PM2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability.

Video object segmentation (VOS) is the fundamental problem of vision-based intelligent transportation, and many VOS algorithms relying on inference from reference masks have been proposed. Due to the inherent defects of the inference strategy and the complex changes of targets, VOS methods that perform well on public datasets are usually ineffective in airport scenarios. We propose a spatiotemporal alignment network (STA-Net) that makes use of Automatic Dependent Surveillance-Broadcast (ADS-B) data as prior information to guide the long-term segmentation of aircraft. ADS-B is an airport-specific signal, which indicates the location of aircraft in real time. Based on ADS-B, we continuously generate new reference masks instead of using previous masks for inference, which greatly reduces the accumulation of inference errors. To achieve this, previous masks of each aircraft are aligned on the temporal domain based on the position information in ADS-B. All temporally-aligned masks are compared, and the one most similar to the current instant is reserved. This mask is both temporally and spatially aligned; hence it is a better reference mask for inference. Aligned masks are updated every time new ADS-B data arrive, so that they can support long-term inference. With the selected mask as a reference, aircraft of interest are segmented within a unified encoder-decoder framework over the long term. Experiments on a benchmark dataset and in a real airport scenario verify the effectiveness of the presented method.

Change detection in airport ground is important for airport security. Due to the particularity of ground environment, e.g. haze and camouflage, airport ground change detection is generally incomplete. If an incomplete detection is used as reference for the detection in subsequent frames, it may result in noticeable detection defects across the frames. In this paper, extended motion diffusion (EMD) is proposed to address the problems. The core idea of the EMD is to design a novel model insensitive to incomplete detection. Firstly the one-to-many correspondence in traditional motion diffusion is extended in the prediction step of EMD to build up correspondence from incomplete detection to intact objects. Prior information, e.g. aircraft motion prior and ground structure prior, is employed in the development of the correspondence. Then based on the correspondence a number of new samples are synthesized and filtered in the identification step of the EMD to compensate possible detection defects. Finally, the reserved samples are collected to train a foreground model, which is used in conjunction with another background model for classification. The proposed method is verified based on the Airport Ground Video Surveillance (AGVS) benchmark. Experimental results show effectiveness of the proposed algorithm in dealing with haze and camouflage.

This paper proposes a fusion method of multiple sensors data on panoramic video and is applied to airport surface surveillance. The framework flow chart is given, then the whole processes of the fusion method are described in detail. The transformation principle between pixel coordinates and global position system(GPS) coordinates is introduced. We develop a new method to get the optimal value of homography trans-formation, which is more suitable for engineering application. In the end, the experimental results proves the effectiveness of the proposed method.

Summary The stability and permeability of the collapsible loess are poor, and the infiltrated water in the loess is difficult to drain out. Due to the dynamic loads induced by trains, the collapsible loess railway subgrade is prone to instability and liquefaction. In this paper, an organic polymer material, Modified Polyimide Grease (MPG) produced was proposed to treat the collapsible loess railway subgrade. The scanning electron microscope, FTIR, and X‐ray diffraction methods were used on the study of the mechanism of MPG. The results indicated that an optimum MPG content of 2.5%, approximately 3 day, could achieve the highest strength and result in the best softening coefficient. After being treated by MPG, the permeability of the loess samples was slightly decreased compared to that of untreated samples, which indicated MPG could improve the strength of the loess without significantly reducing its permeability. This property was suitable for the subject MPG treated loess subgrade to achieve a reasonable drainage. Besides, it is observed that some portions of pores in loess samples were filled by MPG and the strength between the particles was increased. The research results indicated that MPG is a suitable material to improve collapsible loess railway subgrade in the subject area.

Class imbalance refers to the instance where the number of training samples for the majority classes is far more than that of the minority classes (relative imbalance), and the quality of training samples for the minority classes is inferior to that of the majority classes (absolute imbalance), which are further complicated by other imbalance factors, e.g., data overlapping. Video background subtraction aims to classify each pixel into two classes: foreground and background. This paper first reveals that background subtraction is a class imbalance problem, where the foreground and background are the minority and majority classes, respectively. By exploring spatial and temporal correlation inherent in video data, we present an imbalance compensation framework for background subtraction, which consists of two sequential modules, imbalance-compensated bilayer modeling, and imbalance-compensated Bayesian classification. In the first module, spatio-temporal oversampling (SOS) and selective downsampling (SDS) are proposed to compensate the imbalance at data level. SOS attempts to synthesize representative samples appended to the minority sample set, while SDS selectively deletes a number of majority samples in data overlapping areas. The rebalanced samples are then used to learn a bilayer model. In the second module, novel cost functions are proposed to compensate the effect of class imbalance at algorithm level. The cost functions are based on imbalance measurement, and used to construct the prior term in the Bayesian classification scheme. Experiments are conducted on public databases to demonstrate the effectiveness of the proposed method.

We present a learning object XML markup language (LOML) based on object-oriented model of inheritance. Based on Wiley's definition, a learning object is defined as a combination of smaller knowledge bits, such as text, image, and clips, which are integrated together to explain or describe a minimal concept in a course.