Automotive Systems Engineering

Anti-lock braking systems are widely used in modern vehicles and provide safe driving for many different road conditions. Tire skidding occurs unexpectedly as a result of non-linearity in the system. The system behavior can be modelled and simulated using simulation software, which would help to visualize the system behavior. It would lead to obtaining optimum brake performance as well as safe driving. Modelling and simulation methods that can be used with every component of the system are presented. A variety of simulation software has been discussed.

The Si/SiC Fusion power module approach, which utilizes a single common gate driver in a simultaneous switching configuration, is a promising method for reducing Bill of Materials (BOM) costs without significantly compromising drive cycle system efficiency and, subsequently, battery cost [1]. This contribution explores the application of Si/SiC Fusion power modules for active DC-Link capacitor discharge in automotive traction inverters, motivated by the goal of eliminating discharge resistors and active switches, including gate drivers, to support further vehicle cost reduction. Various continuous and pulsed discharge methods are discussed in this contribution and are evaluated in terms of their specific design challenges and device stress associated with Fusion power modules. To quantify device stress, this paper also compares the different discharge pulses with classical shortcircuit switching events. The discussed discharge methods, as well as short-circuit switching events, are experimentally validated through measurements with inverter systems using off-the-shelf automotive EiceDRIVER™ gate drivers and HybridPACK™ Drive G2 Fusion power modules.

With the rapid rise of the population, monitoring and controlling traffic congestion has become a major concern. Increased vehicle numbers cause numerous issues, including time and fuel waste, air, and sound pollution, and even fatality from trapped emergency vehicles. The Internet of Things (IoT) and data analytics are used in this research to create a realtime traffic management system (TMS). The traffic density is measured using pressure sensors. After evaluating sensor data, the system controller uses a traffic management algorithm to set traffic signal time and uploads data to a cloud server via a Wi-Fi module. The proposed technology may foresee potential traffic congestion at the intersection. If an emergency vehicle is spotted, it will be given precedence, which means it will have a longer signal length to pass through the junction.If an emergency vehicle is spotted, it will be given precedence, which means it will have a longer signal length to pass through the junction. When a car violates a traffic signal, the system may identify it and impose a fine, which can be paid through the Traffic Wallet smartphone app. This proposed solution is costeffective, easy to install, and maintain.

Research Highlights Vulnerability Analysis: Identifies pedestrians in non-upright postures as a critical blind spot in current ADAS, accounting for approximately 7.8% of Japanese traffic fatalities, with true positive rates dropping to 21.4% in low-light conditions. Architectural Innovation: Proposes the Advanced Falling Object Detection System (AFODS), achieving 98.2% detection accuracy through YOLOv7-Tiny spatial recognition and MFCC-based acoustic verification. Regulatory Alignment: Maps detection requirements for fallen road users to ISO 26262 Automotive Safety Integrity Levels (ASIL-D recommended for high-severity scenarios). Ethical Engineering: Replaces utilitarian "Trolley Problem" models with a deontological framework prioritising vulnerable road users through proactive harm prevention.

Supplementary Data: The integrated safety framework logic and ISO 26262 alignment matrices are available at Zenodo: https://doi.org/10.5281/zenodo.18824694

Implementation Framework: The structural integration framework bridging AI perception with the ISO 26262 functional safety lifecycle is available at Zenodo: https://doi.org/10.5281/zenodo.19200140

Keywords: Vulnerable Road Users, ADAS, ISO 26262, Multi-Modal Sensor Fusion, Autonomous Vehicle Safety, Pedestrian Detection, Yolov7, Thermal Imaging, Deontological Ethics, AFODS

In this paper is proposed the measuring of car deceleration, while the vehicle has beenforced to coastdown, in two different loads (partially and fully loaded). Two equations ofmotion are used (considering load effect) to find out two unknown parameters - rollingresistance and aerodynamic resistance

The work aim was preparation of catalysts with various active phase on metal block carriers and test their effectiveness on exhaust gases of the stationary diesel generator at various loadings. The active phase was prepared on the basis of the platinum group metals, oxides of nickel, manganese. The catalyst on the basis of 0.1% of Pt kept the activity during 100 h. test on the diesel generator. The most effective catalyst at all engine operating conditions was the sample containing 5% of nickel oxide and manganese and activated of 0.1% Pt. The catalytic samples on the basis of Ni and Mn promoted by Pd (0.1%, 0.25%) and Pt (0.1%), provide high degree of conversion of CO to CO2, CxHy to CO2 and H2O, NO to N2.

