Car Model Research Articles

Generic Moves in The Literature Review Section of PhD Dissertations

The dissertation/thesis as an academic genre has evolved in academic circles in the past 30 years or so. Research into various aspects of the academic genres in general and thesis writing, in particular, started more than two decades ago. However, not much research has been carried out regarding the theorizing of literature review writing as a separate genre in theses, especially in non-native English contexts. The present study aimed to fill the theoretical voids in the research area of Literature Review (LR) section in thesis writing in Pakistani academic settings; thus this study probes into the process of construction of literature reviewing to find out that how students organise this section and deal with the potential problems as an analytical piece of writing with sound argumentation. The current study has investigated the schematic patterns in the literature review through the framework of move analysis in genre theory. The data was drawn from fifteen (15) doctoral theses in the field of English linguistics. The methodology allows for an in-depth coding of the LR chapters in theses to investigate the rhetorical organization. In the coding process, the study has also taken insights from the two previous models of Kwan (2006) and Swales’ seminal CARS model (1990) in order to study the moves (and steps) as rhetorical units. The results of the study reflect the dominant presence of Move 1, Move 2 and Move 4, whereas the employment of Move 3 is comparatively less which justifies the worth of writer’s work. This reveals that the students resort to these moves more often to perform a ritual of literature reviewing and in dire need to be trained enough to craft the LRs argumentatively. Thus, researchers have proposed a revised model to overcome the shortcoming.

Trustworthy Residual Vehicle Value Prediction for Auto Finance

The residual value (RV) of a vehicle refers to its estimated worth at some point in the future. It is a core component in every auto financial product, used to determine the credit lines and the leasing rates. As such, an accurate prediction of RV is critical for the auto finance industry, since it can pose a risk of revenue loss by over-prediction or make the financial product incompetent by under-prediction. Although there are a number of prior studies on training machine learning models on a large amount of used car sales data, we had to cope with real-world operational requirements such as compliance with regulations (i.e. monotonicity of output with respect to a subset of features) and generalization to unseen input (i.e. new and rare car models). In this paper, we describe how we coped with these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.

A comprehensive methodology to obtain electrical analogues of linear mechanical systems

Electrical analogues of linear mechanical systems have traditionally been obtained using a set of rules that can potentially be tedious when applied to complex systems. In addition, such rules do not cover all the possibilities of mechanical systems including rotational and translational generalized coordinates. The present work establishes a comprehensive and systematic methodology for obtaining the electrical analogue of any mechanical system modelled by rigid bodies, springs, and dampers. The method is simple and straightforward. First, the dynamic equations of the mechanical system are derived into matrix form. These equations are then translated to the electrical domain by means of the electromechanical analogy and, finally, the electrical circuit is obtained by direct inspection of the matrices. In addition, the method can deal with a combination of translational and rotational coordinates in the definition of the mechanical model. To support the proposed method, and beyond the planar vehicle models found in the literature, the electrical analogue of a linear 3D 7-DOF full car model is presented in this work.

DESIGNING READING-WRITING TEACHING STRATEGY FOR THE ACADEMIC WRITING COURSE

The concept of integrating reading and writing in the language instruction has been portrayed through an increasing number of research studies. Nevertheless, many areas remain to be explored. Even though previous research studies often portrayed the beneficial impacts of integrating reading and writing, yet they do not suggest how such impacts affect the students in a specific course. Hence, the present research study aims to propose a design of reading-writing teaching strategy that needs to be addressed within a specific course especially the academic writing course. This research study employed the adapted model of ADDIE (Dick et al., 2015) to develop the proposed teaching strategy. The process of need analysis was carried out through analysing the introduction section of research articles written by the undergraduate students using Swales and Feak’s CARS model (2004) and examining the syllabus of reading and writing courses. Meanwhile, as the primary frameworks for the teaching strategy development, the rhetorical reading strategies and academic writing were adopted. The results of need analysis show that, mostly, undergraduate students’ research articles lack 1 step in Move 1, 2 steps in Move 2, and 3 steps in Move 3 particularly in the introduction section. The omission of steps in the CARS model by these student-authors seems to happen due to two major factors, namely the scarcity of (1) control over the writing process and (2) linguistic resources. Hence, suggesting the reading-writing teaching strategy for academic writing courses is considered essential.

