Car Model Research Articles

Prediction-based controller radial neural network for the traction control system

One of the systems needed to improve vehicle safety is the traction control system (TCS). This control mechanism keeps the wheels from slipping too much when the car accelerates, especially when it moves quickly. Because of the significant nonlinear behavior of the tire during acceleration and the unknown impacts of the road surface, it might be difficult to maintain wheel slip in an acceptable range, especially in bad weather. However, some uncertainties, such as unmodeled dynamics and vehicle parameter uncertainty, should be taken into account when designing the controller. Consequently, TCS requires the existence of a strong nonlinear control law. In this study, an analytical design for a TCS controller is made using the method of nonlinear predictive control. The control system’s resistance is then increased by employing an adaptive radial basis neural network (RFNN) to predict the system’s unknown uncertainties. In this study, the controller was designed using half car and quarter car models, respectively. The behavior of the suggested control system for tracking the reference wheel’s slip in the face of uncertainty for various movements is examined in the simulation results that follow. The resulting results have been compared with the simulation results generated from the nonlinear sliding mode controller response used in valid articles in order to provide a more thorough examination of the suggested control system. The findings demonstrate the effective performance of the suggested control mechanism against nonlinear effects and uncertainties.

Разработка предметных графов знаний на основе семантического аннотирования табличных данных

The article outlines an approach and software tool for the automated enrichment of domain-oriented knowledge graphs with new facts derived from semantically annotated tabular data. For semantic annotation of table columns, a combination of three heuristic methods is proposed, leveraging named entity recognition in cells, lexical matching, and feature grouping. This approach is implemented as a dedicated handler within the Talisman software platform. An example and experimental evaluation of the approach during the semantic annotation of columns are provided using a test set of tabular data across six thematic categories: "organization employees," "open vacancies," "car model market," "famous scientists," "book sales," and "tennis player rankings." Evaluation metrics included precision, recall, and F-measure, with final results across all six categories as follows: precision - 79%, recall- 63%, F-measure - 70%. These results highlight the potential of the developed approach for enriching domain-oriented knowledge graphs with new facts from semantically annotated tabular data. The limitations of the proposed approach are also discussedfrom semantically annotated tabular data. The paper also provides a number of limitations of the proposed approach.

Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy.

Target-point interpolation of a program control in the approach problem

For a no Krasovskii Institute of Mathematics and Mechanics, Ural Branch, Russian Academy of Sciences, 620108, Yekaterinburg, Russianlinear controlled system, a fixed-time approach problem is considered in which the target point location becomes known only at the start of motion. According to the proposed solution method, node resolving program controls corresponding to a finite collection of target points from the set of their admissible locations are computed in advance and a refined control for the target point given at the start of motion is determined via linear interpolation of the node controls. The procedure for designing such a resolving control is formulated in the form of two algorithms, one of which is run before the start of the motion, and the other is executed in real time while the system is moving. The error in the transfer of the system’s state to the target point by applying these algorithms is estimated. As an example, we consider the approach problem for a modified Dubins car model and a target point about which only a compact set of its admissible locations is known before the start of motion.

THE STUDY OF THE TECHNICAL CONDITION OF GONDOLA CARS

The article presents the results of the analysis of the technical condition of gondola cars owned by the transport operator BGS rail. The composition of the model range of the car fleet was analysed. It is shown that 14 % of gondola cars produced by Uralvagonzavod have an average service life of over thirty years and have exhausted their service life. Gondola cars of models 12-757 and 12-753 of Kryukov Carriage Works have also exhausted their service life. A positive factor is the presence of innovative cars model 12-7023 produced by KVSZ and 12-4106 model produced by PJSC Dneprovagonmash in the car fleet. Their average age is 6.5 years.An analysis of the main causes of failures that caused the car to uncouple from the train along the route was carried out. In the first place are damage to body elements. This is followed by failures of automatic brakes and wheel sets.Among the damage to gondola car bodies, the first place is taken by malfunctions of the locks on the unloading hatch covers, followed by cracks and breaks in the upper and vertical sheets of the frame cross beams. Broken weld seams of braces and broken linings also pose a serious danger.Failures of auto brakes are caused by loosening of air duct pipes, brake equipment and welded shoes. The main causes of damage to wheel pairs are unacceptable wear of the flange. Among axle box assemblies, the vast majority of failures are caused by excessive heating of the bearings.Failures of automatic coupling equipment are most often caused by fractures of the centering beam and breaks (cracks) of the pendulum suspension. Cracks and other malfunctions of the automatic coupling body are also observed.The bogies mostly have broken springs and mismatched sliding clearances. Also, a quarter of the uncoupled bogies have expired service life of their elements. The average recovery time for gondola cars was analyzed. Due to malfunctions of axlebox units, cars are under repair for more than 15 days, due to failures of wheel pairs - 11.55 days. The least time is spent on auto brakes – 4.2 days.An analysis of the damage to the bodies of various gondola cars models showed that gondola cars of models 12-9745 and 12-9790 are in the worst technical condition. The least susceptible to damage are the «innovative» gondola cars of models 12-7023 and 12-4106.

