作者：
Lu, GuangquanHua, JunZhao, HaoyiLiu, MiaomiaoXu, JinZong, FangBeihang Univ
Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Key Lab Cooperat Vehicle Infrastruct Syst Beijing 100191 Peoples R China Beihang Univ
Natl Engn Lab Comprehens Transportat Big Data Appl Beijing 100191 Peoples R China Beihang Univ
Beijing Key Lab Cooperat Vehicle Infrastruct Syst Beijing 100191 Peoples R China Chongqing Jiaotong Univ
Coll Traff & Transportat Chongqing 100191 Peoples R China Jilin Univ
Coll Transportat Changchun 130012 Peoples R China
Since vehicles are not restricted by lane lines when driving inside intersections, they travel freely and their paths have many possibilities, which are related to the traffic order of other road users. It is necessar...
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Since vehicles are not restricted by lane lines when driving inside intersections, they travel freely and their paths have many possibilities, which are related to the traffic order of other road users. It is necessary to understand and describe vehicle paths through intersections. Previous studies on modeling vehicle paths through intersections have predominantly been limited to vehicles turning in one single direction or specific intersections, ignoring the application universality. This study set out to establish a unified model to describe non-conflict paths of vehicles through intersections. An entrance lane-based coordinate system with geometric parameters was established to describe the position relationships of entrance and exit lanes. Three-order Bezier curves with control parameters were used to describe vehicle paths. By extracting the parameter values from driving simulator-based experiments, the entrance and exit lane-based vehicle path model was proposed to represent the relationship between geometric and control parameters. Actual path data captured by an unmanned aerial vehicle were used for model validation, and results show the validity of the proposed model with a mean Root Mean Square Error of 0.4613 m between observed and fitted paths. More validation considering position distributions of path origins and destinations and angle deviations when entering and leaving intersections was performed. Cases from HighD open dataset also demonstrated the model's potential for lane-changing scenarios. This study provides an exciting opportunity to commonly describe vehicle paths without conflicts at geometrically regular and irregular intersections and is hoped to have the possibility of planning local paths for autonomous vehicles.
Capsicum annuum varieties are highly sensitive to drought. Under water stress conditions, these can show yield losses of up to 70 %. Due to the above, this work proposes a novel approach to obtain estimators of drough...
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Capsicum annuum varieties are highly sensitive to drought. Under water stress conditions, these can show yield losses of up to 70 %. Due to the above, this work proposes a novel approach to obtain estimators of drought stress based on linear regression models for morpho-physiological and biochemical variables in jalapeno pepper (C. annuum cv. jalapeno M), bell pepper (C. annuum cv. california wonder), and serrano pepper (C. annnuum cv. serrano tampiqueno). Jalapeno pepper plants were grown for 69 days under permanent water deficit conditions at 40, 60, 80 % and 100 % of field capacity (FC) (100 % FC as control). Throughout the crop cycle, we monitored the plant's height and weight, basal stem diameter, transpiration, photosynthesis, stomatal conductance, NDVI, and proline. This monitoring allowed us to obtain linear regression models from the accumulated values for these variables, from which the slope values (beta) were used as estimators of drought stress using the interval estimation method, in the same way, this method was used to estimate water status in bell pepper and serrano pepper. For bell pepper, drought levels of 40, 60, 80 and 100 % FC were imposed for 12 days and serrano pepper 60 and 100 % FC for 63 days. The results showed that this method can be used to estimate drought stress in jalapeno pepper for all the irrigation levels through photosynthesis and NDVI and can be applied for bell pepper and serrano pepper using stem diameter and plant height, and in the case of serrano pepper, NDVI showed adequate results. Also, this work establishes the relationship between the jalapeno pepper responses (morpho-physiological and biochemical) to drought stress during vegetative, flowering, and fruiting stages through a Principal Component Analysis (PCA). The PCA found that interaction among morphological, physiological, and biochemical responses change concerning the phenological stage of the plant. The results suggested several direct and inverse relationships
The ridge-type estimators have been intensively studied and modified for the linear regression model. In this article, we introduce a modified unbiased two-parameter estimator (MUTPE) as a new estimator to solve the m...