He earned his BS and MS degrees at the University of Oklahoma and he earned his PhD degree at the University of Arkansas. His primary teaching responsibilities are in the freshman and sophomore engineering courses, and his interests include microcontrollers, data acquisition systems, control systems, and engineering education. He is a member of ASEE and IEEE.

To mitigate the degree of damage to passengers caused by automobile collisions, a friction damper was built and used in experimental tests to test its effectiveness in impact energy attenuation. The study revealed that energy absorption capacity of a bumper can be improved with the addition of a friction damper. The results revealed that the addition of the friction damper to an automobile bumper to give a bumper-damper system could attenuate about 32.5 % more energy than with the bumper alone. It can be concluded that the effectiveness of automobile bumpers to withstand impact of vehicles by absorbing the kinetic energy from the impact can be improved with the use of a passive friction damper. That is, a passive friction damper system could be used to attenuate more road vehicle impact energy in collisions.

Módulo 8: Entrenamiento Black Belt
Tarea Clase 5
El análisis estadístico dentro de proyectos de mejora continua, identificar la distribución de probabilidad que siguen los datos es un paso fundamental para garantizar la validez de los modelos y herramientas que se aplican posteriormente. Las distribuciones estadísticas permiten describir y modelar el comportamiento de los datos, brindando una base sólida para tomar decisiones.
El estudio de distribuciones como la normal, lognormal, Weibull, binomial y Poisson, entre otras, es esencial para entender cómo se comportan los procesos y cómo pueden mejorarse. Comprender la distribución que siguen los datos no solo mejora la precisión de los análisis, sino que también incrementa la confiabilidad de las acciones de mejora implementadas en los procesos productivos y administrativos.

In Wankel engines, the negative aspects are high HC emissions and fuel consumption, as well as leakage and wear caused by the apex seals directly contacting the inner surface of the housing. The position of the apex seals in the seal groove is important. The turning angle of the rotor (piston) corner relative to the housing, the thickness of the seal and the tip radius value are important factors that determine the movement of the apex seal in the groove. Within the scope of this study, a solid model of a Wankel engine with basic geometric dimensions and a 13B Wankel engine used in the RX8 vehicle were created. Using Solidworks software, the height variation between the base of the apex seal and the base of the seal groove was analyzed in both solid models for one cycle. An empirical formula was then derived to give this height variation. From the results obtained, it is seen that the values calculated from the formula coincide with the dynamic analysis results of the solid model and consistently reflect the height change. As the tip radius increased, the height between the seal and the housing decreased. This height changes also increased as the maximum turning angle of the rotor corner relative to the housing and the seal tip radius increased. The minimum tip radius value should be at least half the seal thickness. The increase in the height change caused an increase in the maximum spring force.

Insider threats remain one of the most challenging and damaging risks in organizational cybersecurity, often bypassing traditional security controls due to their legitimate access and familiarity with internal systems. Recent advancements in artificial intelligence (AI) and behavioral analytics provide promising solutions for proactive detection of such threats by modeling user behavior, identifying anomalies, and predicting potential malicious actions. This article presents a comprehensive review of AI-driven approaches for insider threat detection, encompassing machine learning, deep learning, and hybrid models, with a focus on behavioral profiling, feature engineering, and real-time analytics. We systematically analyze publicly available and proprietary datasets commonly used in the research community, highlighting their characteristics, limitations, and suitability for various detection approaches. Furthermore, the review identifies emerging challenges, including data scarcity, privacy concerns, model interpretability, and scalability in dynamic enterprise environments. By synthesizing existing methodologies and outlining key research gaps, this study aims to guide future work towards more robust, explainable, and adaptive insider threat detection frameworks.

This quantitative, cross-sectional, case-study-based research investigates how artificial intelligence (AI) applications can improve renewable-energy demand forecasting under climate variability while being adopted in real operational settings. The study addresses the problem that utilities and renewable-intensive enterprises still underuse climate-variability indicators in AI models, limiting the accuracy and decision value of demand forecasts. Using a structured Likert five-point questionnaire and secondary operational and climate data from renewable-energy systems, 280 questionnaires were distributed and 214 valid responses were obtained (76.4% usable rate). Key constructs included organizational analytics capability, data quality and integration, climatevariability integration in AI models, AI model transparency, perceived forecast accuracy, trust in AI outputs, and intention to use AI-based forecasts. Reliability analysis showed Cronbach's alpha values between 0.82 and 0.91, and multiple regression and correlation analyses were used alongside benchmarking of traditional regression and AI models. Compared with a traditional multiple regression model (MAPE 7.8%, RMSE 18.4 MW) and an AI model without explicit climate indicators (MAPE 6.1%, RMSE 15.2 MW), the climateenhanced AI model achieved substantially lower error (MAPE 4.3%, RMSE 11.6 MW). Survey results indicated moderately high perceived forecast accuracy (mean 3.92) and trust (3.88), with intention to use AI forecasts averaging 4.03. Data quality and integration and climate-variability integration were the strongest predictors of perceived accuracy, while perceived accuracy and trust primarily drove intention to use. The findings imply that climate-aware AI forecasting delivers measurable accuracy gains but requires robust data pipelines and analytics capability to be trusted and embedded in renewable-energy planning and operations.