Xiaomi: An Analysis of Market Strategy under the High Cost-Performance Ratio

Abstract: Xiaomi Corporation, which was established in 2010 in Beijing, is well-known for its smartphones along with other smart hardware as well as IoT devices. It is the second-largest manufacturer of smartphones in the world, behind Samsung, with a market share of about 12%, most of which run on the MIUI (now HyperOS) operating system. It mainly focuses on providing products that are of excellent quality and performance but still maintain affordability which attracts customers from around the world. This research delves into Xiaomi's adaptive marketing tactics as a reaction to evolving market dynamics and changing consumer demands, and examines Xiaomis market plan by looking at three clear instances. The first is how Xiaomi keeps its strong position in the smartphone market by bringing out improved models every year with a steady price tag. This helps to maintain their image of being very good value for money. Second, the choice of the company to put $300,000 for three years into remaking its logo with Japanese designer Kenya Hara. This highlights how redesigning has a strategic effect on brand image and worldwide identification. Thirdly, this study talks about Xiaomis start in the car industry when it introduced the first car model called Xiaomi SU7 in month April. It shows a major shift from making only smartphones to also producing electric cars.

Stacked Generalization with Sequential-Model Based Optimization for estimating Used Car Valuation in Indonesia

In Indonesia, the purchase and sale of used vehicles is a common practice. The valuation of a used vehicle is influenced by several factors, making it challenging to determine an appropriate selling price. To address this issue, this study employs a stacked generalization (stacking) algorithm to integrate Machine Learning (ML) techniques that have demonstrated efficacy in prior research on used car valuations. The Sequential Model-Based Optimization (SMBO) algorithm is employed to achieve high accuracy while ensuring an efficient hyperparameter optimization process. The initial price of a vehicle is undoubtedly a significant determinant of its resale value. However, this fact is frequently overlooked in previous studies on developing car price estimation models. This study makes a contribution to the field by addressing this issue. The use of the initial price as an input for the model enables two distinct types of analysis: one for the assessment of used car prices and the other for the measurement of the degree of residual valuation of used cars in relation to their initial costs. The results demonstrated that the optimized stacking model exhibited superior predictive ability compared to the other algorithms in both analyses. Feature analysis substantiated the considerable influence of the initial price on the used car's price. This study also corroborates the assertion that accurately predicting the valuation of a used car cannot be achieved by solely considering the usage of the previous owner, such as the car's age and mileage. It is crucial to take into account the car's original attributes, particularly its initial price.

Efficient compression of reduced impedance matrices for trimmed structural models: a novel methodology and industrial validation

In response to increasingly strict vibration and noise regulations, software frameworks such as Nastran and Actran have stepped up to the challenge, providing sophisticated solutions to efficiently model damping treatments within numerical methods. One of the available techniques is the representation of trim components as frequency-dependent reduced impedance matrices (RIM) in a frequency response analysis of fully trimmed models. While RIM enables coupling between physical or modal-based components, challenges arise due to large dense matrices. Each matrix is reduced either on physical coupling DOFs or modes of vibration of the component and stored on disk. Since each matrix is dense and large in industrial models, it leads to storage and efficiency issues related to computational and input/output (I/O) operations. This paper introduces a methodology to compress RIMs, particularly for modal components, by selecting the most contributing modes to represent the coupling relationship of each surface. Numerical results on an industrial car body model demonstrate that this approach effectively eliminates low-effect contributions, minimizes storage requirements, and reduces computational time. General Motors' involvement in validating the method adds real-world applicability and reliability to the findings, enhancing its practical engineering value.