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The ridge-type estimators have been intensively studied and modified for the linear regression model. In this article, we introduce a modified unbiased two-parameter estimator (MUTPE) as a new estimator to solve the multicollinearity problem for the linear regression model. The MUTPE has been obtained as a linear combination of unbiased two-parameter estimator (UTPE). We give a simulation study to demonstrate the theoretical results. The results of the simulation have revealed that the proposed estimator has better effectiveness than both UTPE and ridge estimators under some circumstance. Finally, we analyzed a real-life data to justify the performance of the modified estimator MUTPE in the context of a linear regression model.
During the emergence of a novel pandemic, predictive modelling process is more important in the phase of public health planning and response. Relating models to data provides a view into unseen variables, such as the ...
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During the emergence of a novel pandemic, predictive modelling process is more important in the phase of public health planning and response. Relating models to data provides a view into unseen variables, such as the occurrence of cryptic transmission and the prevalence of infection. These models allow exploration of counterfactuals and hypothetical interventions. Predictive modelling is a valuable model based on the clear definition and estimation of the variables. Researchers or policy makers who use the model outputs have a clear understanding of what can and cannot be achieved by this method. The results of this study are suggested that substantially more cases were present in many countries than were reported in the official statistics. In this paper we have identified the potential discrepancy between reported cases and true disease burden provided a crucial early warning to the international community. In this research paper we proposed statistical modelling and data-driven computer simulations provided accurate projections of global epidemic dispersal, quantifying the role of physical distancing in places and reductions in international travel on the spatiotemporal pattern of spread of COVID-19 based on linearregression analysis.
作者：
Chvostekova, M.Palacky Univ
Fac Sci Dept Math Anal & Applicat Math CR-77147 Olomouc Czech Republic Slovak Acad Sci
Dept Theoret Methods Inst Measurement Sci Bratislava 84104 Slovakia
In this article we deal with simultaneous two-sided tolerance intervals for a univariate linear regression model with independent normally distributed errors. We present a method for determining the intervals derived ...
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In this article we deal with simultaneous two-sided tolerance intervals for a univariate linear regression model with independent normally distributed errors. We present a method for determining the intervals derived by the general confidence-set approach (GCSA), i.e. the intervals are constructed based on a specified confidence set for unknown parameters of the model. The confidence set used in the new method is formed based on a suggested hypothesis test about all parameters of the model. The simultaneous two-sided tolerance intervals determined by the presented method are found to be efficient and fast to compute based on a preliminary numerical comparison of all the existing methods based on GCSA.
The surge in demand for medical equipment necessitates accurate prediction of talent demand in the medical industry, becoming a crucial support for the strategic planning of talent and human resources in universities....
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ISBN:
(纸本)9798350387780;9798350387797
The surge in demand for medical equipment necessitates accurate prediction of talent demand in the medical industry, becoming a crucial support for the strategic planning of talent and human resources in universities. However, the difficulty in effectively collecting talent data in the medical equipment industry results in limited availability of talent demand data. At the same time, existing research focuses only on the linear or nonlinear single relationship of talent demand sequence data, thereby reducing the accuracy and robustness of predictions. In this paper, we propose a Switching Strategy Algorithm based on Residual Grey model (SSA-RGM) to address the above issue. SSA-RGM is a hybrid model that includes a novel residual grey model, commonly used simple linear regression model, and a switching strategy based on optimal fitness. In this model, we introduce a residual operation based on the grey model and, for the first time, incorporate a penalty factor to mitigate the impact of linear shifts in small-sample data within nonlinear systems. Furthermore, we adopt the principle of optimal fitness to select the best model in the hybrid model. Experimental results on six real-world datasets show that SSA-RGM achieves the best talent prediction performance in various settings.
The main goal of this study was to evaluate the impact of an animation and visualization of data structures (AVDS) tool on both perceptions and objective test performance. The study involved a rigorous experiment that...
