scholarly journals An Approach of Linear Regression-Based UAV GPS Spoofing Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lianxiao Meng ◽  
Lin Yang ◽  
Shuangyin Ren ◽  
Gaigai Tang ◽  
Long Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Short Term Memory ◽  
Stackelberg Game ◽  
Response Strategy ◽  
Security Threat ◽  
Detection Mechanism ◽  
Detection Scheme ◽  
Aerial Vehicle ◽  
Lstm Network ◽  
The Impact

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti-spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti-spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti-spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short-term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti-spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

scholarly journals Gaze Tracking Based on Concatenating Spatial-Temporal Features

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 545
Author(s):  
Bor-Jiunn Hwang ◽  
Hui-Hui Chen ◽  
Chaur-Heh Hsieh ◽  
Deng-Yu Huang
Keyword(s):  
Short Term Memory ◽  
Image Data ◽  
Training Model ◽  
Point Estimation ◽  
Training Data ◽  
Gaze Tracking ◽  
Spatial Features ◽  
Temporal Features ◽  
Lstm Network ◽  
The Impact

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.

Modeling Anticipation and Relaxation of Lane Changing Behavior Using Deep Learning

2021 ◽  
pp. 036119812110286
Author(s):  
Kequan Chen ◽  
Pan Liu ◽  
Zhibin Li ◽  
Yuxuan Wang ◽  
Yunxue Lu
Keyword(s):  
Short Term Memory ◽  
Trajectory Data ◽  
Lane Changing ◽  
Factors Affecting ◽  
Video Footage ◽  
Input Variables ◽  
Vehicle Trajectory ◽  
Lstm Network ◽  
Two Stages ◽  
The Impact

Modeling lane changing driving behavior has attracted significant attention recently. Most of the existing models are homogeneous and do not recognize the anticipation and relaxation phenomena occurring during the maneuver. To fill this gap, we adopted long short-term memory (LSTM) network and used large quantities of trajectory data extracted from video footage collected by an unmanned automated vehicle in Nanjing, China. Then, we divided complete lane changing behavior into two stages, that is, anticipation and relaxation. Description analysis of lane changing behavior revealed that the factors affecting the two stages are significantly different. In this context, two LSTM models with different input variables were proposed to predict the anticipation and the relaxation during the lane changing activity, respectively. The vehicle trajectory data were further divided into an anticipation dataset and a relaxation dataset to train the two LSTM models. Then we applied numerical tests to compare our models with two baseline models using real trajectory data of lane changing behavior. The results suggest that our models achieved the best performance for trajectory prediction in both lateral and longitudinal positions. Moreover, the simulation results show that the proposed models can precisely replicate the impact of the anticipation phenomenon on the target lane, and the relationship between the speed and spacing of the lane changing vehicle during the relaxation process can be reproduced with reasonable accuracy.

PENGARUH KUALITAS PELAYANAN DAN KEPERCAYAAN TERHADAP LOYALITAS NASABAH PT BANK MANDIRI,Tbk CABANG JAKARTA KELAPA GADING BARAT

2015 ◽  
Vol 3 (3) ◽  
Author(s):  
Imam Wibowo ◽  
Santi Putri Ananda
Keyword(s):  
Linear Regression ◽  
Service Quality ◽  
Multiple Linear Regression ◽  
Analysis Tool ◽  
Positive Contribution ◽  
Homogeneous Population ◽  
Independent Variables ◽  
Research Population ◽  
The Impact

Purpose-To study the impact of the service quality and trust on customers loyalty of PT.Bank Mandiri,Tbk; Kelapa Gading Barat Branch. To improve the customers loyalty there are several factors that can influence them, such as service quality and trust. Methodology/approach-The research population was all customers PT.Bank Mandiri,Tbk;Kelapa Gading Barat Branch.According to the homogeneous population and based on the Gay and Diehl Theory, the samples taken were 50 people. Variables in this investigations consisted of: a).Independent Variables (exogenous): Service Quality (X1) and Trust (X2). b).The dependent variable (endogenous) Customers Loyalty (Y). Analysis tool being used is multiple linear regression which previously conducted validity and realiability. Findings-The result of investigations that service quality and trust simultaneously have a very strong contribution of 75,5% to the customers loyalty, and partially showed that service quality has significant and positive contribution to the customers loyalty of 64,8%. Partially, the trust variable has significant and positive contribution which amounted to 55,9% to the customers loyalty.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Security Threat Analyses and Attack Models for Approximate Computing Systems

2021 ◽  
Vol 26 (4) ◽  
pp. 1-31
Author(s):  
Pruthvy Yellu ◽  
Landon Buell ◽  
Miguel Mark ◽  
Michel A. Kinsy ◽  
Dongpeng Xu ◽  
...  
Keyword(s):  
Error Resilience ◽  
Security Threats ◽  
Approximate Computing ◽  
Security Threat ◽  
Security Vulnerabilities ◽  
Computing Systems ◽  
Quantitative Analyses ◽  
Resilience Mechanisms ◽  
The Impact ◽  
Attack Models

Approximate computing (AC) represents a paradigm shift from conventional precise processing to inexact computation but still satisfying the system requirement on accuracy. The rapid progress on the development of diverse AC techniques allows us to apply approximate computing to many computation-intensive applications. However, the utilization of AC techniques could bring in new unique security threats to computing systems. This work does a survey on existing circuit-, architecture-, and compiler-level approximate mechanisms/algorithms, with special emphasis on potential security vulnerabilities. Qualitative and quantitative analyses are performed to assess the impact of the new security threats on AC systems. Moreover, this work proposes four unique visionary attack models, which systematically cover the attacks that build covert channels, compensate approximation errors, terminate normal error resilience mechanisms, and propagate additional errors. To thwart those attacks, this work further offers the guideline of countermeasure designs. Several case studies are provided to illustrate the implementation of the suggested countermeasures.

