Search Results

Though modal analysis is a common tool to evaluate the dynamic properties of a structure, there are still many individual decisions to be made during the process which are often based on experience and make it difficult for occasional users to gain reliable and correct results. One of those experience-based choices is the correct number and placement of reference points. This decision is especially important, because it must be made right in the beginning of the process and a wrong choice is only noticeable in the very end of the process. Picking the wrong reference points could result in incomplete modal analysis outcomes, as it might make certain modes undetectable, compounded by the user's lack of awareness about these missing modes. In the paper an innovative approach will be presented to choose the minimal number of mandatory reference points and their placement.

The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components while employing ultrasonic guided wave based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using laser-Doppler scan of surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators on-board structurally integrated. Using direct wave field data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from design and qualification standpoint; however, those may cause significant background signal artifacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture.

The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts.

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. 

Verification and validation (V&V) of autonomous vehicles (AVs) is a challenging task. AVs must be thoroughly tested, to ensure their safe functionality in complex traffic situations including rare but safety-relevant events. Furthermore, AVs must mitigate risks and hazards that result from functional insufficiencies, as described in the Safety of the Intended Functionality (SOTIF) standard. SOTIF analysis includes iterative identification of driving scenarios that are not only unsafe, but also unknown. However, identifying SOTIF’s unknown-unsafe scenarios is an open challenge. In this paper we proposed a systematic optimization-based approach for identification of unknown-unsafe scenarios. The proposed approach consists of three main steps including data collection, feature extraction and optimization towards unknown unsafe scenarios.

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.

Water content estimation is a key problem for studying the PEM fuel cell. When several hundred fuel cells are connected in serial and their active surface area is enlarged for sufficient power, the difference between cells becomes significant with respect to voltage and water content. The voltage of each cell is measurable by the cell voltage monitor (CVM) while it is difficult to estimate water content of the individual. Resistance of the polymer electrolyte membrane is monotonically related to its water content, so that the new online high frequency resistance (HFR) measurement technique is investigated to identify the uniformity of water content between cells and analyze its sensitivity to operating conditions in this paper. Firstly, the accuracy of the proposed technique is experimentally validated to be comparable to that of a commercialized electrochemical impedance spectroscopy (EIS) measurement equipment.

As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy.

In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem.

This paper delves into the investigation of flatness-based active damping control for hybrid vehicle transmissions. The main objective is to improve the current in-production controller performances without the need for additional sensors or observers. The primary goals include improving torque setpoint tracking, enhancing robustness margins, and ensuring zero steady-state torque correction. The investigation proceeds in several steps: Initially, both the general differential flatness property and the identification of flat outputs in linear dynamical systems are revisited. Subsequently, the bond graph formalism is employed to deduce straightforwardly the dynamical equations of the system. Next, a new flat output of the vehicle transmission is identified and utilized to formulate the trajectory tracking controller to align with the required control objectives and to fulfill the system constraints.

This specification covers a blend of tungsten carbide-cobalt aggregate, a nickel alloy, and a nickel aluminum aggregate in the form of powder.

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion resistant steel of the type identified under the Unified Numbering System as UNS S30200.

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S32100.

This specification covers an aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock.

This specification covers tubular-shaped pins, fabricated from carbon steel, having a full-length longitudinal slot to permit flexure when inserted into a hole.

This specification covers a corrosion- and heat-resistant steel in the form of bars, wire, forgings, mechanical tubing, flash-welded rings, and stock for forging or flash-welded rings.