In this paper, a Markov transfer matrix is used to characterize exogenous sudden shocks, and a closed economic DSGE model including a financial accelerator is constructed to simulate the impact of sudden shocks on China’s macroeconomic performance. The study found that: (1) Sudden impacts reduce output, investment, consumption, capital, technology, and enterprise value, but improve labor, inflation, and risk premium, thus weakening macroeconomic risk resilience. (2) The impact of sudden shocks on macroeconomic variables, from large to small, is net worth, technical level, labor, inflation, investment, capital, output, and external premium. (3) It is appropriate for the government to adopt the principle of combining broad finance measures and tight currency controls in order to improve the risk resistance ability of macroeconomic operations.

This research has two objectives: (1) to model and analyze the momentum effect and (2) to propose a portfolio-reconstruction algorithm that uses the momentum effect to obtain excess return. The momentum effect tends to be present in the stock market and describes the phenomenon whereby rising (declining) stocks tend to continue to rise (decline). However, because existing research does not separate momentum effects from stock price fluctuations, it is not always possible to obtain an excess return when working with an unknown dataset that contains a momentum effect. In this research, we define a new external-force momentum-effect (EFME) model based on bias in stock price rises (declines). We prepared an artificial stock dataset that contained this momentum effect and constructed a portfolio with the proposed algorithm. Then, we analyzed the relationship between the EFME model and excess return and verify that excess return is obtained. Additionally, we confirmed that the proposed method yields higher excess return than the existing method when applied to artificial and real stock datasets.

Europe and Japan have both adopted negative interest rate policies as part of their monetary easing measures. However, despite the benefits that are claimed to be associated with increased lending demand, significant concerns exist regarding an increased burden on private financial institutions as a result of the application to their excess reserves. In this paper, we focus on the risks associated with increased investment of surplus funds for the operation of financial institutions. We propose an agent-based model for interlocking specific bankruptcy based on changes in financial situations as a result of market price fluctuations involving assets held by financial institutions. To extend the proposed model to handle macro market shocks, we describe decision making regarding funds that are surplus to the operation of financial institutions. Additionally, we analyze the impact of price declines involving marketable assets on financial systems.

This paper considers a two-echelon supply chain problem that includes a manufacturer and a retailer. The manufacturer plays a leading role in the supply chain and must make efforts to increase sales. Due to many uncertain factors in business, the market demand, manufacturing costs and retail operating costs are assumed to be uncertain variables. Expected and chance-constrained models are developed to address these uncertain variables. Stackelberg game is used to solve the proposed models. The equilibrium optimal wholesale price and unit margin are provided in order to determine the maximum profit. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed models.

We aim to develop a real-time feedback system of learning strategies during lesson time to improve academic achievement. It has been known that mutual viewing-based learning is an effective educational method. However, even though mutual viewing is an effective lesson style, there are effective or ineffective learning strategies in the learners’ individual activities. In general, the method of evaluating learning strategies is a questionnaire survey. However, the questionnaire cannot measure the learning strategies in real time. Thus, it is difficult to detect the students who use ineffective learning strategies during lesson time in real time. Recently, a system that can measure the learning strategies in real time has been developed. Using this system, it is possible to detect students who use ineffective learning strategies during lesson time on the mutual viewing-based learning. From this point of view, we aim to develop a recommendation system for real-time learning strategies for teachers and students to achieve a highly educational effect. For this purpose, we must know the features of effective or ineffective learning strategies via a system that can measure learning strategies. In this paper, we report the discovery of features of effective or ineffective learning strategies based on the data-mining approach using the k-means method, transition diagram, and random forest. We classified the time-series learning strategies over 40 min into 216 strategies and surveyed the improvement probability of academic achievement via a random-forest-based classification model. By embedding our results into the system, we may be able to automatically detect students who use ineffective learning strategies and recommend effective learning strategies.

With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.

With the advancement in internet technologies, requirements for quality of indoor wireless communication have increased. Femtocell, which is an effective approach to improve indoor communication quality, can provide highly-efficient indoor network services for users. This study puts forward a power resource control method based on Q learning algorithm for improved solutions to the problems of frequency spectrum and power resource allocation of a two-tier femtocell network. The algorithm was further improved, and was compared with the traditional algorithm via a simulation experiment. It was found that the improved Q learning algorithm could enhance the message capacity and control power resource; this provides a reference for the application of Q learning algorithm in femtocell communication.

We are now living in an era of multimedia data. The past decades have witnessed an unprecedented upsurge in “big data” in China. Mobile multimedia devices are becoming mainstream, promoting the conversion of big data technologies into practices. The deployment of big data provides people with great opportunities to deal with what were formerly great challenges.

Against this background, research is ongoing to explore further possibilities in mobile multimedia developing. Mobile Multimedia Big Data Embedded Systems are transforming science, engineering, healthcare, medicine, finance, business, and ultimately our society itself. This special issue focuses on the state-of-the-art initiatives and the great promise in this field.

We thank all of the authors who submitted papers for this special section. We are also highly appreciative of the reviewers who provided valuable review feedback on the submissions. The editor hopes that this special issue will attract researchers’ interest and contribute to further developments in this field.

The traditional image restoration method only uses the original image data as a dictionary to make sparse representation of the pending blocks, which leads to the poor adaptation of the dictionary and the blurred image of the restoration. And only the effective information around the restored block is used for sparse coding, without considering the characteristics of image blocks, and the prior knowledge is limited. Therefore, in the big data environment, a new method of image restoration based on structural coefficient propagation is proposed. The clustering method is used to divide the image into several small area image blocks with similar structures, classify the images according to the features, and train the different feature types of the image blocks and their corresponding adaptive dictionaries. According to the characteristics of the restored image blocks, the restoration order is determined through the sparse structural propagation analysis, and the image restoration is achieved by sparse coding. The design method is programmed, and the image restoration in big data environment is realized by designing the system. Experimental results show that the proposed method can effectively restore images and has high quality and efficiency.

