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1
Xu, Hong Ke, Chao Cai, Hao Chen, Jian Wu Fang, and Shu Guang Li. "Research on License Plate Tracking and Detection Based on Optical Flow." Applied Mechanics and Materials 135-136 (October 2011): 775–80. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.775.
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Aiming at regulating the toll evasion behaviors in highway weight charges and reducing charge disputes caused by jumping, this article studied the algorithm that tracks vehicle beating when it is passing the scale. Based on license plate location, vehicle movement could be characterized by tracking the plate centroid using Lucas-Kanade optical flow algorithm. The optical flow vector of the centroid was calculated frame by frame, which could be used for drawing trajectory of centroid coordinates, and calculating beating parameters. In order to expand the detection range and adaptability of the algorithm, through calculating optical flow hierarchically combined with Gaussian pyramid, then tracking centroid from high lever to low in the image pyramid, it could achieve the capture of the vehicle' fast moving. Through experiments, trajectory reflected vehicle beating information well, which provides a strong evidence means for levy problem of the highway weight charges.
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Abdelkader, Eslam Mohammed, Abobakr Al-Sakkaf, Nehal Elshaboury, and Ghasan Alfalah. "Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components." Processes 10, no. 1 (December 24, 2021): 36. http://dx.doi.org/10.3390/pr10010036.
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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.
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Tohti, Gulbahar, Mamtimin Gheni, Yu Feng Chen, and Mamatjan Tursun. "Capacity Analysis of Urban Highway Intersections." Key Engineering Materials 462-463 (January 2011): 1170–75. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.1170.
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In this paper, based on survey data of delays on intersections in urban highways and in accordance with theoretical capacity of each traffic lane, various reasons of delay in intersections are analyzed numerically. A discrete traffic model to simulate traffic in intersections using Gaussian mesh method is built. After modifying intersection properties, weight of each factor in terms of their effect to capacity is acquired. An optimized way to solve traffic delay is hereby recommended.
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Zhu, Zixuan, Chenglong Teng, Yingfeng Cai, Long Chen, Yubo Lian, and Hai Wang. "Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field." World Electric Vehicle Journal 13, no. 11 (October 31, 2022): 203. http://dx.doi.org/10.3390/wevj13110203.
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The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird’s-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control.
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Peng, Tao, Li-li Su, Zhi-wei Guan, Hai-jing Hou, Jun-kai Li, Xing-liang Liu, and Yi-ke Tong. "Lane-Change Model and Tracking Control for Autonomous Vehicles on Curved Highway Sections in Rainy Weather." Journal of Advanced Transportation 2020 (November 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8838878.
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In this study, we propose an adaptive path planning model and tracking control method for collision avoidance and lane-changing manoeuvres on highways in rainy weather. Considering the human-vehicle-road interaction, we developed an adaptive lane change system that consists of an intelligent trajectory planning and tracking controller. Gaussian distribution was introduced to evaluate the impact of rain on the pavement characteristics and deduce adaptive lane-change trajectories. Subsequently, a score-based decision mechanism and multilevel autonomous driving mode that considers safety, comfort, and efficiency were proposed. A tracking controller was designed using a linearised model predictive control method. Finally, using simulated scenarios, the feasibility and effectiveness of the proposed method were demonstrated. The results obtained herein are a valuable resource that can be used to develop an intelligent lane change system for autonomous vehicles and can help improve highway traffic safety and efficiency in adverse weather conditions.
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Zhang, Yule, and Shoulin Zhu. "Study on the Effect of Driving Time on Fatigue of Grassland Road Based on EEG." Journal of Healthcare Engineering 2021 (July 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/9957828.
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In order to study the change law of the fatigue degree of grassland expressway drivers over time, this paper takes the semidesert grassland landscape of Xilinhot city as the experimental environment and takes the provincial highway S101 (K278–K424) as an example to design an actual driving test. Taking Urumqi, Inner Mongolia Autonomous Region, as the experimental section, combined with the Biopac MP150 multichannel physiological instrument and its auxiliary knowledge software and mathematical statistics methods, the relationship between EEG and time was studied. The test results show that the primary fatigue factor F1 and the secondary fatigue factor F2 can summarize the fatigue law characterized by 96.42% of EEG information. During 130 minutes of driving on the prairie highway, the periods of high fatigue were 105 minutes and 125 minutes, respectively. Driving fatigue can be divided into three stages over time: 5–65 min fatigue-free stage, 70–85 min fatigue transition stage, and 90–130 min fatigue stage. Fatigue changes over time. The law follows the Gaussian function and the sine function.
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Aghayari, M., P. Pahlavani, and B. Bigdeli. "A GEOGRAPHIC WEIGHTED REGRESSION FOR RURAL HIGHWAYS CRASHES MODELLING USING THE GAUSSIAN AND TRICUBE KERNELS: A CASE STUDY OF USA RURAL HIGHWAYS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 305–9. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-305-2017.