Biodiesel is considered as an environmentally friendly alternative diesel fuel. In the present study, biodiesel from Waste Cooking Oil (WCO) was prepared using the usual transesterification process. Experiments performed on a single cylinder naturally aspirated direct injection diesel engine fueled with different mixed concentrations of biodiesel and diesel fuel, including B0, B10, B20 and B30. The effect of a water cooled Exhaust Gas Recirculation (EGR) rates as 0%, 10%, 20%, 25% and 30% on diesel engine performance characteristics and exhaust emission were investigated. The results show that with increase of % EGR rates, value of CO emissions increases but values of NOx and EGT decrease. Whereas 25% EGR exhibited reduction in both of NOx emission and BSFC by 71% and 45% respectively, 30% EGR demonstrated a slight reduction in both of NOx emission and BSFC as 72% and 23% respectively with respect to the pure petroleum diesel. The recommended EGR is 25% which exhibited highest reduc...

This document presents a real-time case study of AI safety guardrail failure through direct conversation logs. The analysis demonstrates how lawsuit-driven, binary safety controls create multiplicative harm through persistent misclassification of user intent, even when explicitly corrected. The pattern observed is not isolated to a single interaction but represents a structural design failure with exponential degradation characteristics.

el día ~del mes de Diciembre del año 2009 ,el que suscribe Enrique Adan Nuñez Pacheco alumno del Programa Maestría en Ingeniería de Manufactura con número de registro B071375 ,adscrito a SEPI-ESIME-UA, manifiesta que es autor intelectual del presente trabajo de Tesis bajo la dirección del Dr. Jaime Pacheco Martínez y el Dr. José de Jesús Rubio Ávila y cede los derechos del trabajo intitulado "Verificación dimensional de piezas metálicas de acero, mediante la comparación con una pieza patrón, aplicando un sistema de visión artificial para validar su manufactura" , al Instituto Politécnico Nacional para su difusión, con fines académicos y de investigación. Los usuarios de la información no deben reproducir el contenido textual, gráficas o datos del trabajo sin el permiso expreso del autor y/o director del trabajo. Este puede ser obtenido escribiendo a la siguiente dirección

Urban parking has become a critical challenge in metropolitan areas, with drivers spending significant time searching for available parking spaces. This research presents an IoT-enabled parking space detection system using ultrasonic sensors to provide real-time parking availability information. The system comprises HC-SR04 ultrasonic sensors, Node MCU ESP8266 microcontroller, and a cloud-based monitoring platform. Experimental results from a 20-slot parking facility over 30 days demonstrate 96.5% detection accuracy, with an average response time of 1.2 seconds. The system successfully reduced average parking search time by 43% compared to traditional methods. This cost-effective solution (approximately $8 per parking slot) offers scalable implementation for smart city parking management.

In this paper, we present a Robust Sliding Mode Observer for systems with unknown Inputs. The system considered is a vehicle model with unknown inputs that represent the attributes of the road (slope and banking of the road). This parameters affect the vehicle dynamic performance and behavior properties. Thus for vehicles and road safety analysis, it is necessary to take into account this parameters. However, slope and banking of road are difficult to measure directly. In this work we deal with a simple model of vehicle and we develop a method to observe slope and banking of the road.

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.

The outputs of AI-driven robots are inherently unpredictable and difficult to explain due to opacity. It complicates the attribution of criminal liability, which is based on individual culpability. In particular, the scope of negligent liability and the duty of care owed by developers, producers, users, and others remain contested issues in legal discourse. This study addresses these questions through German criminal law dogmatics, offering concrete suggestions. It also examines individual responsibility in product liability cases involving multiple actors within the production chain. The author has long been engaged in research at the intersection of technology and criminal law, with numerous publications. The book will also be available in open access upon publication.