scholarly journals THU0433 TREATMENT WITH PEGLOTICASE IMPROVES HEPATIC FIBROSIS ESTIMATED BY FIBROSIS-4 INDEX IN SUBJECTS WITH CHRONIC REFRACTORY GOUT

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 454.1-454
Author(s):  
N. Schlesinger ◽  
A. Yeo ◽  
P. Lipsky
Keyword(s):  
Liver Disease ◽  
Linear Regression ◽  
Rank Order ◽  
Linear Regression Analysis ◽  
Area Under The Curve ◽  
Serum Urate ◽  
Complete Analysis ◽  
Alcoholic Fatty Liver ◽  
Significant Difference ◽  
The Impact

Background:Hyperuricemia is associated with non-alcoholic fatty liver disease (NAFLD)1,2, but the relationship to fibrosis remains uncertain3. Moreover, it is not known whether lowering serum urate will affect the course of NAFLD. The availability of data from two randomized trials of pegloticase, a pegylated recombinant mammalian uricase, that profoundly decreases serum urate afforded the opportunity to test the hypothesis that lowering urate might improve NAFLD.Objectives:To determine whether treatment of chronic refractory gout patients with pegloticase was associated with improvement in NAFLD determined by Fibrosis 4 index (Fib4).Methods:Databases from patients with chronic refractory gout who participated in two randomized 6 month clinical trials (RCTs) of pegloticase were analyzed4. Sub-sets who had persistent urate lowering to levels <1 mg/dL in response to biweekly pegloticase (Responders, n=36) were compared to those who received placebo (n=43). Since liver biopsy information was not available on these subjects, we relied on Fib4, a validated non-invasive estimate of liver fibrosis in a variety of liver diseases5,6calculated from measurements of AST, ALT, platelet count and age (Age x AST/platelets x √ALT). A Fib4 value of 1.3 is an indication that further evaluation of liver disease is warranted.Results:At baseline, the mean Fib4 values were 1.40 ± 0.86 in pegloticase responders and 1.04 ± 0.53 in subjects receiving placebo. As shown in figure 1, subjects receiving placebo exhibited a change of 0.26 ± 0.41 in the Fib4 score over the six months of the RCTs compared with 0.13 ± 0.62 in the pegloticase responders (p=0.048; by linear regression). When only the subjects with a Fib4 value > 1.3 were considered, a significant difference in the change in the Fib4 values over the 6 months of the trial between pegloticase responders and those receiving placebo was also observed (-0.15 ± 0.67 vs 0.37 ± 0.42, p=0.004, by linear regression). The correlations between serum urate area under the curve (AUC) over the 6 months of the trial and the change in Fib4 value was rs=0.33, p=0.0.0004 (Spearman rank-order correlation coefficient). Finally, multiple linear regression analysis indicated serum urate AUC (as a surrogate measure for group) is the main contributor to the change in Fib4 (p=0.018 by linear regression).Conclusion:The data are consistent with the conclusion that persistent lowering of serum urate had a significant impact on Fib4 levels, implying a possible effect on the course of NAFLD. The results support a more complete analysis involving biopsy examination of the impact of urate on liver inflammation and fibrosis.References:[1]Yang C et al. PlosOne2017; 12:e0177249[2]Jaruvongvanich V et al. Eur J Gastroenterol Hepatol 2017; 29:1031[3]Jaruvongvanich V et al. Eur J Gastroenterol Hepatol 2017; 29:694[4]Sundy JS, et al. JAMA. 2011; 306 (7):711-20[5]Sterling RK et al. Hepatol 2006; 43:1317[6]Shah AG et al. Clin Gastroenterol Hepatol 2009;7:1104Disclosure of Interests: :Naomi Schlesinger Grant/research support from: Pfizer, Amgen, Consultant of: Novartis, Horizon Therapeutics, Selecta Biosciences, Olatec, IFM Therapeutics, Mallinckrodt Pharmaceuticals, Anthony Yeo Employee of: Horizon Therapeutics, Peter Lipsky Consultant of: Horizon Therapeutics

scholarly journals Research on Closed-Loop Supply Chain Decision-Making in Different Cooperation Modes with Government’s Reward-Penalty Mechanism

2021 ◽  
Vol 13 (11) ◽  
pp. 6425
Author(s):  
Quanxi Li ◽  
Haowei Zhang ◽  
Kailing Liu
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Stackelberg Game ◽  
Recycling Rate ◽  
Transfer Payment ◽  
Closed Loop Supply Chain ◽  
Asymmetry Of Information ◽  
Optimal Decisions ◽  
The Government ◽  
The Impact

In closed-loop supply chains (CLSC), manufacturers, retailers, and recyclers perform their duties. Due to the asymmetry of information among enterprises, it is difficult for them to maximize efficiency and profits. To maximize the efficiency and profit of the CLSC, this study establishes five cooperation models of CLSC under the government‘s reward–penalty mechanism. We make decisions on wholesale prices, retail prices, transfer payment prices, and recovery rates relying on the Stackelberg game method and compare the optimal decisions. This paper analyzes the impact of the government reward-penalty mechanism on optimal decisions and how members in CLSC choose partners. We find that the government’s reward-penalty mechanism can effectively increase the recycling rate of used products and the total profit of the closed-loop supply chain. According to the calculation results of the models, under the government’s reward-penalty mechanism, the cooperation can improve the CLSC’s used products recycling capacity and profitability. In a supply chain, the more members participate in the cooperation, the higher profit the CLSC obtain. However, the cooperation mode of all members may lead to monopoly, which is not approved by government and customers.

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