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Based on world health organization (WHO) report, driving incidents are counted as one of the eight initial reasons for death in the world. The purpose of this paper is to develop a method for regression on effective parameters of highway crashes. In the traditional methods, it was assumed that the data are completely independent and environment is homogenous while the crashes are spatial events which are occurring in geographic space and crashes have spatial data. Spatial data have spatial features such as spatial autocorrelation and spatial non-stationarity in a way working with them is going to be a bit difficult. The proposed method has implemented on a set of records of fatal crashes that have been occurred in highways connecting eight east states of US. This data have been recorded between the years 2007 and 2009. In this study, we have used GWR method with two Gaussian and Tricube kernels. The Number of casualties has been considered as dependent variable and number of persons in crash, road alignment, number of lanes, pavement type, surface condition, road fence, light condition, vehicle type, weather, drunk driver, speed limitation, harmful event, road profile, and junction type have been considered as explanatory variables according to previous studies in using GWR method. We have compered the results of implementation with OLS method. Results showed that R<sup>2</sup> for OLS method is 0.0654 and for the proposed method is 0.9196 that implies the proposed GWR is better method for regression in rural highway crashes.
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Oguri, Y., T. Yamashita, A. Ebihara, N. Kambe, and J. Hasegawa. "PIXE MEASUREMENT OF ATMOSPHERIC PARTICULATE MATTER IN A RESIDENTIAL AREA NEAR A MAJOR URBAN HIGHWAY." International Journal of PIXE 10, no. 03n04 (January 2000): 127–35. http://dx.doi.org/10.1142/s0129083500000183.
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In order to study influence of automobile traffic on a local urban atmospheric environment, we have investigated suspended particulate matter (SPM) collected at sampling sites in a region which includes a major highway and a residential area in the southern part of Tokyo during August - November 1999. The atomic composition of each sample was measured by means of PIXE analysis using a 2.0 MeV proton beam. Sixteen elements were quantitatively measured. The positional dependence of SPM loading was determined for each element using samples simultaneously collected at three different sites. For the experimental results obtained for downwind conditions, the measured concentration as a function of the distance from the highway was compared with a simple calculation based on the Gaussian plume model. The concentration distribution of some heavy elements in the fine fraction is well reproduced by this analysis. It has been found that for ordinary moderate downwind conditions the area within 300-400 m from the highway is directly affected by emission due to the automobile traffic.
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Korablev, R. A., V. P. Belokurov, and S. V. Belokurov. "Influence of anthropogenic impact of vehicles on roadside forest plantations." IOP Conference Series: Earth and Environmental Science 875, no. 1 (October 1, 2021): 012079. http://dx.doi.org/10.1088/1755-1315/875/1/012079.
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Abstract The article presents studies of the growth and development of forest stands along highways as a result of man-made impacts from road transport emissions. The obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonities of roadside stands during the period of light saturation is presented. In this regard, the obtained mathematical model describing the dynamics of the growth of the biomass of stands of various bonitets of roadside forest stands during the period of light saturation is presented. The use of the bonus in research to characterize the growth rate of forest roadside plantings depending on the distance to highways and the density of traffic flows on them allows us to characterize the amount of toxic pollutants entering forests. This allows us to assess the process of expanding the environmentally unfavorable zone along the highway. The article presents the possibility of calculating the concentration of pollutants, based on the model of turbulent diffusion, reduced, after some assumption, to the model of Gaussian distribution in atmospheric air. The dependence on the calculation of the intensity of emissions of pollutants, taking into account the composition of the traffic flow, is given.
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Qing, Feng, Yan Zhao, Xingmin Meng, Xiaojun Su, Tianjun Qi, and Dongxia Yue. "Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway." Remote Sensing 12, no. 18 (September 10, 2020): 2933. http://dx.doi.org/10.3390/rs12182933.
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The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.
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Dimitrijevic, Branislav, Sina Darban Khales, Roksana Asadi, and Joyoung Lee. "Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data." Applied Sciences 12, no. 2 (January 14, 2022): 856. http://dx.doi.org/10.3390/app12020856.
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Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.
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Guadamuz-Flores, Renato, and Jonathan Aguero-Valverde. "Bayesian Spatial Models of Crash Frequency at Highway–Railway Crossings." Transportation Research Record: Journal of the Transportation Research Board 2608, no. 1 (January 2017): 27–35. http://dx.doi.org/10.3141/2608-04.
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Though rare, crashes at highway–railway crossings usually result in severe injuries and fatalities. Intersections along railway corridors share many conditions that support the use of spatial analyses, but so far the spatial effects are essentially unknown. Little research has been conducted about highway–railway crossings using spatial correlation. The study presented in this paper analyzed spatial correlation structures on crash frequency at railway crossings, particularly conditional autoregressive (CAR) and joint specification, with the use of Gaussian Kriging models. Full Bayesian Poisson–lognormal approaches were used to compare the effects of various models, including heterogeneity-only, spatial-only, and heterogeneity–spatial models. These methods were estimated with crash data from a low-speed passenger train service in Costa Rica. The deviance information criterion was used to compare the models. Heterogeneity–CAR models show a better goodness of fit than heterogeneity-only, joint Kriging, and CAR-only definitions. The second-order neighboring model (CAR-2) yielded the best fit and related to an average neighbor distance of about 700 m (0.44 mi). Robust semi-variograms based on joint Kriging models and CAR methods showed similar results. The proportion of variation in the data explained by spatial correlation methods was similar (29% of the total variation). These findings suggest that spatial correlation at highway–railway crossings should be considered in modeling of crash frequencies.
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Yang, Zeying, Yinglin Sun, Jianbo Qu, Chenghe Wang, and Tianmin Wang. "Study on Vehicle Load Model of Baijianhe Bridge." Advances in Civil Engineering 2022 (November 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7523590.
