The dynamics of an sRNA-regulated quorum sensing network model were investigated. The stability and the existence conditions for Hopf bifurcation were obtained. A linear feedback controller was proposed to stabilize the Hopf bifurcation of the system. Numerical simulations verified the theoretical analysis results.

A disparity optimization algorithm based on an improved guided filter is proposed to smooth the disparity image. A well-known problem to local stereo matching is the low matching accuracy and staircase effect in regions with weak texture and slope. Our disparity optimization method solves this problem and achieve a smooth disparity. First, the initial disparity image is obtained by a local stereo matching algorithm using segment tree. Then, the guided filter is improved by using gradient domain information. Lastly, the improved guided filter is adopted as the disparity optimization method to smooth the disparity image. Experiments conducted on the Middlebury data sets demonstrate that by using the proposed algorithm in this paper, the smoothness of the disparity map in slope regions is improved, and a higher precision of dense disparity is obtained.

The main function of Hadoop is the storage and processing of big data, especially the processing of large datasets. However, in practice, there are numerous small files, and Hadoop has many flaws when dealing with these small files. A storage-optimization method for numerous agricultural resource small files based on Hadoop is proposed, using the precursor and subsequent relationship between different small files of agricultural resources to merge small files. By accessing small files and performing metadata caching through an index mechanism, as well as the prefetching mechanism of associated small files, the storage-optimization method improves the reading efficiency. Experimental results show that this method reduces the memory consumption of the Hadoop name node and improves the performance of the system.

This paper proposes a new non-parametric adaptive combination model for the prediction of realized volatility on the basis of applying and extending the time-varying probability density function theory. We initially construct an adaptive time-varying weight mechanism for a combination forecast. To compare the predictive power of the models, we take the SPA test, which uses bootstrap as the evaluation criterion and employs the rolling window strategy for out-of-sample forecasting. The empirical study shows that the non-parametric TVF model forecasts more accurately than the HAR-RV model. In addition, the average combination forecast model does not have a significant advantage over any single model while our adaptive combination model does.

The role of foreign direct investment (FDI) in promoting entrepreneurship has attracted increasing attention from academics and policymakers. However, empirical research has failed to consider model uncertainty, leading to contradictory results. This study designs a new semi-parametric path approach to identify the mechanism and strength of FDIs’ impact on entrepreneurial activities through three levels of difference: the general, time, and country. Our results indicate that a conflicting relationship may occur between FDIs and entrepreneurial activity because the opposing influences at these three levels negate them. Nevertheless, our detailed results are important for guiding business organizations not only among the European Union, but also in specific European countries.

This paper empirically investigates the AH share premium puzzle considering the impact of economic policy uncertainty (EPU) in China, using Hang Seng AH premium (HSAHP) index data from March 2011 to June 2018. Specifically, the index of Baker, Bloom, and Davis is used as a proxy for EPU in China. The data has been divided into two periods: 0 and 1. Period 0 includes data before the launch of the stock connect program and spans from March 2011 to October 2014, while period 1 represents data from after the launch of stock connect program and spans from November 2014 to June 2018. To more robustly test the change in AH premiums after the “connect” is launched; we evaluate the impact of Chinese EPU using non-parametric kernel density estimation. The empirical results show that parameter uncertainty explains variations in price disparity and can significantly reduce the returns of the AH share premium index.

This study examines the impact of external economic policy uncertainty on the distribution of China’s stock returns. The Chinese Economic Policy Uncertainty (CEPU) and global EPU (GEPU) indexes compiled by [1] are employed as a measurement of the external uncertainty. An empirical study is conducted using the GARCH-MIDAS framework. The first innovation of this study is extending the symmetric GARCH-MIDAS model to the case of GJR; the leverage effect is therefore considered. The second innovation is considering the impact of EPU on the overall distribution of returns, rather than on the mean or volatility. Full-sample fitting shows that CEPU can explain around 14% of the return volatility, and CEPU together with GEPU can explain about 17%. Out-of-sample recursive forecasting demonstrates that it is meaningful to extend the models to GJR; the EPU information improves the return distribution forecasting. However, the impact of EPUs is limited, which implies that external uncertainty is quite different from the “internal” economic policy uncertainty directly driving the China’s stock market.

