Browse Topic: Cloud computing
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
With the exponential growth of global data traffic driven by AI, big-data analytics, and cloud computing, today’s single-mode fiber (SMF) networks are edging toward their Shannon-capacity limits. Space-division multiplexing (SDM) in multimode fiber (MMF) has emerged as a leading candidate for the next-generation bandwidth breakthrough because a single MMF can carry many orthogonal transverse modes in parallel. However, random mode coupling during propagation mixes these modes into complex speckle patterns, severely complicating signal recovery. Although conventional digital signal processing (DSP) algorithms are theoretically capable of mode demultiplexing, their computational complexity scales rapidly with the number of modes, rendering them impractical for high-capacity MMF networks.
This paper explains the method of precooling of electric vehicle from grid connected charger reduce load on HVAC and improve the range. HVAC systems are integral part of a commercial EV bus. With the rise of ambient temperatures during various seasons, the load on HVAC System is increasing. Once an Electric vehicle is released from a depot for service, with an initial soaked up ambient vehicle, the HVAC system demands peak power for cooling the interiors which consumes a lot of battery power thus affecting the range. That cause the additional energy consumption required for precooling, which cannot be estimated as it is highly dependent on ambient temperature and range of the vehicle is also dependent on HVAC consumption during summer and peak loads. This paper is proposing a method that uses a special precooling mode which is activated depending on the selection of the vehicle route based on backend application running on cloud. The Application in the cloud checks if the vehicle is charging and also collects all the information about the SOC. This method will help in reduction in power demand from HVAC unit also helps in increasing the range of the vehicle.
A Modular Open Systems Approach (MOSA) for command and control (C2) of autonomous vehicles equipped with sensor and defeat mechanisms enhances force protection against unmanned aerial systems (UAS), swarm, and ground-based robotic threats with current technology while providing an adaptable framework able to accommodate technological advances. This approach emphasizes modularity, which allows for independent upgrades and maintenance; interoperability, which ensures seamless integration with other systems; and scalability, which enables the system to grow and adapt to increasing threats and new technologies – all of which are essential for managing complex, dynamic, and evolving operational threats from UAS, swarm, and ground-based robots. The proposed systems approach is designed around component-based modules with standardized interfaces, ensuring ease of integration, maintenance, and upgrades. The integration of diverse sensors through plug-and-play capabilities and multi-sensor fusion enhances the detection, tracking, and identification of aerial and ground threats. Autonomous decision-making is empowered by artificial intelligence (AI) and machine learning (ML) algorithms and supported by edge and cloud computing for efficient data processing while retaining human-in-the-loop confirmation prior to threat defeat activation. Lifecycle management strategies with a continuous integration/continuous development (CI/CD) pipeline enable rapid updates and a scalable architecture to accommodate evolving threats and new technologies.
The Defense Advanced Research Projects Agency (DARPA) pioneered satellites, the internet, drones, and human-computer interfaces. Now that work is enabling the next round of revolutionary technologies, including artificial intelligence (AI), edge and cloud computing, and the Internet of Military Things (IoMT) for a wide variety of Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications. Laptops and tablets are beneficiaries of yesterday's DARPA breakthroughs as well as enablers of today's and tomorrow's innovations. For example, ruggedized mobile PCs provide powerful new tools for asymmetric warfare by giving soldiers anytime, anywhere access to biometric information such as fingerprints and facial recognition. That information enables them to quickly determine whether a person in street clothes at a checkpoint is a civilian or combatant. This application also highlights the fundamental role of edge computing and the cloud for securely storing and sharing that biometric information.
The Defense Advanced Research Projects Agency (DARPA) pioneered satellites, the internet, drones, and human-computer interfaces. Now that work is enabling the next round of revolutionary technologies, including artificial intelligence (AI), edge and cloud computing, and the Internet of Military Things (IoMT) for a wide variety of Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications.