The Design of Electric Fire Engine

The electric fire engine is based on the single chip microcomputer as the control core. In order to better realize the fire extinguishing function, the latest electric fire engine has added the tracking module, wireless transceiver module, etc., to realize the intelligent fire engine to detect the fire source and extinguish the flame. This design combines the principle of single chip microcomputer with the hardware and software circuit program to realize the function of automatic tracking and fire extinguishing, without the fire personnel risking their lives to carry out on-site operation. This kind of electric intelligent fire truck adds some practical small functions, brings us a lot of convenience, can better serve human beings. The main model of this design is a small toy car model with universal wheel and motor control wheel. The chassis is installed with a variety of sensors with different functions to achieve the purpose of moving forward and backward, steering and avoiding obstacles and eventually extinguishing the fire source. At the same time, the car can also realize wireless remote control. By comparing the shortcomings of the traditional fire truck, some improvements are made, the circuit diagram of intelligent control is designed, and the method of wireless control car for fire fighting is explored with the combination of practice and thinking, so as to further improve the fire truck and make it intelligent. The main purpose of this design is to reduce the fire casualties and improve the need for fire personnel to take the risk of life operation of the traditional fire truck function. Its advantages are automatic tracking, avoidance of obstacles, wireless control, measuring the source of fire.

Combined application of gamma radiation, cleaning and chemical sanitizers in decontamination of vehicle air conditioning filters

This work aimed to analyze the fungal contamination of air-conditioning filter waste (n=15) as an indicator of Quality Air Indoor from different car models in São Paulo city in São Paulo State, Brazil, during the period from October 2018 to July 2019. Three different treatments were used for the decontamination of car air conditioning filters, such as mechanical vacuum cleaning (I), vacuum cleaning and use of sanitizing product (II), and sanitizing product associated with radiation treatment at a dose of 17 kGy (III). After the treatments, microbiological analyses were performed and samples were plated in Petri dishes containing Sabouraud agar transferred by Swabs, and incubated for 7 days at 25 °C. The Petri dishes were stored in a standard Biochemical Oxygen Demand incubator, for the growth of fungal cultures. After incubation, the fungal cultures were evaluated, and the fungal counting was expressed in unit-forming colonies (UFC) and frequency in samples (%). The fungi were examined by lactophenol blue solution staining for microscopy. All samples of treatment I and II were contaminated with various fungal genera and high bioburden, namely (treatment I) Alternaria alternata, Aspergillus flavus, A. niger, Cladosporium spp., Fusarium spp., Mucor spp., Nigrospora spp., Not Sporulated Fungi (NSF), Penicillium spp., Rhizopus spp., Rhodotorula spp., Trichoderma spp. and yeasts. Treatment II showed Alternaria alternata, Aspergillus flavus, A. fumigatus, A. niger, A. ochraceus, Cladosporium spp., Mucor spp., NSF, Penicillium spp., Phoma spp., Rhizopus spp., Rhodotorula spp., Trichoderma spp., and yeasts. Treatment III presented NSF and yeasts, with 80% of material decontamination.

An Optimisation Technique with the Method of Construction for Vehicle Fuel Consumption and Emissions Using Incomplete Block Designs with Some Special Types of Graphs

Background: The main key input variables to this optimization technique for constructing incomplete block designs are using bipartite and spanning subgraphs through numerical examples of vehicle fuel consumption and emissions. The theory of graphs plays a significant role in mathematical sciences and engineering technologies. In addition, the graph models many relations and processes in physical, biological, social, and information systems. Aims and Objectives: The construction methods using Partially Incomplete Block Designs (PBIBD) with differential equations through bipartite and spanning subgraphs that predict hot stabilized vehicle fuel consumption and emission rates for different drivers using different cars are studied in this paper. The other modelling of fuel consumption and emissions have appeared as an essential tool, which helps to develop and measure vehicle techniques and to help estimate vehicle fuel consumption and emissions. This paper aims to develop an optimization technique for the construction method for incomplete block designs LSD with PBIBD(2) through vehicle fuel consumption and emissions. Method: An incomplete block design can be constructed using LSD statistical analysis with bipartite and spanning subgraphs. First, the method for the construction of LSD using bipartite graphs. The second method for the construction of PBIBD(m) using spanning subgraphs. The two construction methods are through numerical examples of an oil company testing five mixings of petrol for fuel efficiency and emission according to the variability of five drivers and five models of cars. Result: The inference of the first model of PBIBD(2) using LSD F-value of 0.08 implies the model is not significant (P-values greater than 0.05). The second model has no significant difference between petrol fuel efficiency and emissions. In the third model, there is no significant difference in fuel efficiency between different cars of petrol bunks. Conclusion: Finally, it is concluded that the response variable is represented above the maximum quality scores from our fourth driver using a second car to the fourth petrol bunk in fuel efficiency.