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In order to calculate the vehicle load model of Baijianhe Bridge, based on the vehicle load data of the health monitoring system, the vehicle type, vehicle weight, wheelbase, and other information were counted and the data were processed and diagraphed to obtain the probability density distribution. At the same time, the automobile load model parameters relative to the national current code and finite element method are calculated. The results show that 2-axle and 6-axle vehicles are the main vehicle types, accounting for about 48%. The number of upstream and downstream vehicles is the same, but the number of vehicles in the lane is much higher than in the overtaking lane. The carriageway is dominated by 6-axle vehicles, while the overtaking lane is dominated by 2-axle vehicles. The probability density distribution of vehicle weight in overtaking lane obeys mixed Gaussian distribution and that in the carriageway obeys Weibull distribution. According to the measured vehicle load data, the vehicle load suitable for Baijianhe Bridge is 1.1 times the highway-I vehicle load of the current Chinese standard “General Code for Design of Highway Bridges and Culvers” (JTG D60-2015).
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Lu, Naiwei, Kai Wang, Honghao Wang, Yang Liu, Yuan Luo, and Xinhui Xiao. "Dynamic Reliability of Continuous Rigid-Frame Bridges under Stochastic Moving Vehicle Loads." Shock and Vibration 2020 (September 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/8811105.
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The current volume of freight traffic has increased significantly during the past decades, impacted by the fast development of the national transportation market. As a result, the phenomena of truck overloading and traffic congestion emerge, which have resulted in numerous bridge collapse events or damage due to truck overloading. Thus, it is an urgent task to evaluate bridge safety under actual traffic loads. This study evaluated probabilistic dynamic load effects on rigid-frame bridges under highway traffic monitoring loads. The site-specific traffic monitoring data of a highway in China were utilized to establish stochastic traffic models. The dynamic effect was considered in a vehicle-bridge coupled vibration model, and the probability estimation was conducted based on the first-passage criterion of the girder deflection. The prototype bridge is a continuous rigid-frame bridge with a midspan length of 200 m and a pier height of 182 m. It is demonstrated that the dynamic traffic load effect follows Gaussian distribution, which can be treated as a stationary random process. The mean value and standard deviation of the deflections are 0.071 m and 0.088 m, respectively. The dynamic reliability index for the first passage of girder deflection is 6.45 for the current traffic condition. However, the reliability index decreases to 5.60 in the bridge lifetime, accounting for an average traffic volume growth ratio of 3.6%.
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Lionnie, Regina, Rizky Citra Ramadhan, Ahmad Syadidu Rosyadi, Muzammil Jusoh, and Mudrik Alaydrus. "Performance analysis of various types of surface crack detection based on image processing." SINERGI 26, no. 1 (February 1, 2022): 1. http://dx.doi.org/10.22441/sinergi.2022.1.001.
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Major cracks on a highway or bridge's concrete surface have a massive risk of damages, accompanied by less maintenance, slow detection, and handling; the worst case of the damage is the structure's total collapse, which can produce fatalities. Moreover, Indonesia's climate and geographical location contribute to a higher level of potential damage to the structure. In order to reduce the potential damage, the need for a surface crack detection system arises. This research analysed three different databases (Database A, B, and C) with different surface concrete crack types, such as early thermal contraction, plastic shrinkage, corrosion reinforcement, and non-crack images. The total images from each Database vary from 14 images for Database A, 80 images for Database B, and 4000 images for Database C. The Otsu thresholding and mathematical morphology operations such as opening, closing, dilation, and erosion with pre-processing methods were combined and produced results for each Database with classification using Euclidean distance calculation. The best results for Database A and B were 100% using combination Otsu thresholding with Laplacian operator and Laplacian of Gaussian filter and the same result for a combination of mathematical morphological operations. The best result using Database C, which had more images than Database A and B, was 80,2% using a combination of mathematical morphological operations.
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Han, Haihang, Hanyu Deng, Qiao Dong, Xingyu Gu, Tianjie Zhang, and Yangyang Wang. "An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure." Advances in Materials Science and Engineering 2021 (July 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/9205509.
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The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.
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Bao, Jieyi, Yi Jiang, and Shuo Li. "Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm." Lubricants 11, no. 7 (June 24, 2023): 275. http://dx.doi.org/10.3390/lubricants11070275.
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Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.
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Zamiri-Jafarian, Yeganeh, and Konstantinos N. Plataniotis. "A Bayesian Surprise Approach in Designing Cognitive Radar for Autonomous Driving." Entropy 24, no. 5 (May 10, 2022): 672. http://dx.doi.org/10.3390/e24050672.
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This article proposes the Bayesian surprise as the main methodology that drives the cognitive radar to estimate a target’s future state (i.e., velocity, distance) from noisy measurements and execute a decision to minimize the estimation error over time. The research aims to demonstrate whether the cognitive radar as an autonomous system can modify its internal model (i.e., waveform parameters) to gain consecutive informative measurements based on the Bayesian surprise. By assuming that the radar measurements are constructed from linear Gaussian state-space models, the paper applies Kalman filtering to perform state estimation for a simple vehicle-following scenario. According to the filter’s estimate, the sensor measures the contribution of prospective waveforms—which are available from the sensor profile library—to state estimation and selects the one that maximizes the expectation of Bayesian surprise. Numerous experiments examine the estimation performance of the proposed cognitive radar for single-target tracking in practical highway and urban driving environments. The robustness of the proposed method is compared to the state-of-the-art for various error measures. Results indicate that the Bayesian surprise outperforms its competitors with respect to the mean square relative error when one-step and multiple-step planning is considered.