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems must be monitored as service degradation is more likely than a complete failure. Established monitoring and error analysis approaches for cloud environments cannot easily be transferred to vehicles. Monitored values must be aggregated and correlated to error events before cloud transmission, or suspects must be created in the vehicle for thorough analysis, reducing the data exchanged with the backend. The SOVD API provides a good foundation to diagnose service-oriented architectures and HPCs. While SOVD offers a wide range of diagnostic and monitoring features, it currently lacks solutions for diagnosing certain aspects and especially monitoring of a service-oriented architecture. This paper addresses these gaps, showcasing approaches and techniques to enhance monitoring and diagnostics.
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce processing costs and delays. When computer functions are shifted to the edge, battery management systems become more responsive and cost-effective. This is accomplished through improved time and cost efficiencies. Furthermore, this will ensure a rapid and efficient client experience, perhaps giving OEMs a competitive advantage in the digital technology arena.
Mode identification, particularly Modal Map Generation, is pivotal within the NVH (Noise, Vibration, and Harshness) domain for managing the performance of complex systems like TBIW/Powertrain. This study addresses the critical task of accurately identifying Global / Local behavior of a particular system as single entity (Complete TBIW, Power train) or all the systems attached to main structure (Sub Systems i.e Seat , Fuel Tank , Pump etc), which is crucial for effective NVH post-processing. Introducing a novel tool/methodology developed by the Applus IDIADA team, this paper presents an efficient approach to Global & Local mode identification across subsystems, TBIW, and Powertrain levels. Leveraging ".op2" file content, mainly Strain Energy Density[1] and Displacement [2], the tool integrates Machine Learning Techniques [3] to produce mode predictions along with detailed visual outputs such as graphs , pie chart , modal charts etc. Implemented as a Python-based solution compatible with major Pre and Post processors, it operates seamlessly with cloud technology [4], thereby reducing prediction time significantly. Beyond predicting mode numbers, the tool also provides actionable insights into subsystem contributions, aiding in enhancing mode shape and continuity studies [5]. Validated with robust data analysis, it ensures reliability in streamlined methodology for Mode Identification for NVH applications[6].
You've got regulations, cost and personal preferences all getting in the way of the next generation of automated vehicles. Oh, and those pesky legal issues about who's at fault should something happen. Under all these big issues lie the many small sensors that today's AVs and ADAS packages require. This big/small world is one topic we're investigating in this issue. I won't pretend I know exactly which combination of cameras and radar and lidar sensors works best for a given AV, or whether thermal cameras and new point cloud technologies should be part of the mix. But the world is clearly ready to spend a lot of money figuring these problems out.
Usage of cloud technology is essential for aftersales tester providers. It eases the rollout of new tester content - for example, diagnostic data of new vehicle types, updated repair manuals or ECU software for flash programming. Cloud technology also implements security services such as user authentication information. Figure 1 shows a typical setup as it is implemented for the service of vehicles such as trucks and buses. The vehicle is parked (vehicle speed = zero) in the service workshop, and its E/E system is connected to the vehicle communication interface (VCI) via CAN or Ethernet. On the tester (TST) side, the TST-to-VCI connection is either USB or WiFi.
The Internet of Military Things (IoMT), sometimes referred to as the Internet of Battlefield Things (IoBT), is gaining momentum for applications that improve defensive and battlefield capabilities. Like its civilian counterpart, the IoMT are networks of sensors, wearables, and imaging devices using edge and cloud computing to improve military operations and safety. However, battery failure in an IoMT device can have serious consequences in applications such as unmanned aerial drones that are used to patrol border areas or secure military bases. Battery life requirements are also high for the sensors and surveillance cameras that can be used to send real-time intelligence back to the command center for strategic decisions. Likewise, predictable battery life for IoMT devices used for vehicle management, battlefield supply chains, and weapon control are critical for efficient operations. Therefore, optimizing the device design and software to reduce power consumption and increase battery life is crucial. One method to do this is to use battery emulation and profiling software, which can imitate the performance and features of different kinds of batteries under various situations. With this software, device designers can calculate battery life accurately. Additionally, emulation software can measure current drain to adjust device designs that can prolong battery life.