Deep Learning-Based In-Cabin Monitoring and Vehicle Safety System Using a 4-D Imaging Radar Sensor

In this paper, we use a multiple-input multiple-output (MIMO) radar for in-vehicle passenger detection. We propose a 2D CNN-LSTM (Convolutional Neural Network- Long Short-Term Memory) to accurately detect, count and classify passengers inside five-seater vehicles. Our deep learning model first extracts feature using a CNN model, and then a series of frames will be delivered to a time series model (LSTM) to predict new scenarios. Additionally, we provide the outcomes of various deep learning models and show that temporal deep learning models perform better in our radar datasets. Furthermore, we provide reliable session-dependent datasets collected from different car models with various passengers (including infants/ children and adults). The results show that our proposed 2D CNN-LSTM model can detect unattended infants/ children in vehicles with more than 95% accuracy, count passengers and identify their occupied seats with an accuracy of 89% and classify passengers with more than 74% accuracy. Since our model is evaluated in a new car with new passengers, it ensures the generality of our proposed method to be deployed in any five-seater vehicle.

Convex Approach to Data-Driven Off-Road Navigation via Linear Transfer Operators

We consider the problem of optimal control design for navigation on off-road terrain. We use a traversability measure to characterize the difficulty of navigation on off-road terrain. The traversability measure captures terrain properties essential for navigation, such as elevation maps, roughness, slope, and texture. The terrain with the presence or absence of obstacles becomes a particular case of the proposed traversability measure. We provide a convex formulation to the off-road navigation problem by lifting the problem to the density space using the linear Perron-Frobenius (P-F) operator. The convex formulation leads to an infinite-dimensional optimal navigation problem for control synthesis. We construct the finite-dimensional approximation of the optimization problem using data. We use a computational framework based on the data-driven approximation of the Koopman operator. This makes the proposed approach data-driven and applicable to cases where an explicit system model is unavailable. Finally, we apply the proposed navigation framework with single integrator dynamics and Dubin's car model.

CACTO: Continuous Actor-Critic With Trajectory Optimization—Towards Global Optimality

This paper presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framework. The motivations behind this algorithm are the two main limitations of TO and RL when applied to continuous nonlinear systems to minimize a non-convex cost function. Specifically, TO can get stuck in poor local minima when the search is not initialized close to a “good” minimum. On the other hand, when dealing with continuous state and control spaces, the RL training process may be excessively long and strongly dependent on the exploration strategy. Thus, our algorithm learns a “good” control policy via TO-guided RL policy search that, when used as initial guess provider for TO, makes the trajectory optimization process less prone to converge to poor local optima. Our method is validated on several reaching problems featuring non-convex obstacle avoidance with different dynamical systems, including a car model with 6D state, and a 3-joint planar manipulator. Our results show the great capabilities of CACTO in escaping local minima, while being more computationally efficient than the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) RL algorithms.

An adaptive metamodel-based global approximation method for black-box type problems

An adaptive metamodel-based global approximation (AMGA) method for solving the global approximation problem of black-box models in large design domain is proposed in this study. The method employs the RBF model to compute the Hessian matrix and a heuristic direct search algorithm DIRECT to find the maximum curvature point of the metamodel surface, through which the design domain is split to obtain additional sampling points and to achieve fast update and fast valuation of the metamodel. The initial design domain is split into a series of sub-design domains by continuous iterations, and the metamodels built within the various sub-design domains achieve the global approximate model of the entire design domain. To demonstrate the final effect of the global approximation model and design domain splitting, six common two-dimensional test functions are chosen. The AMGA method is further tested using seven typical test functions and compared to other sampling and metamodel updating methods, with the findings demonstrating the usefulness of the proposed method in the low-dimensional scenario (less than four variables). Finally, the AMGA method is applied to a sophisticated electric car model, yielding good results.

Rhetorical structures, strategies, and linguistic features of problem statement to promote a teaching writing model

Studies about rhetorical moves in the introduction section have been widely conducted. However, the studies focusing on the research problem statement is still limited. This current study investigated the rhetorical structures, strategies, and linguistic features of the problem statement written by undergraduate students, which aimed to contribute to the teaching model in teaching academic writing. This research was a qualitative case study belonging to genre analysis, using Swale’s CARS model. Thirty undergraduate students’ problem statement texts were explored, and interviewers were further conducted to collect the data. The results indicated that nine types of rhetorical structures were employed, most of which were characterized as incomplete. The rhetorical strategies employed by the students were making a centrality claim, making topic generalizations, reviewing previous research, indicating a gap, establishing need and problem, continuing a tradition, outlining purposes, and announcing the state of the present research. Specific linguistic features were used in each move and step although the students admitted that they had lack of knowledge with linguistic features. Based on the research finding, a Critical Genre-Based model for teaching problem statements in writing is proposed because this model has facilitative effect on consciousness-raising of the genre’s rhetorical structure.