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Ahmad, Mahmood, Badr T. Alsulami, Ramez A. Al-Mansob, Saerahany Legori Ibrahim, Suraparb Keawsawasvong, Ali Majdi, and Feezan Ahmad. "Predicting Subgrade Resistance Value of Hydrated Lime-Activated Rice Husk Ash-Treated Expansive Soil: A Comparison between M5P, Support Vector Machine, and Gaussian Process Regression Algorithms." Mathematics 10, no. 19 (September 21, 2022): 3432. http://dx.doi.org/10.3390/math10193432.
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Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material’s resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.
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Sari, Nurachmawati Meindah, R. Azizah, Lilis Sulistyorini, Endrayana Putut Laksminto Emanuel, Emillia Devi Dwi Rianti, Fuad Ama, Sukma Sahadewa, Agusniar Furkani Listyawati, Ayly Soekanto, and Hardiyono Hardiyono. "Dispersion of Carbon Monoxide Pollutant and The Effect of Health (Case Study on Frontage Road Surabaya by Gaussian Line Source Equation Model)." Jurnal Ilmiah Kedokteran Wijaya Kusuma 11, no. 2 (October 1, 2022): 156. http://dx.doi.org/10.30742/jikw.v11i2.2416.
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Air pollution was being a very important problem and danger for human life. This was related to diseases that arise due to motor vehicle emissions, especially carbon monoxide. Simulation of air dispersion models is the one way to study about air quality that is needed in this regard. This study aims to determine the distribution of carbon monoxide pollutants in Ahmad Yani's frontage and to anticipate the dangers of these pollutants to the health of the people living around the research location. This research discussed about the mathematical model of the dispersion of CO that emitted from cars that passed through the frontage road on the Ahmad Yani Street, Surabaya. The method used is direct observation in the field and numerical simulation using a mathematical model, Gaussian Line Source Equation Model (GLSEM). GLSEM had prepared based on the mechanism of transport of pollutants in dispersion, diffusion and advection. With GLSEM we calculated CO gas concentration values for certain heights downwind. We validated the model by comparing numerical results and measurements of CO concentration. We used the R2 test and we got an R2 close to one. We simulated GLSEM by used Fortran programming language and visualized it with Surfer. The results of the visualization in June showed that the pattern of CO gas dispersion was influenced by the direction and speed of the wind. The results obtained are that the distribution of CO pollutants in the Ahmad Yani frontage is horizontal/downwind. CO concentrations at night are higher than during the daytime. From the CO dispersion pattern, we had known that there were dangerous of air around the frontage for people health. We conclude that around the frontage road of the Ahmad Yani highway there is sufficient open air space so that the danger of CO pollutants being emitted can be minimized so that the health of the community, namely pedestrians, motorcycle drivers and the community around the location can be protected.
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Abbas, Farkhanda, Feng Zhang, Muhammad Ismail, Garee Khan, Javed Iqbal, Abdulwahed Fahad Alrefaei, and Mohammed Fahad Albeshr. "Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques." Sensors 23, no. 15 (August 1, 2023): 6843. http://dx.doi.org/10.3390/s23156843.
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Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment’s results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model’s overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.
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Zou, Yajie, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, and Yinhai Wang. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis." Sustainability 11, no. 2 (January 15, 2019): 418. http://dx.doi.org/10.3390/su11020418.
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Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.
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Stolbova, A. A., S. A. Prokhorov, and O. K. Golovnin. "DETECTION OF LOCAL IRREGULARITIES IN THE ROAD PAVEMENT ON THE BASIS OF WAVELET TRANSFORM OF ULTRASONIC PROFILING DATA." Journal of Dynamics and Vibroacoustics 7, no. 1 (February 4, 2021): 34–38. http://dx.doi.org/10.18287/2409-4579-2021-7-1-34-38.
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The paper presents an approach to the detection of road pavement defects on the streets and highways based on wavelet analysis of data obtained from an ultrasonic profilometer. The approach makes it possible to determine the location of pavement defects in relation to the road lane. The results of implementing the approach using the complex Morlet wavelet and the first derivative of the Gaussian function are presented. Implementation of the approach reduces the influence of interference arising during ultrasonic diagnosis.
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Shang, Yuze, Fei Liu, Ping Qin, Zhizhong Guo, and Zhe Li. "Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution." Engineering Computations 40, no. 5 (July 11, 2023): 1266–86. http://dx.doi.org/10.1108/ec-11-2022-0672.
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PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.
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Bashmakova E. N., Vashukevich E. A., Golubeva T. Yu., and Golubev Yu. M. "Highly efficient generation of squeezed states of light based on Laguerre-Gaussian modes in a cavity." Optics and Spectroscopy 130, no. 14 (2022): 2120. http://dx.doi.org/10.21883/eos.2022.14.53997.2484-21.