The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. Due to the short lifespan of software, it becomes necessary to keep these cloud environments and their applications up to date with security updates and new features. However, as new behavior is introduced to the system, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior. Therefore, a concept is developed mirroring the existing cloud system into a simulation. To collect the necessary data, an observability platform is presented, capturing telemetry and architecture information. Subsequently, a simulation environment is designed that converts the architecture into a simulation model and simulates its dynamic workload by utilizing captured communication data. The proposed concept is evaluated in a real-world application scenario for electric vehicle charging: Vehicles can apply for an unoccupied charging station at a cloud service backend, the latter which manages all incoming requests and performs the assignment. Benchmarks are conducted by comparing the collected telemetry data with the simulated results under different loads and injected faults. The results show that regular cloud behavior is mirrored well by the simulation and that misbehavior due to fault injection is well visible, indicating that simulations are a promising data source for anomaly detection in connected vehicle cloud environments during operation.
The start-up's ‘real-time engineering’ service means you can simulate on someone else's GPUs. The cloud is many things to many people, but it's really just a new way to talk about using other people's computers instead of your own. When it comes to expensive GPUs that run intensive simulations, using someone else's computers-theirs-is exactly what Luminary Cloud wants the automotive industry to do. Luminary Cloud came out of stealth mode in early March, just in time to make a splash at NVIDIA's GTC 2024 event. With the support of $115 million in funding from Sutter Hill Ventures, Luminary Cloud now wants to offer the “world's first modern computer-aided engineering (CAE) software as a service (SaaS).”
A pilot project between ABB Robotics and US non-profit organization Junglekeepers demonstrated the role cloud technology can play in making reforestation faster, more efficient, and scalable. The project was implemented from May-June 2023 using ABB’s cobot operated remotely with cloud technology.
Automated driving, electrification, cloud computing and the push toward software-defined vehicles are forcing automotive and commercial-vehicle developers to revamp design strategies. Tools suppliers are moving to help engineers develop and verify solutions that address the complete vehicle environment, a task that requires a growing number of design tools. During the recent dSPACE World Conference in Munich, Germany, several vehicle manufacturers described their strategies for coping with these trends. dSPACE, which supplies hardware/software-in-the-loop (HIL/SIL) tools, announced plans to see if tool makers can find a way to help developers by making it easier to integrate data created using different development software.
In the ever-evolving landscape of clinical trials, the advancement of edge computing technology in wearable devices is revolutionizing the way research is conducted. Today, the exponential growth of data poses significant challenges for traditional cloud computing models, which struggle to keep up with demand. That’s where edge computing comes in. With its ability to process data closer to its source, edge computing offers a solution to the limitations of conventional cloud infrastructure. By leveraging edge computing, researchers have an opportunity to enhance remote patient monitoring and reshape the traditional clinical trial paradigm.
Companies in many industries, including technology, construction, and healthcare are completely revamping the way in which their manufacturing arms are designing, building, producing, and servicing the goods they need for projects and customers.
The fast growth of electric vehicles has resulted in the widespread application of lithium-ion batteries. Recognized as a critical problem, the accurate estimation of the battery state has drawn much attention. Meanwhile, the continuous progress of the Internet of vehicles technology promotes the algorithm on cloud platform. However, the state of charge and state of health estimation based on on-board battery management system present deficiency such as data loss, noise interference and inconsistent sampling interval through the transmission. Thus, this article developed a multi-scale co-estimation method on the SOC and SOH with consideration of the dataset quality. Firstly, a Thevenin model for SOC estimation is constructed and parameters are identified by least square method. It is noteworthy that the frequency of SOC and SOH updates different time scales. To achieve the co-estimation on the both state and health, the extended Kalman filter algorithm is used twice. The dual extended Kalman filter is applied to operate at different time scales depending on the frequency of state and health updates. Finally, the impact of data loss, repetition and time scale is discussed where the dataset is incomplete through transfer protocol. Simulation confirms that this algorithm can still achieve maximum deviation of 5% for SOC estimation under sampling frequency, and the error can be limited within 6% for capacity estimation. The article provides an SOC and SOH estimation technique for the cloud platform.
