scholarly journals Construction of a Nonlinear Model of Tourism Economy Forecast Based on Wireless Sensor Network from the Perspective of Digital Economy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jun Liu ◽  
Faxian Jia
Keyword(s):  
Prediction Model ◽  
Nonlinear Model ◽  
Circular Economy ◽  
Wireless Sensors ◽  
Regional Economy ◽  
Prediction Models ◽  
High Reliability ◽  
Wireless Sensor ◽  
Hysteresis Nonlinearity ◽  
Tourism Economy

With the outbreak of the new crown epidemic, the world economy has been severely tested, making predictions more difficult. Wireless sensors have the advantages of low cost, ease of use, high reliability, and high safety and have been widely used in the tourism economy. In order to understand the ability of wireless sensors to predict the regional economy, this article uses an example to construct a nonlinear model of wireless sensors to predict the regional economy. With the continuous development of the concept of circular economy, circular economy has gradually been recognized by Chinese scholars and practitioners. After domestic scholars continue to study the theory of circular economy, practicing the concept of circular economy and taking the road of sustainable development have become one of the important directions of the development of industrial theory. Literature analysis and other methods were used to conduct research on databases such as CNKI, Wan fang Database, and SSCI. Literature was collected, and GIS spatial analysis technology was used to analyze different areas and finally get a prediction model. The phenomenon is nonlinearity (such as saturation nonlinearity in the magnetic circuit), and some are caused by the nonlinear relationship between system variables (such as linear resistance and squared nonlinearity between current and power) and some artificially introduced nonlinear links (such as the hysteresis nonlinearity of relays). Experiments have proved that there is a certain error between the prediction model and the actual result; the error value is about 9%, which is less than the value of other prediction models. This shows that the output results of the nonlinear model of wireless sensor regional economic prediction should be processed reasonably. This result has a certain reference value, and its output should be combined with the actual situation. Related research found that under the nonlinear model, the more accurate and comprehensive the input value is, the closer the output result is to the actual value.

Wireless Sensing of Physical Parameters Inside Hermetically Enclosed Conductive Envelopes

2007 ◽  
Author(s):  
Martin Kluge ◽  
Jordi Sabater ◽  
Josef Schalk ◽  
Luong V. Ngo ◽  
Helmut Seidel ◽  
...  
Keyword(s):  
Electromagnetic Waves ◽  
Wireless Sensors ◽  
High Reliability ◽  
Aerospace Industry ◽  
Wireless Sensor ◽  
Physical Parameters ◽  
Wireless Sensing ◽  
Fuel Tanks ◽  
Radio Transmitters ◽  
High Level

In the modern aeronautics and aerospace industry, there is a manifold amount of applications emerging for wireless sensors. While many new systems are making use of radio transmitters, EADS Innovation Works has developed a concept for transmitting energy and data to the inside of hermetically sealed envelopes used for hydraulic accumulators, fuel tanks, oxygen bottles, etc. For such kind of metal enclosures, the use of radio frequency is impossible as the electromagnetic waves are blocked by the surrounding material. Classical approaches like using wire-based feed-throughs threaten the reliability of the overall system and hence, they are less attractive especially when safety relevant components are targeted. The system described in this paper makes use of ultrasonic transmission techniques in order to power and communicate with a wireless sensor inside a metal enclosure. An innovative platform and communication concept allows to efficiently read data from basically any type of low power commercial sensors of the shelf. Major design drivers for the overall system are a high level of integration and high reliability.

Implementation of a Cyber-Physical System Using Wireless Sensor Networks for Monitoring Patients

2015 ◽  
Vol 1 ◽  
pp. 26 ◽  
Author(s):  
Vo Que Son ◽  
Do Tan A
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Physical System ◽  
Patient Monitoring ◽  
High Reliability ◽  
Vital Signs ◽  
Wireless Sensor ◽  
Cyber Physical System ◽  
Self Administration ◽  
Hospital Units