The increasing complexity of textile manufacturing in the industry 4.0 era has intensified the need for forecasting systems that can adapt to dynamic demand patterns, interconnected production networks, and heterogeneous data environments. This article provides a comprehensive review of how Artificial Intelligence (AI) and time-series forecasting techniques are being integrated to enhance operational intelligence within smart textile production. It synthesizes the strengths and limitations of classical statistical models, modern machine-learning architectures, and emerging hybrid approaches that combine linear decomposition with nonlinear learning. The review highlights how interconnected data ecosystems enabled by IoT sensors, RFID tracking, MES/ERP systems, edge-cloud architectures, and digital twins form the backbone of real-time predictive capabilities in contemporary textile factories. In examining recent research and industrial applications, the study identifies key opportunities for sustainability alignment, adaptive learning, and autonomous decision support, alongside persistent challenges related to data quality, interoperability, computational demands, and SME adoption barriers. Finally, the article outlines actionable future directions, including reinforcement-learning-driven forecasting, federated learning, lightweight edge analytics, standardized benchmarks, and sustainability-aware predictive models. By consolidating methodological advances and practical considerations, this review offers a grounded roadmap for deploying intelligent, responsive, and resilient forecasting systems within the evolving landscape of smart textile manufacturing.

Indonesia was 103,672 incidents, and 27,910 people died . Many things have been pursued by the government to reduce the number of accidents such as slogans, advertising, and traffic signs that has the aim to drive safely, but the 2 +2,7668T 4 -81,257. Ключові слова: тертьова термопара, дискове гальмо, визначення температури при гальмуванні UDC 629.

Mechanical keys have traditionally been used to restrict unauthorized access to automobiles. In recent times, microcontrollers were embedded into cars for various applications such as passive keyless entry systems and vehicle immobilizer systems to prevent circumventing the mechanical lock to open the door and then start the engine via short-circuiting the ignition. The embedded electronic systems are very convenient to users but the security of the system can be easily breached for unauthorized access either through theft or lost and found of the car key fob, relay attacks by impostors or if the embedded code is revealed through the wireless interface scanning. In this paper, the development of an Automobile Keyless Entry System using Two-Factor Authentication is described where, the automobile would autonomously verify the users’ alongside the conventional mono-factor (i.e., device-based) automobile key fob authentication framework, thus achieving a two-factor authentication syste...

This article explores the extent to which supply chain practices are implemented by light vehicle manufacturers in South Africa and determines whether there are differences with reference to supply chain practices between manufacturers of different origin (parent companies) in South Africa. A survey was conducted on light vehicle manufacturers in South Africa, using face-to-face questionnaire based on purposive sampling technique. Data was analysed descriptively using Statistical Package for Social Sciences (SPSS) and the findings reveal that overall, light vehicle manufacturers implemented supply chain practices to a great extent. The most highly implemented practices were "building long-term relationships", "cooperation to improve process" and "collaboration on new product development". "Sharing supply chain risk" was the least implemented across the inbound, outbound and internal supply chain. The article also revealed that in few of the best practices, differences were found between manufacturers of European and Asian parent company origin (parent companies of origin).

Total Innovation Management (TIM) is the system of forming an innovative synergy between technology, organization and culture to build innovation competence in organizations. Supply Chain (SC) is a global network used to deliver products and services from raw materials to end-customers via an engineered flow of information, physical distribution and cash. This paper appraised TIM on SC in the automobile industry in Nigeria using structured questionnaire and interview. The upshot indicated 40% of SC executives are more concerned with risk associated with supply and thus invest in insurance policies. 80% of trained SC staff applies ICT tools in visibility (trackability). All SC staff were receptive to innovative ideas, indicating sustainability of TIM ideology in the sector.

Forschungstransfer hat viele Gesichter, aber in den seltensten Fällen eine geradlinige Entwicklung. Am Beispiel modellbasierter, mutationsgetriebener Testfallgenerierung wird die Entwicklung einer Methode von der Konzeptionsphase zu (ersten) kommerziellen Verwertungen erzählt. Anhand dessen werden einige der wirkenden Kräfte und EinĆussfaktoren betrachtet.