As an important tool to promote the technical standards for enterprises, a patent alliance can reduce transaction costs and lawsuit disputes as well as accelerate the promotion and application of proprietary technologies. Moreover, it can enlarge the installed base and influence consumers’ expectations by exploiting the network effects of technical standards. As a result, it can be one of the most effective paths to resolve the problem of patent thicket and promote innovation. Based on several theories with respect to the network effects, this study analyzed the connotation and composition of network effects for technical standards and constructed a concept model that can influence innovations using a patent alliance. A questionnaire investigation was conducted, and an analysis was performed using the structural equation to examine the factors affecting the innovation of patent tools as well as their acting paths. The main research conclusions of this study are as follows: (1) patent alliance is a double-edged sword for technical innovation. The factors related to the patent alliance and network effect, such as the installation-foundation effect, consumer expectation, and positive feedback effect, promote innovation. However, the lock-in effect in the network effect hinders the innovation. (2) Identification of intellectual property and partners is the key factor that influences patent alliance innovation. (3) Standardization strategies and the government play the least role among all innovation factors influencing patent alliance.

We develop a new Keynesian model featuring a dual-pillar monetary policy. We employ this framework to analyze the effects of coordinating macro-prudential rule and monetary policy in China using different tools. The simulation results show that: (1) adopting macro-prudential rule and monetary policy simultaneously can achieve a more stable economic environment than using monetary policy alone; (2) a price-based monetary policy is more effective in stabilizing economic fluctuations than a quantity-based monetary policy when considering the macro-prudential policy; (3) the combination of quantity-based monetary policy and macro-prudential rule can stabilize housing prices and credit growth better than the price-based tools. The study shows that when house prices rise rapidly owing to external shocks, adopting the quantity-based policy instruments and macro-prudential policy is a wise choice. When the financial condition is stable, the combination of price-based instruments and macro-prudential rule is better.

Service robots gain both geometric and semantic information about the environment with the help of semantic mapping, providing more intelligent services. However, a majority of studies for semantic mapping thus far require priori knowledge 3D object models or maps with a few object categories that neglect separate individual objects. In view of these problems, an object-oriented 3D semantic mapping method is proposed by combining state-of-the-art deep-learning-based instance segmentation and a visual simultaneous localization and mapping (SLAM) algorithm, which helps robots not only gain navigation-oriented geometric information about the surrounding environment, but also obtain individually-oriented attribute and location information about the objects. Meanwhile, an object recognition and target association algorithm applied to continuous image frames is proposed by combining visual SLAM, which uses visual consistency between image frames to promote the result of object matching and recognition over continuous image frames, and improve the object recognition accuracy. Finally, a 3D semantic mapping system is implemented based on Mask R-CNN and ORB-SLAM2 frameworks. A simulation experiment is carried out on the ICL-NUIM dataset and the experimental results show that the system can generally recognize all the types of objects in the scene and generate fine point cloud models of these objects, which verifies the effectiveness of our algorithm.

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. In this paper, we present an efficient approach of change-classifier-learning, more specifically, in the proposed approach, a collection of place-specific change classifiers is employed. Our approach requires the memorization of only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Furthermore, through the proposed approach the most recent map can be incorporated into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. The proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.

In a conventional notation used in many studies, a probability space and state space of random variables is identified by its symbol. However, such a notation makes a formula ambiguous in a large equation. This letter proposes to use an index set to identify the probability space and state space of random variables. It is shown that the proposed notation can increase the generality of formulas without ambiguity.

This study proposes a novel method for the segmentation of Archaic Chinese sentences based on a bidirectional long short-term memory (LSTM) + conditional random field (CRF) model. The method added a layer of linear statistical model to the traditional bidirectional LSTM neural network; it can be used for sequence annotation from the sentence level. In addition, this model introduced the stochastic gradient descent (SGD) to prevent excessive fitting, and the viterbi algorithm was used to calculate the optimal sequence of the sentences. In the experiment, this study tests the performance of the proposed method using the History of the Han Dynasty, the History of the later Han Dynasty, Three Kingdoms, and the Book of Jin, amongst others. The results show that the precision value, recall value, and F1 value are 0.77, 0.75, and 0.76, respectively, in the open test, and 0.90, 0.88, and 0.76, respectively, in the closed test.

A quantum circuit implementation of Powell’s conjugate direction method (“Powell’s method”) is proposed based on quantum basic transformations in this study. Powell’s method intends to find the minimum of a function, including a sequence of parameters, by changing one parameter at a time. The quantum circuits that implement Powell’s method are logically built by combining quantum computing units and basic quantum gates. The main contributions of this study are the quantum realization of a quadratic equation, the proposal of a quantum one-dimensional search algorithm, the quantum implementation of updating the searching direction array (SDA), and the quantum judgment of stopping the Powell’s iteration. A simulation demonstrates the execution of Powell’s method, and future applications, such as data fitting and image registration, are discussed.