An insight into conversion of internal combustion engine (ICE) vehicle to electric vehicle for green transportation technology

An electric car is a type of alternative-fuel vehicle that uses electric motors and motor controllers instead of an internal combustion engine. Power is provided via battery packs rather than carbon emission-based fuel. It offers a variety of advantages over typical internal combustion engines, most notably lower local emissions and higher energy efficiency. The aim of this research is to modify the vehicle running with an internal combustion engine into an electric vehicle for transferring material from one place to another inside the industrial area. For this purpose, a Maruti 800 car model has been selected, and during the field work, all unwanted parts of the IC engine are removed to convert it into an electric vehicle. The analytical analysis has been carried out to calculate the power transmission capacity and the speed of the vehicle. The finite element analysis is also carried out to check the strength of reused parts like belt drives and crankshafts. A Simulink model of an electric vehicle is prepared to check its performance and provide suggestions during the vehicle's assembly. The vibration response has proven that the source vibration, the noise, vibration, and harshness (NVH) issues in electric vehicles (EV) differ significantly from those in internal combustion engines (ICE).

Two-Layer Parallel Fuzzy Logic Controller Design for Semiactive Suspension System with a Full Car Model

This paper presents research results on semiactive suspension using a fuzzy logic controller. Firstly, a full car model is built with 7 degrees of freedom using a semiactive suspension system at all four wheels. The dynamic equations are converted to the state space representation to facilitate control design and improve accuracy in simulation design. Then, a two-layer parallel fuzzy logic controller is designed with 49 and 25 rules for two fuzzy inference systems. In this way, the fuzzy logic controller is able to respond to a wide operating range of the system and the output force is smoother. Finally, the evaluation results are performed in the frequency and time domains with a real random road profile, and they showed that the root mean square value of the signals when using a semiactive suspension system with the proposed fuzzy logic controller decreased by over 25% compared to the passive suspension system. This clearly demonstrates the effectiveness of the proposed controller in improving the ride comfort, the road holding and ensuring the suspension travel of cars.

Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set.

Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks

This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously.

A cellular automata model for car–truck heterogeneous traffic flow considering drivers’ risky driving behaviors

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.

H∞ cruise control for high-speed trains with process and data transmission time-varying delay

This paper investigates an H∞ cruise control strategy for uncertain high-speed trains (HSTs) with two time-varying delays. The multibody model of the cascading car is established, taking into account the influence of the flexible coupler and the basic resistance (rolling mechanical resistance + aerodynamic resistance) in the actual operation process. There are many uncertainties in the actual operation of the train, such as disturbances caused by gusts and track irregularities and time-varying delays caused by flexible couplers. Based on the above factors, this paper fully considers and supplements the multibody model. A cruise control strategy based on Lyapunov stability theory and linear matrix inequality (LMI) is proposed. The state feedback control rate with input short time delay is designed, and a cruise control method with double time-varying delays is proposed by sampling data. Constructed Lyapunov–Krasovskii function (LKF) with time-delay parameters for time-delay systems to fully introduce the information of time delays. In view of the disturbance effect, the sufficient condition to suppress the disturbance is given. The proposed H∞ cruise control method controls the fluctuating speed and displacement of the train to quickly converge to the steady state. The HST at equilibrium tracks the desired velocity and ensures a stable relative displacement. The specified H∞ interference attenuation level is achieved to ensure safe and comfortable operation. Two numerical simulations are carried out to illustrate the effect of state time-varying delay and disturbance on train stationarity, which proves the superiority of the proposed method.

Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia

BackgroundMillions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia.MethodsMalaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran’s I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie’s model “BYM”) known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia.ResultsAverage rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020.ConclusionThe study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).

The 1000 mouse experiment: quantifying the variability of tumor response to CAR T cell therapy

Abstract Chimeric Antigen Receptor (CAR) T cell therapy is a type of cancer immunotherapy designed to target specific antigens on the surface of tumor cells using engineered T cells. While effective, there is often variability in patient responses and even within an experiment given the same experimental conditions. Analyzing this variability may help identify key modalities to successful CAR T therapy. We present a platform to explore this variability by quantifying the luminescence signal from in vivo tumor growth over time in a mouse model of human CAR T response. We developed a Python toolkit (https://github.com/soorajachar/radianceQuantifier) to automate labeling, cropping, quantification, and plotting of bioluminescent tumor image data. Applying this toolkit to digitize dozens of prior in vivo experiments conducted over several years allowed us to generate a dataset consisting of over 1000 mice. We then performed statistical analyses and mathematically modeled the time dynamics of tumor responses to CAR T cells in this dataset. We identified three distinct phases of tumor response: initial tumor growth, tumor decay due to killing by CAR T cells, and tumor relapse. These phases were fit to differential equations to extract the growth and decay rates of the tumor for each mouse. Upon fitting our model to the experimental data, we found that the growth rate of relapsed tumors was slower than the initial tumor growth rate within individual mice, suggesting a selection of edited tumor cells post CAR T cell therapy. Future work will leverage our quantitative framework to explore the alterations between relapsed tumor cells and those that are eradicated by CAR T cells. Supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute (Bethesda, MD)