Qiuye Yu

This study develops an energy management strategy (EMS) for hybrid combine harvesters to address fluctuating power demands in agricultural operations. By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (header, conveyor, drum) for accurate energy estimation. Real-time feed rate adjustments are achieved through dynamic responses of critical components, optimizing cycle duration and power allocation. A genetic algorithm synchronizes energy distribution and cycle timing to minimize fuel consumption. Validated via AMESim/Simulink co-simulation with dual engine models, the strategy reduces fuel use by 21.1% compared to conventional systems. Key innovations include quasi-periodic load segmentation, component-response-based feed rate prediction, and GA-driven multi-objective optimization. The approach enhances adaptability to variable harvesting conditions, offering a scalable framework for energy-efficient electrification in agriculture. Results demonstrate significant potential for hybrid systems in reducing operational costs and emissions while maintaining productivity under dynamic workloads.

This paper provides a multi-agent coordinated control system to improve the real-time performance of intelligent vehicle active collision avoidance. At first, the functions and characteristics of longitudinal and lateral collision avoidance agents are analyzed, which are the main components of the multi-agent. Then, a coordinated solution mechanism of an intelligent vehicle collision avoidance system is established based on hierarchical control and blackboard model methods to provide a reasonable way to avoid collision in complex situations. The multi-agent coordinated control system can handle the conflict between the decisions of different agents according to the rules. Comparing with existing control strategies, the proposed system can realize multi decisions and planning at the same time; thus, it will reduce the operation time lag during active collision avoidance. Additionally, fuzzy sliding mode control theory is introduced to guarantee accurate path tracking in lateral collision avoidance. Finally, co-simulation of Carsim and Simulink are taken, and the results show that the real-time behavior of intelligent vehicle collision avoidance can be improved by 25% through the system proposed.

Sonic Infrared (IR) NDE, is a relatively new NDE technology; it has been demonstrated as a reliable and sensitive method to detect defects. SIR uses ultrasonic excitation with IR imaging to detect defects and flaws in the structures being inspected. An IR camera captures infrared radiation from the target for a period of time covering the ultrasound pulse. This period of time may be much longer than the pulse depending on the defect depth and the thermal properties of the materials. With the increasing deployment of composites in modern aerospace and automobile structures, fast, wide-area and reliable NDE methods are necessary. Impact damage is one of the major concerns in modern composites. Damage can occur at a certain depth without any visual indication on the surface. Defect depth information can influence maintenance decisions. Depth profiling relies on the time delays in the captured image sequence. We’ll present our work on the defect depth profiling by using the temporal information of IR images. An analytical model is introduced to describe heat diffusion from subsurface defects in composite materials. Depth profiling using peak time is introduced as well.

Sonic Infrared Imaging uses ultrasound excitation and infrared imaging to detect defects in different materials, including metals, metal alloys, and composites. In this NDE technology, the ultrasound excitation applied is typically a short pulse, usually a fraction of a second. The ultrasound causes the opposing surfaces of a crack or a defect to rub each other and result in temperature change with noticeable infrared radiation increase. This thermal signal can be captured by IR camera and used to locate the defect within the target. Probability of detection of defects can be significantly improved when chaotic sound is introduced to the materials. This nonlinearity between the ultrasound transducer and the target materials is an important phenomenon, and the understanding is critical to improve the repeatability and reliability of this technology. In this paper, we will present our study on this topic with emphasis of characterizing vibration in the crack vicinity.

Sonic Infrared imaging (SIR) technology is a relatively new NDE technique that has received significant acceptance in the NDE community. SIR NDE is a super-fast, wide range NDE method. The technology uses short pulses of ultrasonic excitation together with infrared imaging to detect defects in the structures under inspection. Defects become visible to the IR camera when the temperature in the crack vicinity increases due to various heating mechanisms in the specimen. Defect detection is highly affected by noise levels as well as mode patterns in the image. Mode patterns result from the superposition of sonic waves interfering within the specimen during the application of sound pulse. Mode patterns can be a serious concern, especially in composite structures. Mode patterns can either mimic real defects in the specimen, or alternatively, hide defects if they overlap. In last year’s QNDE, we have presented algorithms to improve defects detectability in severe noise. In this paper, we will present our development of algorithms on defect extraction targeting specifically to mode patterns in SIR images.

Sonic IR Imaging combines pulsed ultrasound excitation and infrared imaging to detect defects in materials. The sound pulse causes rubbing due to non---unison motion between faces of defects, and infrared sensors image the temperature map over the target to identify defects. It works in various materials, including metal/metal alloy, ceramics, and composite materials. Its biggest advantage is that it’s a fast, wide area NDE technique. It takes only a fraction of a second or a few seconds, depending on the thermal properties of the target, for one test over a few square feet. However, due to the nonlinearity in the coupling between the ultrasound transducer and the target, the repeatability has been an issue, which affects its application. In this paper, we present our study on this issue in Sonic IR.