Mobile robots equipped with camera sensors are required to perceive surrounding humans and their actions for safe and autonomous navigation. In this work, moving humans are the target objects. For robot vision, real-time performance is an important requirement. Therefore, we propose a robot vision system in which the original images captured by a camera sensor are described by optical flow. These images are then used as inputs to a classifier. For classifying images into human and not-human classifications, and the actions, we use a convolutional neural network (CNN), rather than coding invariant features. Moreover, we present a local search window as a novel detector for clipping partial images around target objects in an original image. Through the experiments, we ultimately show that the robot vision system is able to detect moving humans and recognize action in real time.

In this study, the stability and Hopf bifurcation of a genetic regulatory network with delays are addressed. Some bifurcations may cause network oscillation and induce instability. An impulsive control method is proposed to control the bifurcations. A numerical simulation was performed to demonstrate the effectiveness of the theoretical results.

To estimate the motion state of object feature point in image space, an adaptive decorrelation Kalman filtering model is proposed in this paper. The model is based on the Kalman filtering method. A first-order Markov sequence model is used to describe the colored measurement noise. To eliminate the colored noise, the measurement equation is reconstructed and then a cross-correlation between the process noise and the newly measurement noise is established. To eliminate the noise cross-correlation, a reconstructed process equation is proposed. According to the new process and measurement equations, and the noise mathematical characteristics of the standard Kalman filtering method, the parameters involved in the new process equation can be acquired. Then the noise cross-correlation can be successfully eliminated, and a decorrelation Kalman filtering model can be obtained. At the same time, for obtaining a more accurate measurement noise variance, an adaptive recursive algorithm is proposed to update the measurement noise variance based on the correlation method. It overcomes the limitations of traditional correlation methods used for noise variance estimation, thus, a relatively accurate Kalman filtering model can be obtained. The simulation shows that the proposed method improves the estimation accuracy of the motion state of object feature point.

We propose UCT-Grid Area Search (UCT-GAS), which is an efficient optimization method that roughly estimates specific values in areas, and consider exploration and exploitation in optimization problems. This approach divides the search space and imagines it to be a multi-armed bandit, which enables us to use bandit algorithms to solve mathematical programming problems. Although the search speed is fast than other search algorithm like differential evolution, it might converge to a local solution. In this study, we improve this algorithm by replacing its random search part with differential evolution after several searches. Comparative experiments confirmed the search ability of the optimal solution, and our method benefits by showing that it avoids falling into a local solution and that its search speed is fast.

The purpose of this study was to assess the symmetrical movements of the arms during walking to prevent falling. In this study, we developed a measurement and analysis system to assess walking movements objectively. In addition, index values were calculated as the symmetry of arm movements. Five healthy participants performed two tasks. The first task was straight walking. The second task was straight walking with weight attached to one arm. The results showed high index values of the swinging arms in both tasks. The index values of the bending arms were greater in the first task than in the second task. Consequently, the index values indicated the symmetrical movements of walking.

The enrollment work of higher vocational colleges is an important part of a school’s strategic decision-making. Developing a reasonable enrollment plan is highly important for a school’s development. Previous enrollment information contains extensive valuable information, which should be used by adopting effective methods of data processing. This study used an improved Apriori algorithm to mine the association rules of enrollment information to obtain the factors that affect enrollment. A higher vocational college in Qingdao was taken as the object of study. Three attributes were selected for association rule mining: college entrance exam results, applied majors, and student background. It was found that student registration rates were significantly different under different rules. The data mining results can provide policy support for future enrollment plans.

Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and isolated notes separately. Specially structured note groups are identified by primitive decomposition and structural analysis. The note groups are decomposed into three fundamental elements: note stem, note head, and note beams. Isolated music symbols are recognized based on shape model descriptors. We conduct tests on real pictures taken live by a camera. The tests show that the proposed method has a higher recognition rate.

Real-time performance assessment is one of the main methods to guarantee the steady operation of production during the combustion process of a coke oven. In this study, a real-time assessment method is proposed for this combustion process based on the analytic hierarchy process (AHP) and intuitionistic multiplicative preference relation. Relevant scholars, senior engineers, and elite workers participated in this project to build the AHP model with three aspects (i.e. safety, stability, and economic benefit) and perform pairwise comparisons of criteria and sub-criteria through group decisions. To support real-time, the pairwise comparisons of alternatives were realized by an automated method using measurement values. This comprehensive assessment method demonstrates ability to provide real-time performance evaluation for the combustion process. An experiment was conducted to evaluate the effectiveness and viability of the proposed method.