The crossover frequency of acoustic simulation for small-scale enclosures by wave and statistic based methods

In the field of room acoustic simulation, wave-based and statistical numerical methods are pivotal for acquiring sound field characteristics. Although the Schroeder Frequency has been utilized to deduce the suitable frequency range of these methods in large enclosures, the crossover frequency warrants further investigation for the small-scale enclosures with complicated inner structures, such as car cabins. This work aims to explore the applicable frequency range of these acoustic simulation methods for a small-scale model, which means identifying the crossover frequency between wave-based Finite Element Method (FEM) and statistical Ray Tracing Method (RTM). First, the crossover frequency was analyzed by the Schroder Frequency equation and modal density estimated from the room transfer functions (RTFs) in a simplified car model, which considering the interior with and without seat occlusion. Further, the crossover frequency was validated by comparing the magnitude spectra of RTFs simulated by FEM and RTM. This work provides a foundational frequency reference for applying statistical acoustic methods in small-scale enclosures.

Применение высокопрочной стали для создания полувагонов повышенной грузоподъемности

Purpose: substantiation of the possibility to increase the load capacity of gondola cars using high-strength steel in the car body structure. Methods: gondola cars load capacity changing analysis. Numerical research of the effect of the steel yield strength value on the mass of the beam structure according to the criteria of strength and stiffness for a beam on two hinged supports operating in bending, and also according to the buckling criteria of a compressed beam. Gondola car body structure finite element analysis in order to confirm the strength and buckling parameters with the normative requirements. Results: the analysis of load capacity of universal fouraxle gondola cars built in the period from the beginning of production of the first Russian cars up to the present time was performed. Results of the analysis of the effect of steel yield strength on the mass of the structure according to the strength criteria and stiffness for a beam on two hinged supports operating in bending, and also according to the buckling criteria of a compressed beam was presented. High-strength steel technical requirements for welded structures of freight wagons is considered. Parameters for evaluating the suitability of rolled high-strength steel for freight wagon construction are given. The results of strength and stability analysis of the gondola car model 12-6744 with high-strength steel, which confirm the design compliance of the car in terms of normative parameters of strength and stability with the requirements of the existing standards are given. The gondola car model 12-6744 advantages are described, as well as the economic effect of its operation for the consignor. Practical importance: the possibility of using high-strength steel to increase the load capacity of gondola cars is substantiated. The gondola car model 12-6744 with high-strength steel was designed. Its advantages are given, and the economic effect of operating such a wagon for the consignor is shown.

The Rhetorical Model of Indonesian Research Article Introductions (RAIs) in the Law Discipline: Problems and Opportunities

The issue of internationalizing academic works into English Scopus-indexed journals has been becoming a growing concern in Indonesia in the last few years. However, investigating rhetorical models of local research articles is still very limited in the Indonesian context to date, while finding this foundation that underpins the global issue may overcome and create awareness for Indonesian academics for the likelihood of publishing their academic papers into reputable journals. This paper attempted to investigate the rhetorical model of Indonesian Law RAIs because none has investigated it in this selected discipline. The purpose is to discover whether or not it is different from earlier findings in English and Indonesian models. The data in the present study are 15 Indonesian Law RAIs published in accredited Science and Technology Index journals (SINTA 2). For the analysis, the study employed a genre-based study using two rhetorical models as primary code analysis. The results revealed that the Indonesian Law RAIs in the present study have a different rhetorical model from the English model, the CARS model. The main difference is the ways of establishing a niche. This finding implies that it may become an obstacle for Indonesian authors in this discipline, on the one hand, but may become an opportunity for them to publish their academic works in reputable journals, on the other hand.

Experimental Investigation of Mechanical Behavior of CFRP-AA7075-T6 Composite and Finite Element Analysis for Automotive Wheel Rim Application

Lightweight with high strength material is the new requirement of the modern world and composite materials are the best option as the solution. Owing to high strength to weight ratio and corrosion resistant of Carbon Fiber Reinforced Polymer (CFRP) compared to aluminum (AA7075-T6), they are well suited to fulfill the demand of modern needs. The present work investigates the mechanical behavior of CFRP-AA7075-T6 composite material experimentally. Finite element analysis (FEA) has also been conducted for the evaluation of stress, strain, and deformation for the application as automobile wheel rims. CFRP-AA7075-T6 shows superior to AA7075-T6 in terms of flexural strength. Impact test reveal that the prepared composite absorbed 11% more energy than AA7075-T6. The strength-to-weight ratio of the prepared composite is 73% higher than that of AA7075-T6. FEA of automobile wheel rim models of prepared composite gives better results than the AA7075-T6 sample. CFRP-AA7075-T6 composite exhibits the lower deformation and equivalent elastic strain of 18.11% and 17.26% respectively than AA7075-T6 at same load level.

