Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenois

Parameter Optimization for Preparing Carbon Fiber/Epoxy Composites by Selective Laser Sintering

Carbon fiber (CF) reinforced thermosetting resin composites offer a wide range of high performance features including excellent strength, modulus and thermal resistance and light weight. Consequently, they are increasingly demanded by aerospace and automotive industries due to the tighter requirements of the transport vehicles for lightweight as well as higher payloads. Although thermoplastics and their composites have been widely used in additive manufacturing (AM), to date it is difficult to manufacture carbon fibers reinforced thermosetting composite parts via AM technologies. Therefore, this study developed a novel method based on selective laser sintering (SLS) to fabricate high-performance carbon fiber/epoxy resin composites. The response surface method was employed to study the processing parameters affecting the quality of final parts, and an optimized processing condition was obtained.Mechanical Engineerin

Amplified role of potential HONO sources in O3 formation in North China Plain during autumn haze aggravating processes

Co-occurrences of high concentrations of PM2.5 and ozone (O-3) have been frequently observed in haze-aggravating processes in the North China Plain (NCP) over the past few years. Higher O-3 concentrations on hazy days were hypothesized to be related to nitrous acid (HONO), but the key sources of HONO enhancing O-3 during haze-aggravating processes remain unclear. We added six potential HONO sources, i.e., four groundbased (traffic, soil, and indoor emissions, and the NO2 heterogeneous reaction on ground surface (Het(ground))) sources, and two aerosol-related (the NO2 heterogeneous reaction on aerosol surfaces (Het(aerosol)) and nitrate photolysis (Phot(nitrate))) sources into the WRF-Chem model and designed 23 simulation scenarios to explore the unclear key sources. The results indicate that ground-based HONO sources producing HONO enhancements showed a rapid decrease with height, while the NO C OH reaction and aerosol-related HONO sources decreased slowly with height. Photnitrate contributions to HONO concentrations were enhanced with aggravated pollution levels. The enhancement of HONO due to Phot(nitrate) on hazy days was about 10 times greater than on clean days and Phot(nitrate) dominated daytime HONO sources (similar to 30 %-70% when the ratio of the photolysis frequency of nitrate (J(nitrate)) to gas nitric acid (JHNO(3)) equals 30) at higher layers (>800 m). Compared with that on clean days, the Phot(nitrate) contribution to the enhanced daily maximum 8 h averaged (DMA8) O-3 was increased by over 1 magnitude during the haze-aggravating process. Phot(nitrate) contributed only similar to 5% of the surface HONO in the daytime with a J(nitrate) =JHNO(3) ratio of 30 but contributed similar to 30 %-50% of the enhanced O-3 near the surface in NCP on hazy days. Surface O-3 was dominated by volatile organic compound-sensitive chemistry, while O-3 at higher altitudes ( >800 m) was dominated by NOx-sensitive chemistry. Phot(nitrate) had a limited impact on nitrate concentrations (Peer reviewe