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This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). The course introduces the basic knowledge of vehicle noise and vibration, provides the analysis and control methods for noise and vibration sources, and transfer paths, and describes the occupants' responses and control. The course is specially designed for NVH engineer and related graduate participants. The course combines the NVH theory and engineering practices. After finishing the course, the students will deeply understand the mechanism of NVH and promote their capacity to solve engineering problems. 

This course examines ADAS and autonomous vehicle technologies that offer the potential to increase safety while attempting to optimize the cost of car ownership. LIDAR (light detection ranging) and Infrared camera sensing are seeing a rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve almost every six months. This course will provide the foundation to build on for these two technologies in automotive applications. It will include a demonstration model for LIDAR and Infrared camera.

This training class describes how to develop and safeguard safety critical embedded software in serial projects with Simulink in compliance with ISO 26262 (part 6). Beginning with a general overview of the ISO standard, we proceed by focusing on the ISO 26262 requirements that are specifically relevant to model based development. We address the impact the standard has had on model-based development with Simulink, as well as the requirements for model and software architecture in safety critical software. We also look at modeling guidelines and testing before wrapping up the class by assessing ISO 26262 readiness of controller functions.

Use of Model-Based Systems Engineering (MBSE) has been growing across industry, extending beyond defense and aerospace to include various commercial enterprises such as automotive and healthcare. Tool vendors are quick to point out benefits of this model-based approach and practices but are not always clear how MBSE benefits can be realized on a project. When deployed successfully, several key considerations should be addressed that maximize the value for a use-case. This four-hour class will discuss the nature and purpose of the MBSE approach and how key information is used for successful MBSE deployment as it relates to Systems Management.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Light detection and ranging (LIDAR) sensing, a sensing method that detects objects and maps their distances, is seeing rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve. This course will provide the foundation to build LIDAR technologies in automotive applications.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Among the challenges are those of sensing the environment in and around the vehicle. Infrared camera sensing is seeing a rapid growth and adoption in the industry. The applications and illumination architecture options continue to evolve. This course will provide the foundation on which to build near infrared camera technologies for automotive applications.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). As the complexity of products increases, traditional text-based systems engineering can no longer meet the needs. To solve the problem, Model-based Systems Engineering offers a unified communication platform among relevant staff by carrying out diagram-based unambiguous description, analysis and design for the demand, and structure and behavior of complex systems in the form of a model.

This course is offered in China only and presented in Mandarin Chinese. Sound quality is one of the most important desired attributes for the customers, and road noise is one of the most vital factors influencing sound quality. Road noise is the major interior noise source, especially for EVs, and it is a common customer complaint. In order to improve the customers’ satisfaction and market share, almost all the OEMs have spent lots of sources on the road noise attenuation.

Most complex systems are moving toward a “smart” solution, with automated methods for identifying and diagnosing problems. This course will explore how efficient system scan be designed in an effective manner to ensure that they meet performance requirements. Because predictive maintenance is important to the aerospace industry, this course will address systems engineering (SE), review prognostic health management (PHM), and explain how you can utilize them to obtain a better system. Additionally, it will address requirements management; model-based design; and verification and validation.

With the world aircraft fleets growing exponentially, the maintenance burden on airlines is also becoming overwhelming. One way to counter this is by making systems “smarter” so that they can self-diagnose themselves, help with troubleshooting, and estimate remaining useful life. Prognostics and Health Management (PHM) is the engineering discipline that forms the basis for developing such smart systems. In this course, the basic tenets of PHM will be taught with an emphasis on the practical application of PHM to aerospace systems.

The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.  

The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

Many technical projects, most vehicle and component testing, and all accident reconstructions, product failure analyses, and other forensic investigations, require photographic documentation. Roadway evidence disappears, tested or wrecked vehicles are repaired, disassembled, or scrapped, and components can be tested for failure. Photographs are frequently the only evidence that remains of a wreck, or the only records of subjects before or during tests. Making consistently good images during any inspection is a critical part of the evaluation process. 

The purpose of this standard is to provide a source of definitions of terms and acronyms commonly used in the air transport maintenance community for test and evaluation with an emphasis on terms applicable to BITE.

The purpose of this document is to establish guidelines that should be observed during initial design, production, and maintenance of aircraft components, and to present short-term and long-term strategies to minimize the costs and impacts associated with decreasing availability of components.

This document is based upon the SAE ARP5602 document, A Guideline for Aerospace Platform Fiber Optic Training and Awareness Education. ARINC Report 807: Fiber Optic Training Requirements is a subset of the SAE ARP5602 document designed to meet the requirements of the commercial air transport industry. Certification to the SAE ARP5602 document fulfills the requirements of ARINC Report 807. This document defines recommended general practices for training requirements of aerospace fiber optic systems. It is the intention of this document to outline proven training practices and general standards of workmanship for technicians engaged in aerospace fiber optic manufacturing, installation, maintenance, and repair for the air transport industry. It is also recommended that management and purchasing personnel receive fundamental training to familiarize themselves with the requirements of aerospace fiber optics.