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Today, the efficient generation of squeezed states of light seems to be a significant practical problem for various quantum-optical and information applications. In this paper, we investigate the possibility of increasing the efficiency of the generation of states based on the Laguerre-Gaussian light modes in the parametric down conversion due to the optimal choice of the cavity configuration. Analyzing the Heisenberg-Langevin equations for the eigenmodes of the system, we estimate the influence of the geometric parameters of the pump beam and the idler and signal beams on the efficiency of generation of squeezed states and on the degree of squeezing. The calculation for a finite number of modes has shown that the highest theoretically possible degree of squeezing in the system is 15.85 dB. Keywords: squeezed light, Laguerre-Gaussian modes, orbital angular momentum, optimization of PDC geometric parameters.
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Wang, Jianjun, Sai Wang, Xueqin Long, Dongyi Li, Chicheng Ma, and Peng Li. "Ellipse-Like Radiation Range Grading Method of Traffic Accident Influence on Mountain Highways." Sustainability 14, no. 21 (October 23, 2022): 13727. http://dx.doi.org/10.3390/su142113727.
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To improve the efficiency of accident treatment on mountain highways and reduce the degree of disruption from traffic accidents, the grading method of the ellipse-like radiation range of traffic accident impact is proposed. First, according to the propagation law of traffic accidents, the general function of mountain highways affected by traffic accidents was constructed based on the Gaussian plume model. Then, based on the gravity field theory, the influence of the accident source point on the accident road was analyzed in the aftermath of a supposed accident. Additionally, considering the cascading failure of the road network, the influence of the accident-intersecting roads was demarcated by the cascading failure load propagation function. Based on this analysis, the ellipse-like radiation range models of traffic accidents on the accident road and the intersecting roads were proposed, respectively. Next, the adjustment parameter was further introduced to incorporate the different levels of influence of traffic accidents on the surrounding road network into the model, and the grading impacts of the accident on the potentially utilized opposite lane were discussed. Finally, according to the queuing theory model, simulation design, and portability analysis, the accuracy of the ellipse-like radiation range grading model was verified. The research results show that, compared with queuing theory and simulation results, the error of the grading model of the ellipse-like radiation range affected by traffic accidents was within a reasonable range; that is, the model can reasonably quantify the difference of traffic accident propagation on the accident road and the intersecting roads. Moreover, the heterogeneity of traffic accident propagation was verified by taking the non-occupied opposite lanes as an example. The grading method of influence radiation range utilized for traffic accidents on mountain highways can quickly provide corresponding auxiliary decision support for accident rescue within varying influence ranges.
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Shaik, Abdul Lateef Haroon Phulara, Monica Komala Manoharan, Alok Kumar Pani, Raji Reddy Avala, and Chien-Ming Chen. "Gaussian Mutation–Spider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification." Remote Sensing 14, no. 24 (December 11, 2022): 6279. http://dx.doi.org/10.3390/rs14246279.
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Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian Mutation–Spider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset.
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Remy, Ingrid, and Stephen W. Michnick. "A highly sensitive protein-protein interaction assay based on Gaussia luciferase." Nature Methods 3, no. 12 (November 12, 2006): 977–79. http://dx.doi.org/10.1038/nmeth979.
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Qi, Hong, Yaobin Qiao, Shuangcheng Sun, Yuchen Yao, and Liming Ruan. "Image Reconstruction of Two-Dimensional Highly Scattering Inhomogeneous Medium Using MAP-Based Estimation." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/412315.
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A maximum a posteriori (MAP) estimation based on Bayesian framework is applied to image reconstruction of two-dimensional highly scattering inhomogeneous medium. The finite difference method (FDM) and conjugate gradient (CG) algorithm serve as the forward and inverse solving models, respectively. The generalized Gaussian Markov random field model (GGMRF) is treated as the regularization, and finally the influence of the measurement errors and initial distributions is investigated. Through the test cases, the MAP estimate algorithm is demonstrated to greatly improve the reconstruction results of the optical coefficients.
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Kuang, Tai, Qing‐Xin Zhu, and Yue Sun. "Edge detection for highly distorted images suffering Gaussian noise based on improve Canny algorithm." Kybernetes 40, no. 5/6 (June 14, 2011): 883–93. http://dx.doi.org/10.1108/03684921111142430.
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Atouf, Issan, Wahban Yahya Al Okaishi, Abdelmoghit Zaaran, Ibtissam Slimani, and Mohamed Benrabh. "A real-time system for vehicle detection with shadow removal and vehicle classification based on vehicle features at urban roads." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (December 1, 2020): 2091. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp2091-2098.
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Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
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Chin, T. M., M. J. Turmon, J. B. Jewell, and M. Ghil. "An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems." Monthly Weather Review 135, no. 1 (January 1, 2007): 186–202. http://dx.doi.org/10.1175/mwr3353.1.
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Abstract Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.
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Gu, Chen, Hong Hong, Yusheng Li, Xiaohua Zhu, and Jin He. "Highly Accurate Multi-Invariance ESPRIT for DOA Estimation with a Sparse Array." Mathematical Problems in Engineering 2019 (February 18, 2019): 1–7. http://dx.doi.org/10.1155/2019/5325817.
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This paper proposes a multi-invariance ESPRIT-based method for estimation of 2D direction (MIMED) of multiple non-Gaussian monochromatic signals using cumulants. In the MIMED, we consider an array geometry containing sparse L-shaped diversely polarized vector sensors plus an arbitrarily-placed single polarized scalar sensor. Firstly, we define a set of cumulant matrices to construct two matrix blocks with multi-invariance property. Then, we develop a multi-invariance ESPRIT-based algorithm with aperture extension using the defined matrix blocks to estimate two-dimensional directions of the signals. The MIMED can provide highly accurate and unambiguous direction estimates by extending the array element spacing beyond a half-wavelength. Finally, we present several simulation results to demonstrate the superiority of the MIMED.
