah
Keyword(s):  
Neural Network ◽  
Close Contact ◽  
Low Cost ◽  
Absolute Error ◽  
Face Mask ◽  
Physical Distance ◽  
Edge Computing ◽  
Computing Device ◽  
Person Detection ◽  
The Neural Network

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.

scholarly journals Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

2021 ◽  
Vol 12 (4) ◽  
pp. 178
Author(s):  
Gilles Van Van Kriekinge ◽  
Cedric De De Cauwer ◽  
Nikolaos Sapountzoglou ◽  
Thierry Coosemans ◽  
Maarten Messagie
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Negative Impact ◽  
Absolute Error ◽  
Electric Grid ◽  
Electric Vehicle Charging ◽  
The Neural Network ◽  
The Mean ◽  
Ev Charging ◽  
Charging Demand

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.

scholarly journals Mind-Wave Controlled Robot: An Arduino Robot Simulating the Wheelchair for Paralyzed Patients

2018 ◽  
Vol 1 (1) ◽  
pp. 6
Author(s):  
Chi Hang Cheng ◽  
Shuai Li ◽  
Seifedine Kadry
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Eeg Signal ◽  
The Neural Network ◽  
Lead Electrode ◽  
Short Period ◽  
Specific Position ◽  
And Control

This project attempts to implement an Arduino robot to simulate a brainwave-controlled wheelchair for paralyzed patients with an improved controlling method. The robot should be able to move freely in anywhere under the control of the user and it is not required to predefine any map or path. An accurate and natural controlling method is provided, and the user can stop the robot any time immediately to avoid risks or danger. This project is using a low-cost and a brainwave-reading headset which has only a single lead electrode (Neurosky mind wave headset) to collect the EEG signal. BCI will be developed by sending the EEG signal to the Arduino Mega and control the movement of the robot. This project used the eye blinking as the robot controlling method as the eye blinking will cause a significant pulse in the EEG signal. By using the neural network to classify the blinking signal and the noise, the user can send the command to control the robot by blinking twice in a short period of time. The robot will be evaluated by driving in different places to test whether it can follow the expected path, avoid the obstacles, and stop in a specific position.

scholarly journals Enhancing protein backbone angle prediction by using simpler models of deep neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fereshteh Mataeimoghadam ◽  
M. A. Hakim Newton ◽  
Abdollah Dehzangi ◽  
Abdul Karim ◽  
B. Jayaram ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Structure Prediction ◽  
Protein Structures ◽  
Absolute Error ◽  
Grand Challenge ◽  
Protein Backbone ◽  
The Neural Network ◽  
Benchmark Datasets ◽  
The Neural Networks

Abstract Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6–8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap.

Prediction Model of Micum Strength for Iron Ore Sinter

2011 ◽  
Vol 402 ◽  
pp. 476-479
Author(s):  
Wei Wang ◽  
Zhi Hui Xu ◽  
Long Long Yang ◽  
Zheng Liang Xue ◽  
Dong Nan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Important Indicator ◽  
The Neural Network ◽  
Iron Ore Sinter ◽  
Real Value ◽  
The Law

Micum strength is an important indicator of quality of sinter; BP artificial neural network model is built to predict the strength of sinter drum. The neural network use the main factors that influence the sinter drum as input data, and output is Micum strength. Experiment results shows that the maximum absolute error between the Micum strength predicted by neural network and real value from the sinter plant is 0.3346, and the average absolute error is 0.1154. These prove that the prediction is accuracy. In addition, because of the "black box" characteristic of the neural network model, the neural network model can not give the law of how the various factors affect the micum strength of sinter ore, this paper also uses the model to analysis the law of how TFe, SiO2 content affect the micum strength. The results not only consist with the sintering theory, but also verify the validity of the model.

Optimization and Analysis for Coke Consumption Based on Clustering

2011 ◽  
Vol 287-290 ◽  
pp. 1112-1115
Author(s):  
Jun Hong Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clustering Analysis ◽  
Coke Consumption ◽  
Prediction Method ◽  
Absolute Error ◽  
Coke Rate ◽  
The Neural Network ◽  
Fuel Rate

In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.

scholarly journals Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
Author(s):  
Zeba Idrees ◽  
Zhuo Zou ◽  
Lirong Zheng
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Real Time ◽  
Low Cost ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Quality Data ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Computing Device

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiali Li ◽  
Zhengyu Lv ◽  
Bo Liu ◽  
Licheng Wu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Computer Game ◽  
Feature Learning ◽  
Superior Performance ◽  
Computing Power ◽  
Unit Structure ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Hidden Layer

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use.

Research on the BPNN in the Prediction of PMV

2010 ◽  
Vol 29-32 ◽  
pp. 2804-2808 ◽  
Author(s):  
Jian Yao ◽  
Jin Xu
Keyword(s):  
Neural Network ◽  
Iterative Process ◽  
Absolute Error ◽  
Mean Radiant Temperature ◽  
Backpropagation Neural Network ◽  
Air Velocity ◽  
The Neural Network ◽  
Radiant Temperature ◽  
Prediction Approach ◽  
Bpnn Model

In view of the problem that it is difficult to calculate the Fanger’s PMV equation due to its complicated iterative process, a backpropagation neural network (BPNN) model was built to predict PMV. Air temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate and clothing index were used as the input of neural network and PMV output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy absolute error below 5%. As a conclusion, this study has a real significance, because it gives a new method with reliability and accuracy in the prediction of PMV.

scholarly journals Improving flood forecasting in Bangladesh using an artificial neural network

2009 ◽  
Vol 12 (3) ◽  
pp. 351-364 ◽  
Author(s):  
A. S. Islam
Keyword(s):  
Neural Network ◽  
Absolute Error ◽  
Flood Forecasting ◽  
Computational Time ◽  
Output Node ◽  
River Stage ◽  
The Neural Network ◽  
Field Data Collection ◽  
The Ganges ◽  
Very High

A river stage neural network model has been developed to study and predict the water level of Dhaka city. A total of five stations located at the border area of Bangladesh on the Ganges, Brahmaputra and Meghna rivers are selected as input nodes and Dhaka on the Buriganga river is the output node for the neural network. This model is trained with river stage data for a period of 1998 to 2004 and validated with data from 2005 to 2007. The river stage of Dhaka has been predicted for up to ten days with very high accuracy. Values of R2, root mean square and mean absolute error are found ranging from 0.537 to 0.968, 0.607 m to 0.206 m and 0.475 m to 0.154 m, respectively, during training and validation of the model. The results of this study can be useful for real-time flood forecasting by reducing computational time, improving water resources management and reducing the unnecessary cost of field data collection.

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