An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability

whu-nercms at trecvid2021:instance search task

We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interactive tasks of Instance Search (INS). For the automatic task, the retrieval target is divided into two parts, person retrieval, and action retrieval. We adopt a two-stage method including face detection and face recognition for person retrieval and two kinds of action detection methods consisting of three frame-based human-object interaction detection methods and two video-based general action detection methods for action retrieval. After that, the person retrieval results and action retrieval results are fused to initialize the result ranking lists. In addition, we make attempts to use complementary methods to further improve search performance. For interactive tasks, we test two different interaction strategies on the fusion results. We submit 4 runs for automatic and interactive tasks respectively. The introduction of each run is shown in Table 1. The official evaluations show that the proposed strategies rank 1st in both automatic and interactive tracks.Comment: 9 pages, 4 figure

Strain-restricted transfer of ferromagnetic electrodes for constructing reproducibly superior-quality spintronic devices

Spintronic device is the fundamental platform for spin-related academic and practical studies. However, conventional techniques with energetic deposition or boorish transfer of ferromagnetic metal inevitably introduce uncontrollable damage and undesired contamination in various spin-transport-channel materials, leading to partially attenuated and widely distributed spintronic device performances. These issues will eventually confuse the conclusions of academic studies and limit the practical applications of spintronics. Here we propose a polymer-assistant strain-restricted transfer technique that allows perfectly transferring the pre-patterned ferromagnetic electrodes onto channel materials without any damage and change on the properties of magnetism, interface, and channel. This technique is found productive for pursuing superior-quality spintronic devices with high controllability and reproducibility. It can also apply to various-kind (organic, inorganic, organic-inorganic hybrid, or carbon-based) and diverse-morphology (smooth, rough, even discontinuous) channel materials. This technique can be very useful for reliable device construction and will facilitate the technological transition of spintronic study

Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet

Automatic seizure detection technology has important implications for reducing the workload of neurologists for epilepsy diagnosis and treatment. Due to the unpredictable nature of seizures, the imbalanced classification of seizure and nonseizure data continues to be challenging. In this work, we first propose a novel algorithm named the borderline nearest neighbor synthetic minority oversampling technique (BNNSMOTE) to address the imbalanced classification problem and improve seizure detection performance. The algorithm uses the nearest neighbor notion to generate nonseizure samples near the boundary, then determines the seizure samples that are difficult to learn at the boundary, and lastly selects seizure samples at random to be used in the synthesis of new samples. In view of the characteristic that electroencephalogram (EEG) signals are one-dimensional signals, we then develop a 1D-MobileNet model to validate the algorithm’s performance. Results demonstrate that the proposed algorithm outperforms previous seizure detection methods on the CHB-MIT dataset, achieving an average accuracy of 99.40%, a recall value of 87.46%, a precision of 97.17%, and an F1-score of 91.90%, respectively. We also had considerable success when we used additional datasets for verification at the same time. Our algorithm’s data augmentation effects are more pronounced and perform better at seizure detection than the existing imbalanced techniques. Besides, the model’s parameters and calculation volume have been significantly reduced, making it more suitable for mobile terminals and embedded devices

Atomic Diffusion and Crystal Structure Evolution at the Fe-Ti Interface: Molecular Dynamics Simulations

The diffusion bonding method is one of the most essential manufacturing technologies for Ti-steel composite plates. In this paper, the atomic diffusion behavior at the Fe-Ti interface during the bonding process of Ti-steel composite plates is studied using classical diffusion theory and molecular dynamics (MD) simulation. Henceforth, the diffusion mechanism of Fe and Ti atoms at the bonding interface is obtained at the atomic scale. The results show that Fe and Ti atoms diffused deeply into each other during the diffusion process. This behavior consequently increased the thickness of the diffusion layer. Moreover, the diffusion quantity of Fe atoms to the Ti side was much greater than that of Ti atoms to the Fe side. Large plastic deformation and shear strain occurred at the diffusion interface during diffusion. The crystal structure of the diffusion zone was damaged and defects were generated, which was beneficial to the diffusion behavior of the interface atoms. As the diffusion time and temperature increased, the shear strain of the atoms at the interface also increased. Furthermore, there is a relationship between the mutual diffusion coefficient and the temperature. Subsequently, after the diffusion temperature was raised, the mutual diffusion coefficient and atomic disorder (Fe atom and Ti atom) increased accordingly

INTERFERENCE AND CONTACT ANALYSIS OF SELF-ALIGNING BEARING IN LARGE ROLLING MILL BASED ON A SIMPLIFIED MODEL

The working conditions of self-aligning bearings in rolling mills are complex. The actual life of the bearing often does not reach the design life as expected. Therefore, it is of great significance to study the global load distribution and local stress distribution of self-aligning roller bearing. The global model involving contact of a number of elements often results in a long calculation time and convergence difficulty. Firstly, proposes a new simplified finite element model of bearing based on Hertz contact theory, which can greatly improve the calculation efficiency. Then, the roller contact loading distribution of the global model is solved based on the interference fit amount of the shaft and the inner ring of the bearing. Finally, the displacements corresponding to the maximum contact load is used as the boundary conditions of the local model, and the stress distribution of the inner ring is solved. The results show that the loading ratio of the two rows of raceways and loading distribution of the contact are varied with working conditions. The interference fit amount specified in the present study makes the minimum Mises stress of the inner ring greater than 40 MPa. This will reduce the strength and fatigue life of the bearing

Thermal performance assessment of cold chain chamber with vacuum insulation panel envelope layer

As one of the super thermal adiabatic materials nowadays, Vacuum Insulation Panel has slim thermal conductivity, which can be no more than 0.0040 W/(m·K) at an internal pressure of less than 10Pa. the excellent thermal performance makes it prosperous in cold chin experiments. Actually, Vacuum Insulation Panel is normally installed with other insulation materials as multilayer envelope in practice. However, influenced by the fluctuation of environmental factors and the difference of thermal & physical properties of materials, the overall thermal performance of multilayer insulation systems with Vacuum Insulation Panel different arrangement sequences is significantly various. A reasonable Vacuum Insulation Panel location plays an important role on the thermal performance. In this paper, the thermal insulation performance of multilayer under unsteady state condition was studied. Three chambers, with the same exterior size, that is, 300 × 300 × 300 mm3, were employed for the innovation purpose. The multi-layer insulation construction involves a 10 mm layer of Vacuum Insulation Panels and a 10 mm layer of polyurethane. And three different envelope structures, namely, internal insulation, sandwich insulation and external insulation were built according to the Vacuum Insulation Panel's location. The numerical heat transfer model and physical model were established. The simulative and experimental results were comprised and analyzed. The results implied that, to maintain the constant temperature in cold/hot chamber, the vacuum insulation panels are located on the face of temperature fluctuation side, i.e. the ambient side, and have a better overall insulation performance

Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks

Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets, liquidity, and credit risk systemic risk. By constructing a Savitzky-Golay-TCN deep convolutional neural network, the systemic risk indicators of China's financial market are predicted, and their accuracy and reliability are analyzed. The research found that: 1) Savitzky-Golay-TCN deep convolutional neural network has a strong generalization ability, and the prediction effect on all indices is stable. 2) Compared with the three control models (time-series convolutional network (TCN), convolutional neural network (CNN), and long short-term memory (LSTM)), the Savitzky-Golay-TCN deep convolutional neural network has excellent prediction accuracy, and its average prediction accuracy for all indices has increased. 3) Savitzky-Golay-TCN deep convolutional neural network can better monitor financial market changes and effectively predict systemic risk