Automobile exterior emotional design method based on deep learning and multiple views imagery integrating calculation

In the context of the increasingly dynamic experience economy, users’ emotional experience with automobile has become a leading factor in consumption, making automobile exterior crucial carriers of emotional needs and design expression. However, users’ emotion expression is from three-dimensional automobile entities, with a single perspective potentially causing deviations. Thus, this paper presents a novel automobile exterior emotion design method based on deep learning and multiple views imagery integrating calculation. This approach enables a seamless transition from users’ emotional needs to automobile exterior design. First, we explore users’ emotional evaluation of automobile exterior using the semantic differential method. The improved model of ResNet101-Attention mechanism was used to predict users’ emotion. Additionally, based on above emotional evaluation values, this study proposed a calculation method of multiple views automobile imagery integrating using the Analytic Hierarchy Process and Entropy Weight, calculate the automobile imagery weights of different views, and obtain the final automobile exterior emotional imagery. Finally, the Captum toolkit and the LoRA fine-tuning training technique are used to construct an automobile exterior design optimization generation model, producing a design that meets users’ emotion needs. By comparing the results obtained using both manual and automatic evaluations, and comparing to classical deep learning models. we verify the model’s validity and the advantages of the proposed method. This intelligent method assists designers in quickly identifying users’ responses for automobile design and provides automobile design that meet their emotional needs

Fuzzy Petri Nets for Traffic Node Reliability.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node's safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases.

경차 브랜드의 헤드라이트에 적용된 디자인 조형성이 소비자 만족도 및 구전의도에 미치는 영향

Design is an important factor in the compact car market beyond economic feasibility and practicality, especially headlights that determine the first impression of the vehicle and greatly influence the brand image. The purpose of this study is to analyze the effect of design formativeness applied to the headlights of compact car brands on consumer satisfaction and word-of-mouth intention. To this end, case studies and empirical studies were conducted on major domestic compact car models, and structural equation model were used for the data collected through the survey. As a result of the study, it was found that formative factors such as form, size, and space of compact car headlights had a positive effect on consumer satisfaction, and this satisfaction led to positive word-of-mouth intentions of consumers. On the other hand, the material factor had no significant effect. This result suggests that compact car manufacturers should emphasize visual elements and move toward improving the consumer experience when developing design strategies. In addition, it was confirmed that there is a need to strengthen strategies to promote positive word-of-mouth by efficiently allocating design resources. In conclusion, in order to secure competitiveness in the compact vehicle market, a strategic approach that considers design originality and technical efficiency at the same time is needed, and it will be important to consider customized marketing strategies that consider the characteristics and strengths of each vehicle.

Automobile response to road safety barriers upon collision

During a collision between an automobile and a road safety barrier, the vehicle will go through many behavior transition phases. This research will focus on determining the behavior of two car models including a sedan car and a pickup truck in collision with two safety road barriers using numerical simulation. This paper will also present images, total force graphs, and velocity graphs throughout the process to evaluate the vehicle's behavior at different impact speeds and angles. It is recognized that at similar impact speeds, collision angles, and barrier types, the pickup truck suffers more damage than the sedan. In addition, the pickup truck colliding with a concrete barrier exhibits the highest force, while the sedan colliding with a W-Beam barrier records the lowest force among the four collision types. Regarding the road safety barrier, the concrete barrier has a shorter force increase/decrease duration than the W-Beam barrier.

Mathematical modeling insights into improving CAR T cell therapy for solid tumors with bystander effects

As an adoptive cellular therapy, Chimeric Antigen Receptor T cell (CAR T cell) therapy has shown remarkable success in hematological malignancies but only limited efficacy against solid tumors. Compared with blood cancers, solid tumors present a series of challenges that ultimately combine to neutralize the function of CAR T cells. These challenges include, but are not limited to, antigen heterogeneity - variability in the expression of the antigen on tumor cells, as well as trafficking and infiltration into the solid tumor tissue. A critical question for solving the heterogeneity problem is whether CAR T therapy induces bystander effects, such as antigen spreading. Antigen spreading occurs when CAR T cells activate other endogenous antitumor CD8 T cells against antigens that were not originally targeted. In this work, we develop a mathematical model of CAR T cell therapy for solid tumors that considers both antigen heterogeneity and bystander effects. Our model is based on in vivo treatment data that includes a mixture of target antigen-positive and target antigen-negative tumor cells. We use our model to simulate large cohorts of virtual patients to better understand the relationship involving bystander killing. We also investigate several strategies for enhancing bystander effects, thus increasing CAR T cell therapy’s overall efficacy for solid tumors.