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The main goal of this study was to evaluate the impact of an animation and visualization of data structures (AVDS) tool on both perceptions and objective test performance. The study involved a rigorous experiment that assessed the usability, acceptability, and effectiveness of the AVDS tool in solving exercises. A total of 78 participants responded to questionnaires and were exposed to the AVDS tool, after which they completed a performance test, half (39) with the AVDS tool (the experimental group) and half (39) without the tool (the control group). Findings showed that the usability of AVDS was good;the experimental group even perceived AVDS usability as excellent. The results show that perceived usefulness, perceived ease of use, and attitudes toward usage jointly led to positive intention to use the AVDS tool. Furthermore, perceived ease of use was a key factor influencing participants' intention to use AVDS. In addition, the AVDS system improved test results and provided flexibility in use, enhancing learning experience and performance.
Industrial agglomeration (IA), a common industrial phenomenon, has been verified to have a significant impact on total factor productivity (TFP) in many industries. However, the impact of IA on TFP is seldom investiga...
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Industrial agglomeration (IA), a common industrial phenomenon, has been verified to have a significant impact on total factor productivity (TFP) in many industries. However, the impact of IA on TFP is seldom investigated in the construction industry, despite the existence of the industrial agglomeration phenomenon in the construction industry. As such, this study aims to probe into the impact of IA on TFP in the construction industry, so as to provide new insights into the industry development and improvement of TFP in the construction industry. Based on the competing results of the agglomeration effect and congestion effect caused by IA, this study proposed three hypotheses on the impact mechanism of IA on TFP in the construction industry. Then, the non-linear regression model and linear regression model were developed to test the hypotheses based on the provincial panel data from 2002 to 2017 in China. The empirical results reveal that IA has a positive linear impact on TFP in the construction industry, and the impact of IA on TFP in the Chinese construction industry during the observed period is in the embryonic stage. Besides, both the firm scale and economic development level have positive impacts on TFP, whereas the specialization structure has a negative impact. Hence, the government can encourage industrial agglomeration in the construction industry to enhance TFP, in order to leverage the knowledge spillovers, labor pool, and other benefits from IA.
This paper proposes a load estimation method for HVAC system of large public gymnasium. The operation data of HVAC system in large public gymnasium are collected and processed by principal component analysis. Accordin...
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This paper proposes a load estimation method for HVAC system of large public gymnasium. The operation data of HVAC system in large public gymnasium are collected and processed by principal component analysis. According to the data processed, this paper analyses the influencing factors of HVAC system, constructs the linearregression estimation model of system load, and optimises the objective function of load estimation by using genetic algorithm to obtain the optimal solution. Experiments show that the method has strong robustness good adaptability to the model parameters, high fitting degree between the load estimation results and the actual values, and the maximum running time is less than 12.8 s. It can complete the load estimation efficiently and has good performance in practical application.
An accurate State of Health (SOH) estimation is crucial for ensuring the safe and efficient operation of electric vehicles (EVs). However, accurately estimating 5011 in real-world applications is challenging due to di...
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ISBN:
(纸本)9798350317671;9798350317664
An accurate State of Health (SOH) estimation is crucial for ensuring the safe and efficient operation of electric vehicles (EVs). However, accurately estimating 5011 in real-world applications is challenging due to diverse aging mechanisms, which result in varying battery characteristics and complicate the development of a universally applicable 5011 estimation model. To address this issue, this paper investigates the aging characteristics of four INR21700 Samsung 30T cells subjected to different fast-charging protocols. By analyzing the incremental capacity (IC) curves, we identify specific features that effectively represent the aging status across different aging mechanisms. Utilizing these universal features, we develop a linear regression model (LRM) capable of adapting to various aging mechanisms for 5011 estimation. The LRM is trained using data from a single cell and tested on the remaining cells. For the cell with the most similar aging trend, the Mean-Absolute-Error (MAE) is 0.94 %, with an R-2 value of 0.99. Even for the cell with the most distinct aging trend and mechanism, the MAE is 1.70%, with an R-2 value of 0.94. These results demonstrate the robustness and adaptability of the proposed LRM for 5011 estimation under diverse aging conditions.
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