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Jasim, Mohammed, and Adel Aldalbahi. "Design of XOR Photonic Gate using Highly Nonlinear Fiber." Electronics 8, no. 2 (February 15, 2019): 215. http://dx.doi.org/10.3390/electronics8020215.
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In this paper, a comprehensive design and simulation of an all-photonic XOR logic gate is proposed. The design is based on the third-order Kerr nonlinear effect in highly nonlinear fiber, i.e., utilizing the self-phase and cross-phase modulations phenomena. This work presents the first photonic logic gate based on highly nonlinear fiber component only that achieves a data rate of 20 Gbps. Moreover, the design is based on two input binary bit sequences, narrow pulsed by a Gaussian distribution as 8-bit incoming data streams. Also, optical cross connectors with different coupling coefficients are used to generate pump and probe signals and tuneable optical band pass filters are leveraged to perform the logic gate functionalities. Remarkable performance outcomes are concluded from the eye pattern diagram and bit error rate analyzers. Simulation results show that the proposed XOR optical logic gate design is achieved at very low power penalties, low bit error rates, a significant Q-factor, and high extinction ratios as compared to existing methods.
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Tai, Ying, Yicong Liang, Xiaoming Liu, Lei Duan, Jilin Li, Chengjie Wang, Feiyue Huang, and Yu Chen. "Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8893–900. http://dx.doi.org/10.1609/aaai.v33i01.33018893.
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In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-ofthe-art models.
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Clarke, Will, Matthew J. Wolf, Alison Walker, and Giles Richardson. "Charge transport modelling of perovskite solar cells accounting for non-Boltzmann statistics in organic and highly-doped transport layers." Journal of Physics: Energy 5, no. 2 (March 27, 2023): 025007. http://dx.doi.org/10.1088/2515-7655/acc4e9.
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Abstract We present a drift–diffusion model of a perovskite solar cell (PSC) in which carrier transport in the charge transport layers (TLs) is not based on the Boltzmann approximation to the Fermi–Dirac (FD) statistical distribution, in contrast to previously studied models. At sufficiently high carrier densities the Boltzmann approximation breaks down and the precise form of the density of states function (often assumed to be parabolic) has a significant influence on carrier transport. In particular, parabolic, Kane and Gaussian models of the density of states are discussed in depth and it is shown that the discrepancies between the Boltzmann approximation and the full FD statistical model are particularly marked for the Gaussian model, which is typically used to describe organic semiconducting TLs. Comparison is made between full device models, using parameter values taken from the literature, in which carrier motion in the TLs is described using (I) the full FD statistical model and (II) the Boltzmann approximation. For a representative TiO2/MAPI/Spiro device the behaviour of the PSC predicted by the Boltzmann-based model shows significant differences compared to that predicted by the FD-based model. This holds both at steady-state, where the Boltzmann treatment overestimates the power conversion efficiency by a factor of 27%, compared to the FD treatment, and in dynamic simulations of current–voltage hysteresis and electrochemical impedance spectroscopy. This suggests that the standard approach, in which carrier transport in the TLs is modelled based on the Boltzmann approximation, is inadequate. Furthermore, we show that the full FD treatment gives a more accurate representation of the steady-state performance, compared to the standard Boltzmann treatment, as measured against experimental data reported in the literature for typical TiO2/MAPI/Spiro devices.
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Develi, I., and A. Basturk. "Highly Accurate Analytic Approximation to the Gaussian Q-function Based on the Use of Nonlinear Least Squares Optimization Algorithm." Journal of Optimization Theory and Applications 159, no. 1 (November 10, 2012): 183–91. http://dx.doi.org/10.1007/s10957-012-0217-0.
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Galati, Rocco, Giacomo Mantriota, and Giulio Reina. "RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots." Robotics 11, no. 5 (September 21, 2022): 99. http://dx.doi.org/10.3390/robotics11050099.
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The paper presents RoboNav, a cost-effective and accurate decimeter-grade navigation system that can be used for deployment in the field of autonomous agricultural robots. The novelty of the system is the reliance on a dual GPS configuration based on two u-blox modules that work in conjunction with three low-cost inertial sensors within a Gaussian Sum Filter able to combine multiple Extended Kalman filters dealing with IMU bias and GPS signal loss. The system provides estimation of both position and heading with high precision and robustness, at a significantly lower cost than existing equivalent navigation systems. RoboNav is validated in a commercial vineyard by performing experimental tests using an all-terrain tracked robot commanded to follow a series of GPS waypoints, trying to minimize the crosstrack error and showing average errors on the order of 0.2 m and 0.2∘ for the measurement of position and yaw angle, respectively.
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Zhang, ShuaiWei, XiaoYuan Yang, Lin Chen, and Weidong Zhong. "A Highly Effective Data Preprocessing in Side-Channel Attack Using Empirical Mode Decomposition." Security and Communication Networks 2019 (October 30, 2019): 1–10. http://dx.doi.org/10.1155/2019/6124165.
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Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.
