An Improved Optimal Slip Ratio Prediction considering Tyre Inflation Pressure Changes

The prediction of optimal slip ratio is crucial to vehicle control systems. Many studies have verified there is a definitive impact of tyre pressure change on the optimal slip ratio. However, the existing method of optimal slip ratio prediction has not taken into account the influence of tyre pressure changes. By introducing a second-order factor, an improved optimal slip ratio prediction considering tyre inflation pressure is proposed in this paper. In order to verify and evaluate the performance of the improved prediction, a cosimulation platform is developed by using MATLAB/Simulink and CarSim software packages, achieving a comprehensive simulation study of vehicle braking performance cooperated with an ABS controller. The simulation results show that the braking distances and braking time under different tyre pressures and initial braking speeds are effectively shortened with the improved prediction of optimal slip ratio. When the tyre pressure is slightly lower than the nominal pressure, the difference of braking performances between original optimal slip ratio and improved optimal slip ratio is the most obvious

Characterising the friction and wear between the piston ring and cylinder liner based on acoustic emission analysis

In this paper, an experimental investigation was carried out to evaluate the friction and wear between the cylinder liner and piston ring using acoustic emission (AE) technology. Based on a typical compression ignition (CI) diesel engine, four types of alternative fuels (Fischer-Tropsch fuel, methanol-diesel, emulsified diesel and standard diesel) were tested under dif-ferent operating conditions. AE signals collected from the cylinder block of the testing en-gine. In the meantime, the AE signals in one engine cycle are further segregated into small segments to eliminate the effects of valve events on friction events of cylinder liner. In this way, the resulted AE signals are consistent with the prediction of hydrodynamic lubrication processes. Test results show that there are clear evidences of high AE deviations between dif-ferent fuels. In particular, the methanol-diesel blended fuel produces higher AE energy, which indicates there are more wear between the piston ring and cylinder liner than using standard diesel. On the other hand, the other two alternative fuels have been found little dif-ferences in AE signal from the normal diesel. This paper has shown that AE analysis is an ef-fective technique for on-line assessment of engine friction and wear, which provides a novel approach to support the development of new engine fuels and new lubricants

To Copy Verbatim, Paraphrase or Summarize:Listeners’ Methods of Discourse Representation While Recalling Academic Lectures

It is unanimously agreed that comprehension of academic lectures is cognitively demanding; however, few studies have focused on a listener’s real-time discourse representation of a lecture. Based on the qualitative analysis of the verbal protocols, the present study investigated sixteen Chinese university students’ verbal recall of an academic mini-lecture to explore how they made sense of the lecture and represented its discourse when they recalled it episode by episode, and to what extent they differed in discourse representation. The results show that listeners’ discourse representation involved a range of cognitive processes such as paraphrasing, summarizing, and verbatim copying. Paraphrasing and summarizing were the main methods of discourse representation used by the participants when they verbally recalled the lecture. Those who correctly paraphrased more idea units recalled more content of the lecture. They were able to select and retain more idea units in their short-term memory, build more associations between the selected idea units, integrate them with the existing discourse structures and ensure contextual coherence in the construction of the local discourse structures. The findings of the study contribute to a better understanding of how listeners comprehend academic lectures and confirm that improving students’ paraphrasing skills and hierarchical discourse construction in recall are conducive to better comprehension of academic lectures.<br/

Influence of long-waved road roughness on fatigue life of dump truck frame

Asymmetrical long-waved road unevenness can accelerate the fatigue failure of the dump truck frame. Due to the difficulty of the tire damping modelling and the complex pitch motion of balanced suspension, it is hard to reveal the relationship between the parameters of long-waved road and the fatigue life of dump truck frame. Firstly, on the premise of not introducing truncation error, by applying matrix operations, an 11 DOF vibration model of dump truck considering tire damping is established to simulate the dynamic responding loads of frame bearing points. Secondly, the proposed model is validated by comparing the predicted and measured vehicle response and subsequently used to predict the dynamic vehicle loads. Finally, using the simulated responding load-time histories, the paper calculates and analyzes the fatigue life of dump truck under different road profile excitations. The results show that the increase of road amplitude can lead to fatigue weak area expanding to the back part of frame, and logarithmically shorten the fatigue life of frame

The next-to-next-to-leading order soft function for top quark pair production

We present the first calculation of the next-to-next-to-leading order threshold soft function for top quark pair production at hadron colliders, with full velocity dependence of the massive top quarks. Our results are fully analytic, and can be entirely written in terms of generalized polylogarithms. The scale-dependence of our result coincides with the well-known two-loop anomalous dimension matrix including the three-parton correlations, which at the two-loop order only appear when more than one massive partons are involved in the scattering process. In the boosted limit, our result exhibits the expected factorization property of mass logarithms, which leads to a consistent extraction of the soft fragmentation function. The next-to-next-to-leading order soft function obtained in this paper is an important ingredient for threshold resummation at the next-to-next-to-next-to-leading logarithmic accuracy.Comment: 34 pages, 9 figures; v2: added references, matches the published versio

Progress in Plasma-Assisted Catalysis for Carbon Dioxide Reduction

Production of chemicals and fuels based on CO2 conversion is attracting a special attention nowadays, especially regarding the fast depletion of fossil resources and increase of CO2 emissions into the Earth’s atmosphere. Recently, plasma technology has gained increasing interest as a non-equilibrium medium suitable for CO2 conversion, which provides a promising alternative to the conventional pathway for greenhouse gas conversion. The combination of plasma and catalysis is of great interest for turning plasma chemistry in applications related to pollution and energy issues. In this chapter a short review of the current progress in plasma-assisted catalytic processes for CO2 reduction is given. The most widely used discharges for CO2 conversion are presented and briefly discussed, illustrating how to achieve a better energy and conversion efficiency. The chapter includes the recent status and advances of the most promising candidates (plasma catalysis) to obtain efficient CO2 conversion, along with the future outlook of this plasma-assisted catalytic process for further improvement

Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series

Multi-modal biomedical time series (MBTS) data offers a holistic view of the physiological state, holding significant importance in various bio-medical applications. Owing to inherent noise and distribution gaps across different modalities, MBTS can be complex to model. Various deep learning models have been developed to learn representations of MBTS but still fall short in robustness due to the ignorance of modal-to-modal variations. This paper presents a multi-scale and multi-modal biomedical time series representation learning (MBSL) network with contrastive learning to migrate these variations. Firstly, MBTS is grouped based on inter-modal distances, then each group with minimum intra-modal variations can be effectively modeled by individual encoders. Besides, to enhance the multi-scale feature extraction (encoder), various patch lengths and mask ratios are designed to generate tokens with semantic information at different scales and diverse contextual perspectives respectively. Finally, cross-modal contrastive learning is proposed to maximize consistency among inter-modal groups, maintaining useful information and eliminating noises. Experiments against four bio-medical applications show that MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in human activity recognition, and by 1.14% F1-score in obstructive sleep apnea-hypopnea syndrome.Comment: 4 pages, 3 figures, submitted to ICASSP 202