Wheel Slip Equilibrium Point Model Reference Adaptive Control Based PID Controller for Antilock Braking System: A New Approach

This paper presents a new approach to wheel slip control in Antilock Braking System (ABS) using an Approximated First Order Wheel Slip (AFOWS) Model Reference Adaptive Control (MRAC) based PID (AFOWS-MRAC-PID) controller. An ABS was modeled in a MATLAB/Simulink environment using a quarter car model with the proposed controller. Simulations were conducted with a wide range of adaptation gains (50, 100, 150, 200, and 250) to study the effectiveness of the proposed control system. The results revealed that the proposed system could track and maintain 10% wheel slip and eliminate oscillation (instability) in terms of overshoot associated with conventional PID controllers, particularly on wet and snowy road surfaces, using adaptation gains of 150, 200, and 250. Overall, the proposed system provided the best performance in terms of stopping distance, vehicle braking velocity, and braking torque on all road surfaces with an adaptation gain of 250, although braking on dry road surfaces was the most effective.

Hygroscopic ionogel for enhanced thermoelectric generation performance

Improving thermoelectric generators (TEGs) performance remains challenging in the context of energy crisis and thermal-pollution. Here, we present a strategy for thermal management and performance enhancement of TEGs by sustainable evaporative cooling utilizing highly hygroscopic and adhesive ionogels (PIGs). Rational swelling and poly-[2-(Methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (PDMAPS) chains with group interactions prevent lithium chloride (LiCl) and 1-ethyl-3-methylimidazolium acetate ([EMIM][Ac]) leakage, while carbon nanotubes (CNTs) and MIL-101(Cr) optimize the evaporative cooling of PIGs. PIGs possess high sorption (252.72% at 25 °C, 90% RH for 12 h) and steady sorption-desorption kinetics. Meanwhile, PIGs exhibit high adhesion (130.89 N m−1) on TEGs. The evaporative cooling of PIGs enhances the temperature difference of TEGs. The potential of PIG-TEG is increased by three times at heat source temperatures of 50–80 °C, and the output power density stabilizes at ∼706.25 mW m−2 after heating at 50 °C for 1 h. Moreover, the PIG-TEG maintains stable output enhancement for prolonged time (over 24 h). Additionally, we integrate PIG-TEGs for the durable power supply of devices and design a movable model car, which utilizes waste heat for self-powering. PIGs realize effective thermoelectric output enhancement of TEGs, and provide ideas in clean energy conversion, wearable devices, and mobile power.

EXPRESS: How Do Brands Change Their Advertising Spending in Response to a Rival’s Product Recall?

A brand manager can interpret a rival’s product recall as an opportunity to preempt sales and/or signal superior quality by raising their brand’s ad spending. Conversely, they may interpret the recall as a threat that may harm their brand’s image and/or lead buyers to draw unfavorable comparisons between their brand and the recalling brand. This interpretation nudges the manager to suppress their brand’s ad spending. The authors test the interpretations empirically in the context of 62 substitute car models’ responses to the recall of a competing model. They assess the response over 31 weeks and 308 geographical regions, leading to 591,976 model-week-region observations. Regression discontinuity in time analysis reports that, on average, a substitute brand responds by lowering its ad spending by 50%, suggesting that the threat interpretation dominates the opportunity interpretation. A decomposition of spending by type suggests that substitute brands increase their spending on price advertising by 25%, decrease spending on quality advertising by 71%, but make no adjustment to brand advertising. This nuanced analysis suggests that substitutes attempt sales preemption, avoid quality signaling, and are not worried about brand spillover. A follow-up analysis reports that this advertising strategy strengthens the positive spillover effect of a brand’s recall on its substitute brands’ sales volume. The key findings hold for another major automobile recall event in the same market. The findings contribute to the literature on the management of quality perceptions while informing about substitute brands’ managers responses to a rival brand’s quality failure and whether the response helps or hurts the substitutes’ sales. Further, the findings build an empirical foundation for future analytic investigation on strategic interactions among brands when a quality defect occurs.