Showing papers by "Shandong Institute of Business and Technology published in 2019"
TL;DR: In this article, the authors investigated the dynamic directional information spillover of return and volatility between the fossil energy market, investor sentiment towards renewable energy and the renewable energy stock market using the connectedness network approach.
146 citations
TL;DR: Wang et al. as discussed by the authors investigated whether China's development aligns with the Environmental Kuznets Curve and Pollution Haven hypotheses, and used the fixed effects panel data partially linear additive model, which integrates economic growth and foreign direct investment into the same framework, to investigate their impact on carbon emissions.
87 citations
TL;DR: Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.
Abstract: A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.
81 citations
TL;DR: Zhang et al. as discussed by the authors identified zombie enterprises in the energy industry with a modified method used by Fukuda and Nakamura (2011) and used the survival analysis model to test the impact of environmental regulation on the market exit of zombie enterprises.
65 citations
TL;DR: A probability computation model is established by mathematical programming to derive the missing probabilities of PLPR and an iterative algorithm to improve the consistency is proposed to obtain the PLPR with satisfactory consistency.
65 citations
TL;DR: Experimental findings show the effectiveness of this approach for timely daily activity recognition from an incomplete stream of sensor events in terms of precision, recall, average saved time, and saved time proportion.
Abstract: Smart homes are designed to promote safe and comfortable living for inhabitants without any manual intervention. The performance of approaches for daily activity recognition is therefore crucial, but current real-time approaches have to wait until a daily activity ends before performing recognition. We present an approach for timely daily activity recognition from an incomplete stream of sensor events, by which the recognition process can start as soon as a daily activity begins. Activity features are generated from several headmost sensor events rather than from all sensor events that a daily activity activated. A public dataset was utilized to evaluate the presented method. Experimental findings show its effectiveness for timely daily activity recognition in terms of precision, recall, average saved time, and saved time proportion.
58 citations
TL;DR: Zhang et al. as mentioned in this paper investigated the combined nonlinear effects of economic growth and urbanization on CO 2 emissions using provincial panel data from China that spans the period of 1997-2016.
52 citations
TL;DR: In this paper, the authors analyzed the main influencing factors of Baoding's emissions, and then predicted its CO2 emissions peak and its time under different scenarios, and provided a reference and experience for other cities in developing strategies to achieve the carbon emission peak, which has important practical significance for China to achieve peak carbon emissions as soon as possible.
42 citations
TL;DR: This work derives families of explicit breather solutions of any order to the Kadomtsev–Petviashvili equation (KPI) and the Boussinesq equation using the Hirota bilinear method combined with the KP hierarchy reduction method.
41 citations
TL;DR: Results indicate that the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of im balanced learning approaches.
Abstract: Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria.
40 citations
TL;DR: In this paper, a robust bi-objective optimal control model was proposed for glycerol producing 1,3-propanediol in batch process with uncertain time-delay and kinetic parameters.
TL;DR: Based on a novel Lyapunov functional by taking the adaptive control law into account, sufficient conditions for asymptotically stable with mixed H∞ and passive performance are obtained in terms of a set of LMIs.
Abstract: In this paper, the problem of reliable mixed H∞ and passive control for a class of nonlinear networked control systems under the adaptive event-triggered scheme with actuator faults and randomly occurring nonlinear perturbations is studied. Firstly, a series of independent stochastic variables with certain probabilistic distribution are presented to formulate the phenomena of actuator faults. Then, different from the traditional event-triggered scheme, the threshold of adaptive event-triggered scheme is determined by an online adaptive control law instead of the preset constant value. Further, based on a novel Lyapunov functional by taking the adaptive control law into account, sufficient conditions for asymptotically stable with mixed H∞ and passive performance are obtained in terms of a set of LMIs. If these LMIs are feasible, the reliable mixed H∞ and passive control gain and the weight parameter of adaptive event-triggered scheme can be gained simultaneously. Finally, two numerical examples are provided to show the effectiveness of the developed technique.
TL;DR: In this article, the authors investigate whether, why, and how institutional transitions affect the role of financial performance in CSR reporting and find that financial performance buffers against external pressures brought by institutional transitions rather than only serving as a slack resource.
Abstract: While many extant studies focus on the relation between financial performance and corporate social responsibility (CSR) reporting, less attention has been given to the shifting role of financial performance in CSR reporting in a changing institutional environment. The objective of this study is to investigate whether, why, and how institutional transitions affect the role of financial performance in CSR reporting. Using samples of A‐share listed companies from 2008 to 2015, we separately examine the impacts of institutional transitions on firms' propensity to issue standalone CSR reports, the quality of voluntary CSR reports, and the quality of mandatory CSR reports. We find that financial performance buffers against external pressures brought by institutional transitions rather than only serving as a slack resource. By highlighting the buffer role of financial performance, our study provides deeper insights on the relation between financial performance and CSR reporting and contributes to extant institutional research on CSR reporting.