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Srivastava, Nimish Kumar, and Sanjeev Kumar Raghuwanshi. "Photonic-technique-based highly steerable beamforming system incorporating a prism of super Gaussian apodized tunable chirped fiber Bragg grating for X-band applications." Applied Optics 59, no. 10 (March 25, 2020): 3010. http://dx.doi.org/10.1364/ao.383460.
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Wei, Hao, Qinghua Zhang, and Yu Gu. "Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System." Sensors 23, no. 12 (June 16, 2023): 5642. http://dx.doi.org/10.3390/s23125642.
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Efficient fault diagnosis of rotating machinery is essential for the safe operation of equipment in the manufacturing industry. In this study, a robust and lightweight framework consisting of two lightweight temporal convolutional network (LTCN) backbones and a broad learning system with incremental learning (IBLS) classifier called LTCN-IBLS is proposed for the fault diagnosis of rotating machinery. The two LTCN backbones extract the fault’s time–frequency and temporal features with strict time constraints. The features are fused to obtain more comprehensive and advanced fault information and input into the IBLS classifier. The IBLS classifier is employed to identify the faults and exhibits a strong nonlinear mapping ability. The contributions of the framework’s components are analyzed by ablation experiments. The framework’s performance is verified by comparing it with other state-of-the-art models using four evaluation metrics (accuracy, macro-recall (MR), macro-precision (MP), and macro-F1 score (MF)) and the number of trainable parameters on three datasets. Gaussian white noise is introduced into the datasets to evaluate the robustness of the LTCN-IBLS. The results show that our framework provides the highest mean values of the evaluation metrics (accuracy ≥ 0.9158, MP ≥ 0.9235, MR ≥ 0.9158, and MF ≥ 0.9148) and the lowest number of trainable parameters (≤0.0165 Mage), indicating its high effectiveness and strong robustness for fault diagnosis.
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Ila, Viorela, Lukas Polok, Marek Solony, and Pavel Svoboda. "SLAM++-A highly efficient and temporally scalable incremental SLAM framework." International Journal of Robotics Research 36, no. 2 (February 2017): 210–30. http://dx.doi.org/10.1177/0278364917691110.
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The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.
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Jia, Hongguo, Bowen Wei, Guoxiang Liu, Rui Zhang, Bing Yu, and Shuaiying Wu. "A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images." Remote Sensing 13, no. 9 (May 3, 2021): 1784. http://dx.doi.org/10.3390/rs13091784.
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Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (RF) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas.
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Bhagawati, Ankita, and Nilakshi Das. "Effect of density gradients on the generation of a highly energetic and strongly collimated proton beam from a laser-irradiated Gaussian-shaped hydrogen microsphere." Physics of Plasmas 29, no. 5 (May 2022): 053107. http://dx.doi.org/10.1063/5.0085089.
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An investigation is made on the influence of the sharpness of the density gradients on the generation of energetic protons in a radially Gaussian density profile of a spherical hydrogen plasma. It is possible to create such density gradients by impinging a solid density target with a secondary lower intensity pulse, which ionizes the target and explodes it to create an expanded plasma target of lower effective density for the high-intensity main pulse to hit on. The density gradients are scanned in the near-critical regime, and separate regimes of proton motion are identified based on the density sharpness. An intermediate-density gradient [[Formula: see text]] favors the generation of high energetic protons with narrow energy spectra that are emitted with better collimation from the target rear surface. Protons with energies exceeding 100 MeVs could be achieved using such modified plasma targets with circularly polarized lasers of peak intensities [Formula: see text] and peak energy [Formula: see text].
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Zaki, M. M., Shaojie Chen, Jicheng Zhang, Fan Feng, Aleksey A. Khoreshok, Mohamed A. Mahdy, and Khalid M. Salim. "A Novel Approach for Resource Estimation of Highly Skewed Gold Using Machine Learning Algorithms." Minerals 12, no. 7 (July 18, 2022): 900. http://dx.doi.org/10.3390/min12070900.
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With the complicated geology of vein deposits, their irregular and extremely skewed grade distribution, and the confined nature of gold, there is a propensity to overestimate or underestimate the ore grade. As a result, numerous estimation approaches for mineral resources have been developed. It was investigated in this study by using five machine learning algorithms to estimate highly skewed gold data in the vein-type at the Quartz Ridge region, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Decision Tree Ensemble (DTE), Fully Connected Neural Network (FCNN), and K-Nearest Neighbors (K-NN). The accuracy of MLA is compared to that of geostatistical approaches, such as ordinary and indicator kriging. Significant improvements were made during data preprocessing and splitting, ensuring that MLA was estimated accurately. The data were preprocessed with two normalization methods (z-score and logarithmic) to enhance network training performance and minimize substantial differences in the dataset’s variable ranges on predictions. The samples were divided into two equal subsets using an integrated data segmentation approach based on the Marine Predators Algorithm (MPA). The ranking shows that the GPR with logarithmic normalization is the most efficient method for estimating gold grade, far outperforming kriging techniques. In this study, the key to producing a successful mineral estimate is more than just the technique. It also has to do with how the data are processed and split.
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An, Feng, Shanyu Han, Xixi Hu, Kaijun Yuan, and Daiqian Xie. "Adiabatic potential energy surfaces and photodissociation mechanisms for highly excited states of H2O." Chinese Journal of Chemical Physics 35, no. 1 (February 2022): 104–16. http://dx.doi.org/10.1063/1674-0068/cjcp2111241.