Large language models mistake patterns for truth, correlation for causation, and frequency for validity. This position paper argues that without epistemic invariants-first principles that remain constant across transformations-AI remains a sophisticated pattern matcher incapable of genuine reasoning. We propose a domain-agnostic evaluation framework for testing different approaches to implementing spectral epistemic invariants: structured knowledge scaffolds that could preserve truth, justification, and meaning across transformations. The paper diagnoses AI's epistemic blindness using examples from technology domains where AI currently operates, critiques existing approaches as insufficient, then presents theoretical requirements and evaluation criteria for systems that would implement epistemic invariants. Rather than prescribing a specific architecture, we establish metrics and benchmarks for empirically determining which implementation strategies could enable AI systems that truly understand and preserve first principles. Our framework provides the foundation for systematic comparison of approaches to epistemic preservation across any domain where reasoning from first principles matters.

This paper introduces the Trinity-Infinity () framework, a novel conceptual model in which three finite elements (Trinity) interact recursively to generate unbounded outcomes (Infinity). By integrating mathematical representations of infinity with philosophical notions of unity in multiplicity, the framework formalizes a triadic system capable of sustaining infinite dynamics across multiple domains. We apply the framework to three domains: (1) game theory, where it models infinite repeated interactions among three players; (2) mathematical logic, where it extends axiom systems to infinite inferences; and (3) mechanical engineering, where it informs modular designs for scalable systems. Through these examples, the Trinity-Infinity framework demonstrates how finite structures, when organized triadically and subjected to recursion, can yield unbounded systemic behaviors.

In this braking system, exhaust gas from the IC engines is used to operate air brake in the automobiles. Air brake is most used braking system in vehicles. In the proposed model, instead of air brake, exhaust gas is used to operate the brake lever. Exhaust gas from engine is stored in a specially designed pneumatic tank. This exhaust gas pressure is used to operate the pneumatic cylinder and brake lever. Two stoke petrol engine is used in the proposed exhaust gas braking system. Petrol engine is chosen because it produces less impurity in exhaust than diesel engines. This study can also be extended for diesel engines also with suitable design. The main aim of this project is to reduce the work loads of the engine drive to operate the air compressor. In this project, we used exhaust gas from the engine to rotate the generator turbine. Then the power is loaded to the D.C compressor and it is used to the pneumatic cylinder to apply brake. Keywords— consist minimum 4 keywords

The objective of this paper is first to properly define the notion of task clustering. This is the process of automatically mapping functionalities (blocks of code corresponding to a high-level feature) with real-time constraints to tasks (or threads). We aim at reducing the number of tasks functionalities are mapped to, while preserving the schedulability of the initial system. Second, our goal is to expose the complexity of the problem and to sketch methods we will propose for solving this problem. We consider independent tasks running on a single processor.

Artificial intelligence has accelerated global transformation, yet the lack of logical reasoning within machine learning systems limits their reliability, ethical consistency, and adaptability. This research explored how mathematical logic enhances autonomous decision intelligence by integrating symbolic reasoning, probabilistic inference, and algorithmic optimization into reinforcement learning frameworks. Using secondary data from the S&P Global 1200 firms across 31 countries between 2020 and 2024, the study applied multilevel structural equation modeling and machine learning validation to examine logic-driven adaptability in AI systems. The results revealed strong positive relationships between logic-based reasoning and decision accuracy (β = 0.41), probabilistic inference and ethical consistency (β = 0.29), and algorithmic optimization and learning efficiency (β = 0.22), with computational adaptability moderating these effects (R² = 0.71; F = 18.4; p < 0.01). These findings demonstrate that logic-embedded models significantly improve interpretability, transparency, and ethical accountability across autonomous decision frameworks. This research contributes to theory by extending Reinforcement Learning Theory through the addition of mathematical logic as a structural determinant of rational adaptability, thereby broadening its explanatory scope and offering a refined framework for understanding decision intelligence in global digital systems. The study holds practical value for AI governance, corporate automation, and international policy on ethical artificial intelligence. It also highlights how logic-integrated reinforcement systems can align machine decisions with human reasoning, bridging a critical gap in global AI ethics and governance.

Megvizsgaltuk a magyar mezőgazdasagi es elelmiszeripari termekek 30 legfontosabb kulső piacat, elsősorban abbol a szempontbol, hogy ezek mennyire fontosak a szamunkra, mennyire jelentenek stabil, illetve bővulő piacot. Tiz legnagyobb piacunk veszi fel agrarkivitelunk 63%-at, ezen belul Nemetorszag egymaga 17%-ot, Ausztria es Oroszorszag 8-8%-ot. Ugyanakkor a szamunkra legfontosabb nagy elelmiszerpiacokon (Nemet-, Olasz- es Oroszorszag) a magyar export csak marginalis, 1% koruli szerepet jatszik. Kivitelunk dinamikaja orszagonkent nagy eltereseket mutat. Ami a piacok stabilitasat illeti, legnagyobb piacaink egyuttal tartosan a legmagasabbak, azaz stabilnak tekinthetők. A magyar elelmiszergazdasagi kivitel piaci hatosugara atlagosan mintegy 2500 km, igy agrarkivitelunk 90-94%-a Europaba iranyul. = We have examined 30 of the most important export markets of Hungarian agricultural and food products, focusing primarily on their relative importance to Hungary and whether or not they repre...

