scholarly journals Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6756
Author(s):  
DongHyun Ko ◽  
Seok-Hwan Choi ◽  
Sungyong Ahn ◽  
Yoon-Ho Choi
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Indoor Localization ◽  
Artificial Noise ◽  
Good Prediction ◽  
Localization Method ◽  
Good Prediction Accuracy ◽  
Attack Scenario ◽  
Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

scholarly journals Research on Rubber Spring Model of High-speed EMU Based on Non-hyperelastic Forces

Mechanika ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 12-21
Author(s):  
Chuanbo XU ◽  
Maoru CHI ◽  
Liangcheng DAI ◽  
Yiping JIANG ◽  
Yongfa CHEN ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Accuracy ◽  
High Speed ◽  
Dynamic Condition ◽  
Static Condition ◽  
Good Prediction ◽  
Calculation Formula ◽  
Good Prediction Accuracy ◽  
Calculation Results ◽  
Better Than

The research on the mechanical model of rubber spring is one of the hot spots in train dynamics. In order to accurately calculate the viscoelastic force of the rubber spring, especially the non-hyperelastic forces (NHEF) part, a NHEF model is proposed based on the elliptic approximation method. Furthermore, the calculation formula of periodic energy consumption is put forward. The NHEF model is verified by experiments, and the function λ isconstructed to verify the formula of periodic energy consumption. The calculation results showed that the NHEF model had high accuracy in predicting the dynamic and quasi-static NHEF of rubber spring, the prediction accuracy of shear condition was better than that of compression condition, and the accuracy of quasi-static condition was better than that of dynamic condition; the calculation formula of periodic energy consumption had a good prediction accuracy in all working conditions.

scholarly journals A Neural Network Model for the Compressive Strength of a Hybrid LM6 Aluminium Alloy Composite

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1652-1654
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Aluminium Alloy ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Central Composite Rotatable Design ◽  
Alloy Composite ◽  
Ann Model ◽  
Good Prediction Accuracy ◽  
Central Composite

Adding more than one reinforcement increases the flexibility in composites. The objective of the work is to develop a model to predict the compressive strength in an LM6 aluminium alloy reinforced with SiC and flyash particles. Central composite rotatable design had been employed to carry out the experiments with size and composition of the reinforcements as the parameters. ANN model developed has good prediction accuracy with error being less than 5%.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Author(s):  
Elise Ai Hwee Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Diagnostic Methods ◽  
Calibration Model ◽  
Good Prediction ◽  
Blood Concentrations ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces. Methods: Vis-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). QLD samples were quantified using Hemastix® and FAMACHA © scores. Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for blood in naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuichi Okinaga ◽  
Daisuke Kyogoku ◽  
Satoshi Kondo ◽  
Atsushi J. Nagano ◽  
Kei Hirose
Keyword(s):  
Gene Regulation ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Estimation Methods ◽  
High Dimensional ◽  
Good Prediction ◽  
Gene Regulation Network ◽  
Good Prediction Accuracy ◽  
Regulation Network

AbstractThe least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as $$N=300$$ N = 300 , is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.

scholarly journals 3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2731 ◽  
Author(s):  
Shuyan Cheng ◽  
Shujun Wang ◽  
Wenbai Guan ◽  
He Xu ◽  
Peng Li
Keyword(s):  
Deep Learning ◽  
Radio Frequency Identification ◽  
Target Location ◽  
Indoor Localization ◽  
Three Dimensional ◽  
System Stability ◽  
Rfid Technology ◽  
Localization Method ◽  
Variation Characteristics ◽  
Efficient Data

As the core supporting technology of the Internet of Things, Radio Frequency Identification (RFID) technology is rapidly popularized in the fields of intelligent transportation, logistics management, industrial automation, and the like, and has great development potential due to its fast and efficient data collection ability. RFID technology is widely used in the field of indoor localization, in which three-dimensional location can obtain more real and specific target location information. Aiming at the existing three-dimensional location scheme based on RFID, this paper proposes a new three-dimensional localization method based on deep learning: combining RFID absolute location with relative location, analyzing the variation characteristics of the received signal strength (RSSI) and Phase, further mining data characteristics by deep learning, and applying the method to the smart library scene. The experimental results show that the method has a higher location accuracy and better system stability.

scholarly journals Modelling postural discomfort perception using CHAID decision tree algorithm

2019 ◽  
Vol 63 (1) ◽  
pp. 1749-1750
Author(s):  
Soomin Hyun ◽  
Woojin Park
Keyword(s):  
Decision Tree ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Body Part ◽  
Upper Body ◽  
Good Prediction ◽  
Practical Applications ◽  
High Prediction Accuracy ◽  
Good Prediction Accuracy ◽  
High Prediction

Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpretability as well as good prediction accuracy. The goal of this study was to evaluate the utility of the Chi-square Automatic Interaction Detector (CHAID) decision tree modeling method in developing postural discomfort prediction models. Ten individual-specific decision tree models were developed, which predicted overall upper-body discomfort from local body part discomfort ratings. The prediction models were found to achieve high prediction accuracy and interpretability. (150 words)

scholarly journals Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4724
Author(s):  
Xiaoqian Huang ◽  
Rajkumar Muthusamy ◽  
Eman Hassan ◽  
Zhenwei Niu ◽  
Lakmal Seneviratne ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Tactile Sensing ◽  
Good Prediction ◽  
K Nearest Neighbor ◽  
Good Prediction Accuracy ◽  
Grasping Force ◽  
Dynamic Time ◽  
Neuromorphic Vision

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

Sign in / Sign up

Export Citation Format

Share Document