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Full-dimensional adiabatic potential energy surfaces of the electronic ground state X̃ and nine excited states Ã, Ĩ, B̃, C̃, D̃, D̃′, D̃″, Ẽ′ and F̃ of H2O molecule are developed at the level of internally contracted multireference configuration interaction with the Davidson correction. The potential energy surfaces are fitted by using Gaussian process regression combining permutation invariant polynomials. With a large selected active space and extra diffuse basis set to describe these Rydberg states, the calculated vertical excited energies and equilibrium geometries are in good agreement with the previous theoretical and experimental values. Compared with the well-investigated photodissociation of the first three low-lying states, both theoretical and experimental studies on higher states are still limited. In this work, we focus on all the three channels of the highly excited state, which are directly involved in the vacuum ultraviolet photodissociation of water. In particular, some conical intersections of D̃–Ẽ′, Ẽ′-F̃, ÖĨ and Ĩ– C̃ states are clearly illustrated for the first time based on the newly developed potential energy surfaces (PESs). The nonadiabatic dissociation pathways for these excited states are discussed in detail, which may shed light on the photodissociation mechanisms for these highly excited states.
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Ahmadi, Kavan, and Artur Carnicer. "Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks." Journal of Physics: Conference Series 2407, no. 1 (December 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2407/1/012002.
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Abstract In this communication, we present a method to estimate the aberrated wavefront at the focal plane of a vectorial diffraction system. In contrast to the phase, the polarization state of optical fields is simply measurable. In this regard, we introduce an alternative approach for determining the aberration of the wavefront using polarimetric information. The method is based on training a convolutional neural network using a large set of polarimetric mapping images obtained by simulating the propagation of aberrated wavefronts through a high-NA microscope objective; then, the coefficients of the Zernike polynomials could be recovered after interrogating the trained network. On the one hand, our approach aims to eliminate the necessity of phase retrieval for wavefront sensing applications, provided the beam used is known. On the other hand, the approach might be applied for calibrating the complex optical system suffering from aberrations. As proof of concept, we use a radially polarized Gaussian-like beam multiplied by a phase term that describes the wavefront aberration. The training dataset is produced by using Zernike polynomials with random coefficients. Two thousand random combinations of polynomial coefficients are simulated. For each one, the Stokes parameters are calculated to introduce a polarimetric mapping image as the input of a neural network model designed and trained for predicting the polynomial coefficients. The accuracy of the neural network model is tested by predicting an unseen dataset (test dataset) with a high success rate.
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Hoei, Yoshio. "Non―Gaussian-Type Tube-Based Entropy Model for Elastomeric Networks with Polymer Phase Influenced by Filler Loading: Data Analysis for Lightly to Highly Carbon-Black Filled Styrene-Butadiene Rubber Networks." Journal of Macromolecular Science, Part B 56, no. 11-12 (November 28, 2017): 873–93. http://dx.doi.org/10.1080/00222348.2017.1394765.
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Rajaei, Narges, Ghazaleh Rahgouy, Nasrin Panahi, and Nima Razzaghi-Asl. "Bioinformatic analysis of highly consumed phytochemicals as P-gp binders to overcome drug-resistance." Research in Pharmaceutical Sciences 18, no. 5 (2023): 505–16. http://dx.doi.org/10.4103/1735-5362.383706.
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Background and purpose: P-glycoprotein (P-gp) is an adenosine triphosphate (ATP)-dependent membrane efflux pump for protecting cells against xenobiotic compounds. Unfortunately, overexpressed P-gp in neoplastic cells prevents cell entry of numerous chemotherapeutic agents leading to multidrug resistance (MDR). MDR cells may be re-sensitized to chemotherapeutic drugs via P-gp inhibition/modulation. Side effects of synthetic P-gp inhibitors encouraged the development of natural products. Experimental approach: Molecular docking and density functional theory (DFT) calculations were used as fast and accurate computational methods to explore a structure binding relationship of some dietary phytochemicals inside distinctive P-gp binding sites (modulatory/inhibitory). For this purpose, top-scored docked conformations were subjected to per-residue energy decomposition analysis in the B3LYP level of theory with a 6-31g (d, p) basis set by Gaussian98 package. Findings/Results: Consecutive application of computational techniques revealed binding modes/affinities of nutritive phytochemicals within dominant binding sites of P-gp. Blind docking scores for best-ranked compounds were superior to verapamil and rhodamine-123. Pairwise amino acid decomposition of superior docked conformations revealed Tyr303 as an important P-gp binding residue. DFT-based induced polarization analysis revealed major electrostatic fluctuations at the atomistic level and confirmed larger effects for amino acids with energy-favored binding interactions. Conformational analysis exhibited that auraptene and 7,4′,7″,4‴-tetra-O-methylamentoflavone might not necessarily interact to P-gp binding sites through minimum energy conformations. Conclusion and implications: Although there are still many hurdles to overcome, obtained results may propose a few nutritive phytochemicals as potential P-gp binding agents. Moreover; top-scored derivatives may have the chance to exhibit tumor chemo-sensitizing effects.
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Arun, P. L., and R. Mathusoothana S. Kumar. "Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control." Multimedia Tools and Applications, January 6, 2021. http://dx.doi.org/10.1007/s11042-020-10060-y.
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AbstractOcclusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.