Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms

Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms

Effect of Lubricant Additives on the Oxidation Characteristics of Diesel Engine Particulate Matter

Three common lubricant additives, including an antioxidant, detergent, and an antifoamer, were added to diesel fuel to perform a diesel engine bench test. Particulate matter samples underwent thermogravimetric analysis to investigate the effect of lubricant additives on the particulate matter oxidation process, characteristic temperature, and activation energy. The results showed the following. Different lubricant additives result in different variation trends in the thermogravimetric curve of a particulate matter sample by varying the rotating speed and torque. When the rotating speed was stable, as the torque increased, the ignition temperature of the particulate matter of Fuel C declined rapidly during the initial stage and then increased rapidly. When the torque was stable, as the rotating speed increased, the ignition temperature of the particulate matter of Fuel C increased initially and then declined. The particulate matter of Fuel C had the lowest level of activation energy at approximately 57.89 J·mol−1. The particulate matter of Fuel A had the highest level of activation energy at approximately 74.10 J·mol−1. When the fuel has a higher cetane number, the combustion chemical reaction rate is faster and results in a more complete reaction. The active substance contact surface increases, which facilitates particulate matter oxidation

Rain Rendering and Construction of <i>Rain Vehicle Color</i>-24 Dataset

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at [email protected]

Particle-attached microorganism oxidation of ammonia in a hypereutrophic urban river.

To elucidate the importance and mechanisms of particle-attached microorganisms on ammonia oxidation, we conducted a controlled simulation experiment with samples collected from the Shunao River, an ammonia-rich hypereutrophic urban river in eastern China. The effects of particle concentration, ammonia concentration, organic carbon source and concentration, dissolved oxygen concentration, and pH were investigated on ammonia transformation rate (ammonia removal rate and NO2 -  + NO3 - accumulation rate) and abundance of particle-attached ammonia-oxidizing bacteria (AOB) and archaea (AOA). All these factors significantly influenced ammonia transformation rates. Our results provided direct evidence that microorganisms attached on riverine suspended particles were associated with ammonia oxidation. Sequencing revealed that the AOA genus Nitrososphaera, and the AOB genus Nitrosomonas were the most dominant in particle-attached ammonia-oxidizing microbial communities. Further analysis showed that AOB communities had higher species richness and diversity compared with AOA communities. Additionally, AOB amoA genes were ~10-100 times more abundant than AOA amoA genes, and AOB abundance was more strongly correlated with ammonia transformation rates than AOA abundance in most experiments, indicating that particle-attached AOB were more important than AOA in the hypereutrophic urban river. This study adds to our knowledge of particle-attached microorganism oxidation of ammonia

Grid impedance detection based on complex coefficient filter and full-order capacitor current observer for three-phase grid-connected inverters

This article proposes a new grid impedance detection method incorporating the complex coefficient filter (CCF) with full-order capacitor current observer for a T-type three-level grid-connected inverter controlled by the inverter output current feedback. Compared with conventional CCF impedance detection algorithms, the proposed method reduces the number of current sensors and detects the grid impedance accurately. First, based on the sampled inverter output current and grid-connected voltage signals, the grid-connected current is estimated. Then, the CCF method is used to extract harmonics from the grid-connected current and voltage signals to calculate the grid impedance. Finally, the correctness of the full-order capacitor current observer is verified by simulation and the feasibility and effectiveness of the proposed algorithm are verified experimentally based on a laboratory prototype. © 1986-2012 IEEE

Polythionine and gold nanostar-based impedimetric aptasensor for label-free detection of α-synuclein oligomers

International audienceThe neurotoxicity of alpha-synuclein oligomers (alpha-syno) could cause Parkinson's disease (PD) which is an irreversible neurodegenerative disease. In order to diagnose and treat PD patients at an early stage and reduce the damage of sampling to humans, a non-invasive measure is used to analyze ultra-trace levels of alpha-syno. In this work, a new impedimetric aptasensor based polythionine (pTH)/Au nanostars (AuNSs) platform was designed for label-free detection of alpha-syno. The pTH and AuNSs demonstrated excellent electrical conductivity, fast electron transfer capability, and the synergistic amplification effect, which could enhance the sensitivity and improve the limit of detection (LOD). The ssDNA aptamers recognized and bound to alpha-syno via specific bases. Therefore, the ultra-sensitive alpha-syno aptasensor based on pTH and AuNSs presented a LOD of 0.07 aM and a dynamic range from 0.10 aM to 10.00 fM. The aptasensor also displayed good response range, high sensitivity, and low LOD in diluted human plasma samples to provide new methods for early diagnosis of PD

A novel continuous control set model predictive control for lc-filtered three-phase four-wire three-level voltage-source inverter

In this article, a novel continuous control set model predictive control (CCS-MPC) is proposed for an LC-filtered three-phase three-level four-wire voltage-source inverter (3P-4W-3L-VSI). The proposed MPC algorithm provides independent control of each phase of the 3P-4W-3L-VSI under various unbalanced load conditions, which shows superior performance under unbalanced loads. Furthermore, different from conventional MPC methods, the CCS-MPC achieves fixed switching frequency, hence simplifying the design of LC filters. In addition, dc offsets are introduced to the proposed modulation to effectively balance the neutral-point voltage of the 3P-4W-3L-VSI. Experimental results are presented to verify the effectiveness of the proposed MPC algorithm. © 1986-2012 IEEE