Browse Topic: Computer software and hardware

Items (6,382)
The resource-intensive process of road testing constitutes an essential part of the development of powertrain software. A significant proportion of explorative tests and adjustments for use in service are conducted during the vehicle test phase. However, the observed trends of decreasing development cycles and increasing system complexity generate a field of conflicts. In order to address this issue, this paper proposes road test emulation as a data-driven approach for continuously adapting powertrain software to the evolving overall system. A dedicated data strategy is designed to enhance customer-oriented software development. Therefore, test scenarios equivalent to in-service conditions are determined based on customer data. These test scenarios enable an emulation of road testing and the analysis of the system in a real-world operational context from the early stages of the product development process. System-specific data from the vehicle under development itself is utilised to
Martini, TimKempf, AndréWinke, FlorianAuerbach, MichaelKulzer, André Casal
With the rapid development of Internet of Vehicles (IoV) and cyber-physical systems (CPS), connected autonomous vehicles (CAVs) have also developed rapidly. However, at the same time, in-vehicle networks also face more security challenges, mainly in terms of resource constraints, dynamic attacks, protocol heterogeneity, and high real-time requirements. Firstly, the trade-offs between lightweight encryption primitives and their software and hardware collaborative design in terms of performance, resource overhead, and security strength are analyzed. Secondly, the resource efficiency of AI-based intrusion detection system (IDS) is evaluated at the edge. Finally, we propose a dynamic adaptive collaborative defense framework (DACDF), which integrates federated learning with dynamic weight distillation, blockchain authentication with lightweight verifiable delay function (Light-VDF) and cross-domain IDS with hierarchical attention feature fusion to deal with collaborative attacks in resource
Zhou, YouZhang, JiguiDing, KaniYang, Guozhi
In contemporary society, where Global Navigation Satellite Systems (GNSS) are utilised extensively, their inherent fragility gives rise to potential hazards with respect to the safety of ship navigation. In order to address this issue, the present study focuses on an ASM signal delay measurement system based on software defined radio peripherals. The system comprises two distinct components: a transmitting end and a receiving end. At the transmitting end, a signal generator, a first time-frequency synchronisation device, and a VHF transmitting antenna are employed to transmit ASM signals comprising dual Barker 13 code training sequences. At the receiving end, signals are received via software-defined radio equipment, a second time-frequency synchronisation device, a computing host, and a VHF receiving antenna. Utilising sliding correlation algorithms enables accurate time delay estimation. The present study leverages the high performance and low cost advantages of the universal
Li, HaoSun, XiaowenWang, TianqiZhou, ZeliangWang, Xiaoye
In order to meet the demand for the transformation of traditional manufacturing industries into intelligent manufacturing, a virtual monitoring system for the production workshops of nuclear - key products has been built. There are problems such as poor environment, long distance and remote collaborative office in this production workshop, and managers lack information tools to master the workshop status in real time. In order to minimize the harm of nuclear radiation to the human body, in view of the problems of low transparency, poor real - time performance and low data integration in traditional two - dimensional forms, configuration software and video monitoring, a remote monitoring system for virtual workshops driven by digital models has been developed. This system realizes the remote dynamic display of real - time information in the workshop based on data collection and three - dimensional modeling technologies. Virtual monitoring technology improves the management efficiency of
Wu, YimingChen, RuiLi, Na
In the power industry, high-power Diesel Generator (DG) sets often utilize high power V-engine cylinder configurations to enhance power output within a compact design, ensuring smoother operation and reduced vibration. In this V-engine configurations, the exhaust gas mass flow rate is significantly higher compared to inline engines of similar displacement, due to the greater number of cylinders operating in a compact space, which leads to a higher volume of exhaust gases expelled in a shorter duration. This necessitates the use of a dual Exhaust After Treatment System (EATS) to effectively manage NOx emissions. High-power gensets typically emit NOx levels around 9 g/kWh, presenting significant challenges for developers in adhering to stringent emission standards. To address these challenges and meet CPCB IV+ emission norms, we propose a dual urea dosing system integrated with a novel control strategy aimed at optimizing the treatment of exhaust gases. This paper introduces a dual
K, SabareeswaranK K, Uthira Ramya BalaS K, NejanthenA, RavikumarS, Mahendra BoopathiYS, Ananthkumar
Warranty claims function as primary source of characterizing field failures across industries, wherein appropriate classification of these claims is critical for further analysis. The classification of warranty claims is a highly laborious effort, involving significant man-hours of warranty analysts. This can be highly optimized and made efficient using direct interpretation of the claim data on 3D model using unity game engine. Additionally, the color perception technique using immersive technology (AR/VR) can help to identify the vital few & drive prioritization of the field failures leading to faster problem resolution. The capabilities of UI/UX & advanced visualization are integrated to develop novel methods to classify the warranty claims & interpret it on a 3D model using immersive technology which is novel and one of its kind in industry. Unique characteristics of this tool is it focuses on the warranty claim classification by claim cost & count of claims and presents the heat
Nankery, Viveksavadatti, SandeepShete, AtulApkare, SanketGanapathi, Poongundran
Modern automotive systems generate a wide range of audio-based signals, such as indicator chimes, turn signals, infotainment system audio, navigation prompts, and warning alerts, to facilitate communication between the vehicle and its occupants. Accurate Classification and transcription of this audio is important for refining driver aid systems, safety features, and infotainment automation. This paper introduces an AI/ML-powered technique for audio classification and transcription in automotive environments. The proposed solution employs a hybrid deep learning architecture that leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs), trained using labeled audio samples. Moreover, an Automatic Speech Recognition (ASR) model is integrated for transcribing spoken navigation prompts and commands from infotainment systems. The proposed system delivers reliable results in real-time audio classification and transcription, facilitating better automation and
Singh, ShwethaKamble, AmitMohanty, AnantaKalidas, Sateesh
In the agricultural industry, the logistics of transporting and storing bales, used as cattle feed, pose significant challenges for large scale farms. Traditional storage of bales in barns is labor-intensive, high in capital expenditure and requires multiple trips of transport vehicle on and off the field. Improper handling during this transition can lead to substantial losses in time, resources and loss of hay. This development aims to eliminate the last-mile transportation step, by enabling year-round storage of bales directly in the field. A patented wrapping material, along with strategic orientation of wrapped bales, enhances their resistance to weather conditions. Field experiments demonstrated that this innovative material not only protects the bales from adverse environmental factors but also effectively retains their nutrient and moisture content. A critical aspect of this solution is ensuring the correct orientation of the wrap seams, as the bales are continuously rotated
Kadam, Pankaj
This paper introduces an AI-powered mobile application designed to enhance vehicle warranty management through real-time diagnostics, predictive maintenance, and personalized support. The system supports multi-modal inputs (text, voice, image, video), integrates real-time On-Board Diagnostics (OBD) data, and accesses OEM warranty terms via secure APIs. It employs supervised, unsupervised, and reinforcement learning to deliver accurate fault detection, tailored recommendations, and automated claim decisions. Contextual analysis and continuous learning improve precision over time. The application also provides service cost estimates, part availability, and proactive maintenance alerts. This approach improves customer satisfaction, reduces warranty costs, and streamlines aftersales support. Utilizing advanced AI and machine learning algorithms, the application interprets customer queries through multiple input modes—text, voice, video, and image—and retrieves relevant information from the
Ramekar, Vedant MadhavChaudhari, Hemant
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