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The rapid growth of emerging technologies with artificial intelligence and big data as new representatives has brought great influence to all walks of life, as well as the education. College sports is an important part of the education system and deeply affected by the artificial intelligence. The paper will briefly summarize the current status of college physical education in China and discuss how to innovate its methods in the era of artificial intelligence.

In this paper, we report one bifacial p-type PERC solar cell with efficiency over 22% using laser doped selective emitter produced in larger-scale commercial line on 6-inch mono-crystalline wafer. On front side of the solar cell, square resistance of p-n junction was found to be closely related with laser power at certain laser scan speed and frequency. On the other side, the rear fingers with different ratios of height and width and rear silicon nitride (SiNx) layer with different thickness were optimized, and a highest rear efficiency of the bifacial solar cell was obtained. Finally, bifacial silicon solar cells with the front and rear efficiencies exceeding 22% and 15% (AM1.5, 1000 W/m2, 25 °C) were successfully achieved, respectively.

The listed companies are outstanding representatives of enterprise groups in China that have a significant influence on the national economic development and the industrial transformation. Their choices of financing behavior are not only transparent but also have a model effect. This chapter examines the refinancing behavior of Shanxi’s listed resource companies from two aspects, general and group characteristics. The study finds that the current refinancing selection of Shanxi’s listed resource companies is a combination of results of Shanxi’s resource-based economic structure, the development cycle of the coal industry, the influence of the state-owned assets management system, the policy and social governance environment, the level of the enterprises’ own development and other factors. The chapter also puts forward some policy suggestions on various factors influencing the refinancing of Shanxi’s listed resource companies.

Bridges have become an important part of urban transport, and the length and number of Chinese bridges are world leaders. In the event of an accident, it will cause significant losses. Therefore, it is especially important to prevent the health of the bridge and to repair it in advance. This paper describes the system monitoring of bridge monitoring based on situational awareness-based big data. Based on the development process of bridge monitoring, a big data bridge processing system based on situational awareness technology is proposed. The system uses the situational awareness architecture to take machine learning as the core, and the data collected by the sensor is imported into the bridge health monitoring system. The monitoring data is processed by the expert system of machine learning, and the processing suggestions are given in the human-computer interaction interface. Finally, the paper gives the problems to be solved and future prospects of bridge monitoring.

Due to the influence of space equivalent representation and pattern composition, traditional principal component analysis (PCA) is faced with challenges in identifying small faults. This paper proposes a fault diagnosis method is proposed based on multi-levels principal component space based on high-dimensional feature representation. Firstly, based on the original PCA, the dimensions are expanded first and then reduced, so that the information that cannot be expressed in the original space is fully expressed. This method further divides the principal element space based on the eigenvalues, and projects the occurrence of the event and the size of the identification abnormality according to the projection to different sub-spaces. Compared with the usual fault detection methods, simulations verify that this algorithm can effectively measure the level of faults and improve the correct detection rate of minor faults.

Multivariate statistical analysis is often used in fault diagnosis. PCA is a commonly used multivariate statistical analysis method, based on the threshold method for fault diagnosis. But the PCA is the original space of the equivalent that didn't increase any amount of information, often resulting in a lot of minor failures difficult to diagnose. Deep Learning has a successful application in pattern recognition, linear representation of detail based on multi-level network. But Deep Learning is not explainable, only to give training results and there is no theoretical basis. On the basis of the original PCA, firstly expands and then reduces the dimension. The former is intend to fully express the information which cannot be expressed in original space by adding a projection, two projection,..., on the basis of original projection, and so on. And then generate a multi-level projection frame representing the space from the sample space high-dimensional feature. Experiment shows that the minor faults that cannot be detected in the original projection space can be detected under the projection frame of the new multi-level high-dimensional feature, and the detection rate of fault detection is improved.

This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also to achieve a better result. Finally, an experiment is provided for testing. Simulation results show the method proposed here is valid and has good performance.

In this paper, a software-defined radio access network (SD-RAN) is considered where content caching is performed at the small-cell base stations (SBSs) under the coordination of a central macro-cell base station (MBS). Two benchmark algorithms are firstly discussed to maximize the content hit ratio within the associated small cells locally and to maximize the content hit ratio within the macro cell, respectively. In order to minimize the content provisioning cost of the entire SD-RAN, it needs to strike a balance between the local hit ratio and the hit ratio within the macro cell. With this inspiration, a heuristic cooperative caching algorithm is proposed by dividing the cache space at each SBS into two portions for storing the duplicated contents and unique contents respectively. The optimal partition factor at which the average content provisioning cost of SD-RAN is minimized is analytically obtained in a closed form.

In this paper, the problem of stabilizing two agents with a beacon into a triangular formation is studied, in which the beacon is static and the specified triangle is characterized only by interior angles constraints. Assuming that each agent can measure the bearing of the beacon in its local frame. Meanwhile, the two agents neighbor each other, that is, they communicate with each other so that they can measure the bearing angle respectively and exchange the measured angle information each other. A control strategy is proposed utilizing only angle information which steers the two agents to form a triangular formation with the beacon. Both theoretical analysis and simulations are provided to demonstrate the effectiveness of the control algorithm.

Because of the serious non-linearity and coupling among state variables caused by the complex energy conversion process in ultrasonic motor, the motion control strategy of ultrasonic motor still need to be studied in detail to obtain better performance. As a kind of special control method, the low-frequency PWM control strategy can make the on-line optimum control easier, and may be an effective means to realize high performance motion control using ultrasonic motor. But this kind of strategy has not been studied in detail until now. This paper analyzes the difference among three low-frequency PWM control methods in details, works out the principle of selection among these methods. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3262

This paper presents a biologically plausible model for the structural information processing in early vision. Our investigation on the frequency spectrum of natural images filtered by the retina shows that the DC component containing much redundancy information and the high frequency components containing much noisy information are reduced, while the middle and low frequency components containing much structural information are enhanced. Simple cells in the primary visual cortex (V1) extract structural primitives from the filtered signals resulting in the emergence of diverse receptive field shapes. We name these structural primitives as structors, and study the neural mechanisms responsible for this diversity of V1 simple cell receptive field shapes. Sparse coding with the L0-norm constraint is reexamined which suggests that the local structure of natural images is determined by few structors regardless of their coefficients. We perform an analysis on the spatial distribution of the input signal and prove that signals in the neighborhood of a special structor has a star shape and peaks at the structor. That is, the structors are the modes of the probability density function of the input signal, and learning the structors can be interpreted as mode detection. Mean sift method is applied to detect modes, and the updating rule for the mean shift appears to be Hebbian. We propose the Hebbian-based mean shift to simulate the emergence of the diversity of simple cell receptive field shapes. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like structors.