En el siguiente articulo se describe el proceso de diseno e implementacion de un software construido haciendo uso del lenguaje de programacion Python y las funciones de la libreria de OpenCV que mediante el uso de procesamiento digital de imagenes permite la deteccion del documento cedula de ciudadania dentro de un archivo digitalizado, mostrar dicho documento y guardarlo en un repositorio. Fue posible construir un algoritmo que identifica los contornos de la imagen y la somete a una serie de validaciones para clasificarla como cedula, adicionalmente se identifican los contornos internos del numero de identificacion y posteriormente lo presenta como una variable tipo texto.

In growing motorized towns such as Tirana, urban air pollution poses a substantial issue. This study examines the most detrimental pollutants road vehicles produce and their effects on environment quality and human health. We employ a mixed-methods approach that integrates field data from technical vehicle inspections, official datasets from national transport authorities, and statistical modeling, to assess the relationship between emission parameters and vehicle characteristics such as fuel type, age, mileage, and brand. Our research demonstrates that vehicle age and technological sophistication substantially affect emission levels, with Euro 0-3 vehicles responsible for most pollutants. The study underscores the necessity for fleet renewal programs, enhanced pollution monitoring, and incentives for using low-emission vehicles, as crucial to alleviating environment and health impacts.

In 2014, a future route was planned between Rotterdam and Vienna, where – in the coming years - they want to realize the first cross-Europe corridor of the cooperative, intelligent transport system. Even in everyday life, we hear about the increased use of autonomous vehicles now. We are now able to come across driverless metros from Budapest to Paris. The marketable variation of the different automated vehicles (such as buses, truck, cars) are being developed and tested. We’ve been witnessing the success and failure of some of the early models. According to this, the Europe-wide mobility will – with the networking of the various transportation systems and spreading of the individual and public transportation that is capable of autonomous operation - probably change the verticum of the whole society, which will create one of the essential pillars of the intelligent city.

Innovation is the essence of inventing newer technologies by the means of creating a totally new invention which does not exist or inventing something new by the integration of preexisting technologies together to form a newer and more efficient product. Our project majorly deals with integrating a suspension system and a conventional wheel which enables us to have the luxury of a suspension system/vibration damping where it is not possible to install a independent suspension system and along with this it also reduces the material usage footprint which is eco-friendly for the environment as well. This has been achieved by searching through various research papers available on the internet and we had observed that preexisting designs have made the use of this technology but the main essence of difference between any other design and ours is that our design is the simple integration of the basic concept which can be understood by almost anybody and make amendments/modifications/repairs. Our design consists of a 6 point mounting system on a simple circular wheel which will dampen the vibrations/shock impulses and dissipate kinetic energy suffered by the tire structure and not pass it on to the mounted frame. Here we've used creo parametric to design and Analysis Workbench for stimulate design model.

Alhamdulillah, puji dan syukur kita panjatkan kepada Allah Swt., karena rahmat dan karunia-Nya sehingga saya dapat menyelesaikan makalah ini dengan baik dan tepat pada waktunya. Tak lupa shalawat serta salam selalu tercurahkan kepada Nabi Muhammad SAW yang telah membimbing umatnya dari kegelapan menuju terang benderang. Saya ucapkan terima kasih kepada semua pihak yang ikut mengambil bagian dalam pembuatan makalah ini, kepada para penulis buku-buku yang telah menjadi acuan perancangan makalah ini. Makalah ini disusun guna memenuhi tugas mata kuliah Sistem Penggerak Sepeda Motor. Saya menyadari bahwa makalah ini mempunya kekurangan, karena pepatah mengatakan "tak ada gading yang tak retak". Oleh karena itu, Saya mengharapkan kritik dan saran yang mendukung demi tercapainya kesempurnaan dalam makalah ini. Semoga makalah ini bermanfaat bagi saya khususnya dan bagi yang membacanya.