At present, the error control method for high-speed serial data transmission obtains the errors by comparison and then controls them. If the data transmission channel is not denoised, the packet loss and error codes become serious, and energy consumption increases. The use of fuzzy classification is proposed to control data transmission errors. The method uses the combination of wavelet transform and transform domain difference to double denoise the channel, and it completes the clustering of data transmission errors by fuzzy classification. Considering packet loss, error codes, and energy consumption in data transmission error control, when the communication distance between two nodes is small, automatic repeat request is used to control data transmission errors. As the distance between nodes increases, forward error correction is used to control data transmission errors. When the communication distance gradually increases, data transmission errors are controlled by hybrid automatic repeat request. Experiments showed that the proposed method can reduce the data transmission error, control energy consumption, packet loss rate, and bit error rate, and enhance the denoising effect.

In order to meet the increasing demand, the demand of cold chain logistics under the background of B2C e-commerce mode is predicted, to provide theoretical guidance for the development of cold chain logistics. A multivariate linear regression demand prediction model based on grey relational analysis is proposed. The present situation of cold chain logistics demand is as the basis for the analysis. Using appropriate quantitative analysis method, the factors affecting the demand of cold chain logistics are screened, and the selection principles of logistics demand evaluation index for cold chain products are determined, including product supply, logistics demand scale, and cold chain efficiency and so on. The grey correlation analysis is used to standardize the data sequence and calculate the correlation degree between the factors. The factor of large correlation degree is chosen as the key factor, and the multivariate linear regression prediction equation is constructed. According to the progressive regression idea, the model is amended to improve the goodness of fit of the model. The grey multivariate regression model is applied to predict and analyze the cold chain logistics demand of a fruit product in a certain city. The result shows that the model can predict the demand of cold chain logistics accurately.

The retrieval of features in a large-scale image database can improve the degree of visualization of images. The conventional method of feature-retrieval is a time-consuming process because it retrieves by searching the keywords. In this paper, a rapid feature retrieval method based on granular computing is proposed for use in a large-scale image database. In this method, we first collect and process the images from the database. Next, we construct a binary tree to realize the multi-class classification of the image features and complete the feature retrieval using support vector machines. The experimental results demonstrate that the proposed method can effectively retrieve the features in the large-scale image database. The effectiveness of retrieval can reach more than 95%.

In the field of accounting in our country, the research of accounting information disclosure in universities has not been paid enough attention. With the deepening of the reform of higher education, the diversification of financing channels of universities has made the financial management of universities more complex. It requires a more robust index system of accounting information disclosure of universities which can show the accounting information truly, timely and completely in the accounting report of universities. This paper summarizes the significance, principles, and methods of the system establishment of accounting information disclosure of universities. On the basis of the work, the index system of accounting information disclosure of universities with four rule layers and sixteen scheme layers is constructed. According to the method of analytic hierarchy process, this paper assigns the weight of each scheme layer and constructs a complete index system of accounting information disclosure of universities.

With the rapid development of the real estate market, real estate evaluation is becoming more and more important and active. The real estate is now evaluated according to the expertise and experience of the appraiser. The evaluation results are often influenced by the subjective randomness of the evaluation personnel and the complicated and changeable environmental factors. It is not only a professional technology, but also a complicated art. Therefore, how to improve the scientific, accuracy and efficiency of real estate evaluation has become an important issue that needs to be studied and solved in the real estate evaluation industry. This paper takes mass real estate evaluation system as the research object, adopts the BP neural network to research the design principles of the evaluation system and the design method of the model, and designs and develops the mass intelligent evaluation system to improve the intelligence, scientific, accuracy and credibility of the evaluation system.

Innovation in this paper adopts the method of Internet of things, add solved the transportation scheduling based on the experience of the solid waste before feedback slower, based on the mathematical model of solid waste transportation scheduling problems cannot cope with the sudden situation. After fully considering the requirements of solid waste transportation green sustainability and the maximization of waste clearance in sanitation PPP project, the scheduling strategy based on iot data is given. And through in XX city, XX district cleaning section for example, the application shows that this method can meet the demand of public sanitation services, the basis of the solution of the maximum fuel consumption, reduce sanitation company the possibility of a fine, maximize trash pickup company interests, improve the efficiency of transportation.

The present current control method in doubly fed induction generator cannot realize the segmented grid-connected current control, it’s hard to effectively control the current in doubly fed induction generator. Therefore, a current control method in doubly fed induction generator under low switching frequency is proposed in this paper. Which means to build a mathematical model of the doubly fed induction generator under low switching frequency to analyze the parameters of doubly fed induction generator filter. Then the parameter values of the filter can be obtained. The current in generator can be predicted by adopting the double-sampling predict method. And the current control in generator can be improved according to dead beat control. Then the on-line identification of current parameters by least square method is needed to finish the current control method in doubly fed induction generator under low switching frequency. The experimental results show that the proposed method realized the segmented grid-connected current control in doubly fed induction generator.

In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

At present, the vegetable yield estimation in China is performed by manual sampling and visual observation of vegetable counts. This is not only time-consuming and labor-intensive, but it also has low precision. In this study, we capture video surveillance images of the tomatoes during plant maturation, and use neural networks to identify pictures, extract growing features, identify the number of vegetables hanging from the plants, and establish an estimation model for tomato yield. We then take a sample of the vegetables to be measured. Strains are image-analyzed and processed to predict yield per plant and yield per unit area to obtain an accurate prediction of tomato yield.