Cloud computing technologies are embodied with automotive sector copiously. It aids in using data and computing services to manage information, communication, and computing, through Internet-based apps and online digital services. A cloud computing-based framework is suitable for developing and deploying simulation models to study, analyse and optimise the vehicle performance. The framework proves functional in collecting vehicle data, processing and then using them for datadriven or model-based development to deliver a complete software solution. Server-less cloud computing technologies with storage and function triggers form the architecture. The paper outlines a data-driven model of a Three-Way Catalyst (TWC) to test the cloud framework as an end-to-end solution. The model estimates a metric to quantify the oxygen storage capacity of the TWC over the air. This metric is an online adaptive gain, estimated through system diagnosis using the Recursive Least Squares method. This is followed by a Decision Tree Classification algorithm to classify these metrics according to their useful life. Thus, realising TWC health diagnostics.
Two-wheelers especially scooters constitute a majority market share in Asian countries. A hybrid drive-train integration of electric motor/motors with a conventional IC engine is a suitable solution to achieve reduction in CO2 emissions and as an alternative to IC Engine only vehicles. A model based supervisory controller is proposed, considering the behavior of the electrical drive, IC engine as well as the transmission, which determines the modes of operation. The controller determines the commanded torque split between the engine and electric motor across all modes of operations. With the information about the driving cycle, an optimal controller based on dynamic programming that minimizes fuel and equivalent electrical energy consumption with charge sustaining feature is proposed. This supervisory controller was simulated for hybrid configuration running on WMTC driving cycle to minimize equivalent energy consumption. The performance of the proposed controller with hybrid powertrain is compared against a conventional powertrain. Cloud Computing is gaining force as it acts as an online fast computing platform enabling scalability, online resource management, seamless integration with hardware along with the most important benefit of reduced computation requirements at the edge. This supervisory controller was hence also implemented on a Cloud Service platform.
The purpose of the OBIGGS is to reduce the amount of oxygen in the fuel tank to a 'safe' level to significantly reduce the possibility of ignition of fuel vapors. There are circumstances where equipment of OBIGGS like ASMs, Ozone Converter Catalysts, etc. gets degraded earlier than the provided MTBF. This paper studies the present conventional systems limitations, like due to memory constraints only the faults and limited shop data are being recorded, hence there is no provision to store/report the stream of data margins with which we can pass/fail the performance tests. This paper also explains how a new design of the Connected concept achieves access to real-time data from the system and how the data is pushed to the cloud network. A connected solution for the OBIGGS is the technology to access real-time data (Systems LRUs Performance data and Custom data Parameters) from the Systems controller data bus, this data is further applied to AI/ML methods for predictive/prognostics features to compare why the performance of the ASMs in some systems may degrade quicker than others and to inform of when equipment of OBIGGS may need to be inspected/tested/replaced. VOCs related to ASMs degradation, FQIS field issues, sensors, valves, and other equipment's data parameters can be monitored over time that would be of value and an interest in more details for the Suppliers, Manufacturers, and Customers side.
Aircraft Manufacturing procedures are very critical which always require skillful engineers who must adhere to various process and procedure during daily work. The challenge is not only to identify right tools and manuals but also to keep track of operator usage data and behavior. With Augmented reality (AR), intelligent tools and analytics, we can provide a new lease of life to first-line assembly engineers. The objective is how AR can help us to reduce rework or scrap with the concept of Industry 4.0 [1] were integrating with cutting edge technologies like machine learning and the internet of things (IoT) to meet the fundamental requirements during manufacturing. Smart Manufacturing consists of four major components Cyber-physical systems, IoT, cloud computing and cognitive computing. Our objective is to integrate Augmented reality with IoT sensor to achieve Cyber-physical systems connectivity and fill the gap between the smallest physical assets connected with digital infrastructure. It can help us to increase manufacturing throughput, make tools intelligent, accelerate training, inventory management and most important operator safety. Our AR applications are developed on PTC [9] software platform which can be tablet-based, or headset based, and content can be accessible globally within organisation. Overall, it is not about a technology that lies in the visualization process only, it is about how data are visualized, captured and utilized to make the working environment safe and productive, which makes this technology powerful in manufacturing ecosystem.
Whenever you buy something online, your customer data is automatically updated and stored on thousands of virtual machines in the cloud. But up to now, there has been no way to guarantee that a software system is secure from bugs, hackers, and vulnerabilities. Now, researchers have developed SeKVM, the first system that guarantees — through a mathematical proof — the security of virtual machines in the cloud.
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