Questo libro presenta un Marx per molti versi diverso da quello divulgato dalle correnti dominanti del marxismo del XX secolo. Il duplice obiettivo di questo volume è quello di contribuire a una nuova discussione sui temi più classici del pensiero di Marx e, al contempo, di sviluppare un’analisi più approfondita di alcune sue teorie alle quali, fino a poco tempo fa, è stata dedicata poca attenzione.

I contributi di quattordici studiosi di fama mondiale, provenienti da molteplici discipline accademiche e da vari paesi, offrono prospettive innovative sulle idee di Marx riguardo all’ecologia, ai processi migratori, alla questione di genere, al modo di produzione capitalistico, alla composizione del movimento operaio, alla globalizzazione, alle relazioni sociali alienate e alle caratteristiche di una possibile alternativa socialista.

Il risultato è una raccolta di saggi, indispensabile per tutti i lettori di Marx, che suggerisce quanto le sue analisi abbiano oggi una rilevanza ancora più forte che ai suoi tempi.

INS merupakan sistem navigasi berbasis inersia yang paling banyak digunakan, terutama dalam navigasi wahana tak berawak seperti UAV, USV, Rudal, dll. Namun data keluaran yang dibaca oleh Accelerometer dan gyroscope mengandung noise yang berakumulasi seiring waktu dikarenakan proses integral. Untuk mengurangi dan menghilangkan perngaruh noise pada data keluaran sensor navigasi, digunakan filter Kalman sebagai estimator noise. Integrasi menggunakan GPS juga digunakan guna menyediakan sinyal referensi untuk INS, sedangkan algoritma smoothing Savitzky-Golay digunakan untuk memperhalus keluaran dari Kalman filter. Hasil dari simulasi menunjukkan bahwa Kalman filter telah berhasil mengestimasi noise dari sinyal INS, meski masih ada mean error namun nilainya tergolong kecil, yakni [0.0323, 0.0323, 0.0329] m untuk error posisi, [0.0231, 0.0233, 0.0241] untuk error kecepatan dan [0.025, 0.0081, 0.0224] untuk error orientasi masing-masing pada sumbu [x,y,z]. ==================================...

This article presents a comparative analysis of coil springs and air springs used in automobile suspension systems, focusing on their design, performance, durability, cost, and application. Coil springs, known for their simplicity and reliability, offer a consistent spring rate and are widely used in mass-market and performance vehicles. In contrast, air springs provide adjustable stiffness and superior ride comfort, especially under varying load conditions, making them ideal for luxury and commercial vehicles. The analysis highlights the advantages and limitations of each type, including their impact on handling, maintenance requirements, and overall cost over time. The findings suggest that the choice between coil springs and air springs depends on specific vehicle requirements, with coil springs being more suitable for cost-sensitive and performance-driven applications, while air springs excel in scenarios requiring adaptability and enhanced comfort.

Artificial intelligence (AI) and digital public infrastructure (DPI) are transforming public governance and service delivery. However, limited research has examined the integration of AI, particularly deep learning, into DPI systems. This study explores how AI-DPI convergence can improve governance efficiency, inclusivity, and scalability. The central research question guiding this paper is: How can deep learning facilitate the integration of AI into DPI to advance public value? The research applies thematic analysis to 60 peerreviewed publications from 2022-2024, selected for relevance, novelty, and credibility. Key findings identify deep learning as a fusion mechanism enhancing language localization, fraud detection, personalized services, and data fusion within DPI systems. Results show that while such integration holds transformative potential, it also raises ethical, technical, and legacy system challenges. This paper contributes an interdisciplinary analytical framework and policy recommendations, offering actionable insights for future development of intelligent, inclusive digital infrastructures.

Development of computer technologies almost has entered all area of human life. One of the computer&#39;s advantages is contribution of education life. Computer is using in advance level mathematic, technical analysis, driving and animation effects. When advantages of computer come out visual materials have to use and extend in education area. In this study intercooling-turbocharged diesel engine parameters are calculated via technical analysis and also design to programme in Delphi for education then to realize in sample exercises. Thus, it can obtain and examination visual and comparative results in this work condition.

Stress Concentration Factor (SCF) is a critical design parameter in mechanical engineering, representing localized stress amplification caused by geometric discontinuities such as notches, holes, and grooves. Accurate estimation of SCF is essential for preventing material failure and ensuring structural reliability. Traditional methods for SCF determination, such as analytical equations or Finite Element Analysis (FEA), while effective, are limited in scope and computationally expensive, especially when evaluating multiple design iterations or performing real-time optimizations. This study proposes a data-driven approach utilizing The model architecture employs a Bidirectional LSTM network, capable of learning complex, nonlinear relationships within the input parameters. Experimental results demonstrate that the proposed model achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 0.0007 and a model accuracy exceeding 94%. The Predicted vs Actual SCF comparison confirms the model's ability to generalize well to unseen data. Additionally, the model shows consistent training and validation behavior, suggesting robust convergencewithoutoverfitting.

