scholarly journals Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6389
Author(s):  
Kyriakos Koritsoglou ◽  
Vasileios Christou ◽  
Georgios Ntritsos ◽  
Georgios Tsoumanis ◽  
Markos G. Tsipouras ◽  
...  
Keyword(s):  
Temperature Sensor ◽  
Nonlinear Response ◽  
Low Cost ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Generalization Error ◽  
Acceptable Error ◽  
The Mean ◽  
Digital Temperature Sensor ◽  
Monitoring Devices

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

scholarly journals Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1038 ◽  
Author(s):  
Fernando Delgado-Ramos ◽  
Carmen Hervás-Gámez
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Absolute Error ◽  
Integrated Water Resources Management ◽  
Coefficient Of Determination ◽  
Water Volume ◽  
Cumulative Probability ◽  
Water Users ◽  
The Mean ◽  
The Right

Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasting monthly and yearly streamflows during the current hydrological year, which is applicable to headwater catchments. The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model performance statistics metrics (including the Coefficient of Determination R2; the Root-Mean-Square Error RMSE; the Mean Absolute Error MAE; the Index of Agreement IOA; the Mean Absolute Percent Error MAPE; the Coefficient of Nash-Sutcliffe Efficiency NSE; and the Inclusion Coefficient IC) were used and the results showed good levels of accuracy (improving as the number of observed months increases). The model forecast outputs are the mean monthly and yearly streamflows along with the 10th and 90th percentiles. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain, achieving an accuracy of 92% and 80% in March 2017. These risk-based predictions are of great value, especially before the intensive irrigation campaign starts in the middle of the hydrological year, when Water Authorities have to ensure that the right decision is made on how to best allocate the available water volume between the different water users and environmental needs.

scholarly journals Inter-Floor Noise Monitoring System for Multi-Dwelling Houses Using Smartphones

2020 ◽  
Vol 12 (12) ◽  
pp. 5065
Author(s):  
Suhyun Kang ◽  
Seungho Kim ◽  
Dongeun Lee ◽  
Sangyong Kim
Keyword(s):  
Noise Source ◽  
Mean Absolute Error ◽  
Low Cost ◽  
Monitoring And Evaluation ◽  
Absolute Error ◽  
Maximum Error ◽  
Sound Level ◽  
Apartment Buildings ◽  
The Mean

The noise between the floors in apartment buildings is becoming a social problem, and the number of disputes related to it are increasing every year. However, laypersons will find it difficult to use the sound level meters because they are expensive, delicate, bulky, etc. Therefore, this study proposes a system to monitor the noise between the floors, that will measure the sound and estimate the location of the noise using the sensors and applications in smartphones. To evaluate how this system can be used effectively within an apartment building, a case study has been performed to verify its validity. The result shows that the mean absolute error (MAE) between the actual noise generating position and the estimated noise source location was measured at 2.8 m, with a minimum error of 1.2 m and a maximum error of 4.3 m. This means that smartphones, in the future, can be used as low-cost monitoring and evaluation devices to measure the noise between the floors in apartment buildings.

scholarly journals Air-to-Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Zhang ◽  
Jinxiao Wen ◽  
Guanshu Yang ◽  
Zunwen He ◽  
Xinran Luo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
Low Cost ◽  
Path Loss ◽  
Absolute Error ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Prediction Errors

Recently, unmanned aerial vehicle (UAV) plays an important role in many applications because of its high flexibility and low cost. To realize reliable UAV communications, a fundamental work is to investigate the propagation characteristics of the channels. In this paper, we propose path loss models for the UAV air-to-air (AA) scenario based on machine learning. A ray-tracing software is employed to generate samples for multiple routes in a typical urban environment, and different altitudes of Tx and Rx UAVs are taken into consideration. Two machine-learning algorithms, Random Forest and KNN, are exploited to build prediction models on the basis of the training data. The prediction performance of trained models is assessed on the test set according to the metrics including the mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, two empirical models are presented for comparison. It is shown that the machine-learning-based models are able to provide high prediction accuracy and acceptable computational efficiency in the AA scenario. Moreover, Random Forest outperforms other models and has the smallest prediction errors. Further investigation is made to evaluate the impacts of five different parameters on the path loss. It is demonstrated that the path visibility is crucial for the path loss.

Fabrication of 3D Temperature Sensor Using Magnetostrictive Inkjet Printhead

2020 ◽  
Vol 64 (5) ◽  
pp. 50405-1-50405-5
Author(s):  
Young-Woo Park ◽  
Myounggyu Noh
Keyword(s):  
Flexible Electronics ◽  
Inkjet Printing ◽  
Temperature Sensor ◽  
Computer Aided Design ◽  
Low Cost ◽  
Three Dimensional ◽  
Drop On Demand ◽  
Aided Design ◽  
Nanoparticle Ink ◽  
Inkjet Printhead

Abstract Recently, the three-dimensional (3D) printing technique has attracted much attention for creating objects of arbitrary shape and manufacturing. For the first time, in this work, we present the fabrication of an inkjet printed low-cost 3D temperature sensor on a 3D-shaped thermoplastic substrate suitable for packaging, flexible electronics, and other printed applications. The design, fabrication, and testing of a 3D printed temperature sensor are presented. The sensor pattern is designed using a computer-aided design program and fabricated by drop-on-demand inkjet printing using a magnetostrictive inkjet printhead at room temperature. The sensor pattern is printed using commercially available conductive silver nanoparticle ink. A moving speed of 90 mm/min is chosen to print the sensor pattern. The inkjet printed temperature sensor is demonstrated, and it is characterized by good electrical properties, exhibiting good sensitivity and linearity. The results indicate that 3D inkjet printing technology may have great potential for applications in sensor fabrication.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals A Digital Improvement—Trimming a Digital Temperature Sensor with EEPROM Reprogrammable Fuses

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1700
Author(s):  
Anca Mihaela Vasile (Dragan) ◽  
Alina Negut ◽  
Adrian Tache ◽  
Gheorghe Brezeanu
Keyword(s):  
Standard Deviation ◽  
Power Supply ◽  
Temperature Sensor ◽  
Experimental Results ◽  
Temperature Error ◽  
Thermal Sensor ◽  
Memory Cells ◽  
Temperature Range ◽  
Read Only Memory ◽  
Digital Temperature Sensor

An EEPROM (electrically erasable programmable read-only memory) reprogrammable fuse for trimming a digital temperature sensor is designed in a 0.18-µm CMOS EEPROM. The fuse uses EEPROM memory cells, which allow multiple programming cycles by modifying the stored data on the digital trim codes applied to the thermal sensor. By reprogramming the fuse, the temperature sensor can be adjusted with an increased trim variation in order to achieve higher accuracy. Experimental results for the trimmed digital sensor showed a +1.5/−1.0 ℃ inaccuracy in the temperature range of −20 to 125 ℃ for 25 trimmed DTS samples at 1.8 V by one-point calibration. Furthermore, an average mean of 0.40 ℃ and a standard deviation of 0.70 ℃ temperature error were obtained in the same temperature range for power supply voltages from 1.7 to 1.9 V. Thus, the digital sensor exhibits similar performances for the entire power supply range of 1.7 to 3.6 V.

scholarly journals Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 72
Author(s):  
Li Wang ◽  
Yong Zhou ◽  
Qing Li ◽  
Tao Xu ◽  
Zhengxiang Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Index System ◽  
Quality Index ◽  
Hubei Province ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Soil Conditions ◽  
The Mean ◽  
County Scale ◽  
Assessment Results

Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

Sign in / Sign up

Export Citation Format

Share Document