Sensing, distributed computation and wireless communication are the essential building components of a Cyber-Physical System (CPS). Having many advantages such as mobility, low power, multi-hop routing, low latency, self-administration, utonomous data acquisition, and fault tolerance, Wireless Sensor Networks (WSNs) have gone beyond the scope of monitoring the environment and can be a way to support CPS. This paper presents the design, deployment, and empirical study of an eHealth system, which can remotely monitor vital signs from patients such as body temperature, blood pressure, SPO2, and heart rate. The primary contribution of this paper is the measurements of the proposed eHealth device that assesses the feasibility of WSNs for patient monitoring in hospitals in two aspects of communication and clinical sensing. Moreover, both simulation and experiment are used to investigate the performance of the design in many aspects such as networking reliability, sensing reliability, or end-to-end delay. The results show that the network achieved high reliability - nearly 97% while the sensing reliability of the vital signs can be obtained at approximately 98%. This indicates the feasibility and promise of using WSNs for continuous patient monitoring and clinical worsening detection in general hospital units.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

Predicting Change Prone Classes in Open Source Software

2018 ◽  
Vol 8 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Deepa Godara ◽  
Amit Choudhary ◽  
Rakesh Kumar Singh
Keyword(s):  
Prediction Model ◽  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
New Technology ◽  
Modern Technology ◽  
Time Frequency ◽  
Rigorous Testing ◽  
Technology Changes ◽  
Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

scholarly journals Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

2005 ◽  
Vol 12 (5) ◽  
pp. 459-464 ◽  
Author(s):  
Guizhou Hu ◽  
Martin M. Root
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Empirical Model ◽  
Complex Disease ◽  
Prediction Models ◽  
Longitudinal Research ◽  
Study Data ◽  
Individual Risk ◽  
Data Set

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

CORRECTION OF PREDICTION MODEL OUTPUT–APPLICATION TO GENERAL CORROSION MODEL

2021 ◽  
Vol 156 (A4) ◽  
Author(s):  
N Hifi ◽  
N Barltrop
Keyword(s):  
Prediction Model ◽  
Reliability Analysis ◽  
Model Calibration ◽  
Structural Reliability ◽  
Prediction Models ◽  
General Corrosion ◽  
Model Output ◽  
Reliability Models ◽  
Corrosion Model ◽  
Recorded Data

This paper applies a newly developed methodology to calibrate the corrosion model within a structural reliability analysis. The methodology combines data from experience (measurements and expert judgment) and prediction models to adjust the structural reliability models. Two corrosion models published in the literature have been used to demonstrate the technique used for the model calibration. One model is used as a prediction for a future degradation and a second one to represent the inspection recorded data. The results of the calibration process are presented and discussed.

Detection of moisture and carotenoid content in carrot slices during hot air drying based on multispectral imaging equipment with selected wavelengths

2021 ◽  
Vol 17 (9) ◽  
pp. 727-735
Author(s):  
Jiamei Long ◽  
Jia Yang ◽  
Jing Peng ◽  
Leiqing Pan ◽  
Kang Tu
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Multispectral Imaging ◽  
Prediction Models ◽  
Projection Algorithm ◽  
Carotenoid Content ◽  
Support Vector ◽  
Hot Air Drying ◽  
Air Drying ◽  
Hot Air

Abstract Moisture content and carotenoid content are important indicators for evaluating the drying process of carrot slices. There are growing attention to develop non-destructive methods as effectively analytical tools in quality assurance of drying carrot slices. In this study, the characteristic wavelengths of moisture and carotenoid content in carrot slices during hot air drying were extracted based on hyperspectral imaging technology. A multispectral imaging equipment was built after that, and the wavelengths of filters were determined according to the characteristic wavelengths. Based on the successive projection algorithm (SPA), the optimal wavelengths of moisture and carotenoid content were further determined, and prediction models of both were established based on the system. There were 12 filters selected in this study. The results showed that a support vector machine (SVM) prediction model for moisture content was established based on seven optimal wavelengths with 0.991 for the coefficient of determination of prediction set (R 2 p ) and 10.318 for the residual prediction residual (RPD). Based on eight optimal wavelengths, a SVM prediction model for carotenoid content was also established with 0.968 for R 2 p and 5.337 for RPD. The prediction performance is close to or even better than that based on hyperspectral. The study confirmed the feasibility of using the multispectral imaging equipment to measure the moisture and carotenoid content of carrot slices during drying based on selected wavelengths, laying a foundation for the further preparation of a portable multispectral detector for the quality of dry products.

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