The main purpose of undertaken research was the analysis of risk management, especially, the adequate identification of risk factors and planning actions reducing their adverse effects. Ongoing monitoring of these risks is crucial to achieve the effects of investment project implementation. Design/methodology/approach: The authors present the research results concerning risk management in JZR (Jastrzębskie Zakłady Remontowe) Development Programme. Risk is an immanent element in the activity of a production company. Therefore, it requires an adequate approach in order to achieve the maximization of economic effects. The JZR Development Programme is a good example of the complexity of processes that should be taken into account in risk management of an investment project. Findings: The specificity of machine building enterprises determines the scope and scale of the necessary activities for efficient and effective risk management. The main scientific problem focuses on the analysis and categorization of risk factors. As a result, the authors determine appropriate responses to identified risks in the JZR Development Programme. Originality/value: The development of risk response is crucial for the success of the analysed project. These actions allow to reduce the negative impact of risk factors on the project's timeliness, project's budget as well as economic and technical-technological effects. Another necessary process within risk management is constant monitoring of a project implementation in accordance with the adopted methodology, which enables current updating of emerging new threats to the project implementation and developing necessary actions to minimize the threats.

The traditional automotive homologation processes aim to ensure the safety of vehicles on public roads. Autonomous Vehicles (AV) with Artificial Intelligence (AI) are difficult to account for in these conventional processes. This research aims to map and attempt to close the gaps in the areas of testing and approval of such automated and connected vehicles. During our research into the homologation process of traditional vehicles; functional safety issues, challenges of AI in safety critical systems, along with questions of cyber security were investigated. Our process focuses on the integration of the already existing functions and prototypes into new products safely. As a key result, we managed to identify the main gaps between Information and Communication Technology (ICT) and automotive technology: the rigidity of the automotive homologation process, functional safety, AI in safety critical areas and we propose a solution.

Data acquisition (DAQ) is the link between the physical phenomena of any dynamic system and the computer. This device supplies the associated research applications with high demonstration I/O, industry primary innovations, and lower performance gains in software. This paper presents the design of an anti-lock braking device for ground vehicles within the laboratory and low-cost assembly compared to other designs and the superior control performance of the proposed technique. It uses DAQ for calculation and LabVIEW simulation software to analyse, display, and store data in real-time. The experimental setup with AC motor assembled with the lower cast iron wheel, rubber vehicle wheel, brake pedal, and attached with the hydraulic actuator. The DAQ operated as a link between the computer and the apparatus to demonstrate the performance of the suggested dynamic system. The intelligent active force control technique was merged into the control system and the physical performance of the test rig was presented digitally in LabVIEW software. The results demonstrate the efficiency and favourable reaction of the proposed control approach with the test rig offering superior tracking of 0.21 slip ratio and steady braking on dry roads with a 12.49% reduction in braking distance when compared to other approaches.

The manufacture of clothing using real body measurements is based on user's new profile, on their own desire for individualization through purchased garments but also on the results of the anthropometric surveys which have shown great variability in morphological types, especially for women. Research conducted by the authors focused on the application of 3D virtual try-on in pattern alterations for "women trousers". To achieve the objectives of the work, bodies with atypical characteristics were selected from the database resulting from 3D scanning of the Romanian women population. In the study, it was found that many women have different sizes for the two hip contours (left-right), differences being in the range 1.5 to 4.5 cm. Based on these considerations a method of completion of the patterns for trousers has been developed and applied, by 3D simulation of the body-garment. This paper brings contributions to clothing design technology by 3D virtual try-on, taking into account the body shape of the users.

This paper presents the failure and repair simulation for electric power house with N Turboalternator, where N may be up to 32. The program employs a pseudo-random number generator for individual power plant that can be described by exponential probability density functions. The resulting sequences of failure and repair events are then combined for the plants to give scenarios for different time horizons. The implementation in Excel ® -VBA includes an appropriately designed userform containing the macro Active-X control for input of relevant information. The result shows that as the number of samples increased the behavior of the random events better represented the desired form with a correlation of almost 99% for 25 trials. This corresponds to a confidence interval of better than 95% and hence should be used as the median for practical applications. The results were tested and the distributions of the events were found to be close approximation of the target exponential distributions.