TL;DR: In this article, the authors examined the long-term behavior of HDPE geomembranes under field conditions using analytical and mechanical tests as well as the geoelectric leak location method.
TL;DR: This study analyzes shared bicycle use from the perspective of the theory of planned behavior, and proposes a model to investigate factors influencing shared bicycle usage in China.
Abstract: The worldwide rise of shared bicycle use has changed the way people travel. Here we analyze shared bicycle use from the perspective of the theory of planned behavior, and propose a model to investigate factors influencing shared bicycle usage in China. A total of 211 shared bicycle users selected from 28 provinces throughout China completed a self-reported survey. Structural equation modelling (SEM) was used to delineate the pathway from shared bicycle usage. The SEM model demonstrated that: (1) shared bicycle use intention was significantly associated with four variables, namely travel attitude(β = 0.491, t = 24.569), social norms(β = 0.149, t = 6.771), travel habits(β = 0.146, t = 7.226) and perceived behavioral control (β = 0.190, t = 11.110); (2) shared bicycle use behavior was significantly affected by shared bicycle use intention(β = 0.406, t = 15.936), and also by travel habits(β = 0.320, t = 11.921); (3) shared bicycle use behavior was also affected by demographic variables (gender, age) and situational factors (distance). The conclusions of this study provide useful data for operators of bicycle services and government policy makers.
TL;DR: This paper constructs the probabilistic dual hesitant fuzzy comparison matrix (PDHFCM) and proposes an integrated VIKOR and AHP method and uses the method to solve the AI strategy selection problem.
Abstract: Artificial intelligence (AI) is the most popular technology for searching the natural essence of human beings' intelligence. AI is influencing the paradigm of the enterprise management and product optimization. The development of AI in enterprise management provide an opportunity for all enterprises to construct automated business processes, improve the customer experience, and expand the product differentiation. Nowadays, the changeable world increases the level of difficulty to make an appropriate AI strategy for a company because of the information uncertainty and complexity. Probabilistic dual hesitant fuzzy set (PDHFS), which is a very effective tool to handle uncertain information, contains the hesitant fuzzy information and the corresponding probabilistic information. Standing in front of the more and more complex evaluation/selection problems, decision makers (DMs) could express their preference information more flexibly using the probabilistic hesitant fuzzy information. In this paper, we focus on the strategy selection problem in AI and solving it by a proposed integrated AHP and VIKOR method under probabilistic dual hesitant fuzzy information. First, we construct the probabilistic dual hesitant fuzzy comparison matrix (PDHFCM) and propose a specific transformation function for using AHP method under probabilistic dual hesitant fuzzy information. For completing the AHP, we redefine a consistency measure and propose an appropriate information-improved approach to obtain the consistent comparison matrix and the corresponding weight values simultaneously. In addition, we study the properties of PDHFS deeply and propose a new comparison method and a novel distance measure for PDHFS to distinguish the different probabilistic hesitant fuzzy information effectively. Then, we propose an integrated VIKOR and AHP method and use the method to solve the AI strategy selection problem. Finally, the availability and effectiveness of the proposed method are illustrated by a case on AI strategy selection.
TL;DR: Li et al. as mentioned in this paper examined the effects of vegetation greenness on albedo from 2000 to 2014 in China's grasslands, which have considerable intra-and inter-annual variations, using remote sensing-based albedos and two-band Enhanced Vegetation Index (EVI2) data.
Abstract: Changes in Earth’s albedo due to vegetation dynamics, snow cover, and land cover change have attracted much attention. However, the effects of vegetation dynamics on albedo have not been comprehensively documented according to its spatial (regional), temporal (within growing season), and spectral (visible, near-infrared, and shortwave) characteristics. This study examined the effects of vegetation greenness on albedo from 2000 to 2014 in China’s grasslands, which have considerable intra- and inter-annual variations, using remote sensing-based albedo and two-band Enhanced Vegetation Index (EVI2) data. Generally, we found an insignificant negative correlation between the shortwave (SW) albedo and EVI2 for grasslands in China. However, the visible (VIS) albedo was more sensitive to changes in vegetation greenness than near-infrared (NIR) albedo in China’s grasslands. The relationship between the NIR albedo and EVI2 was more complicated, especially in the Tibetan Plateau (TP), where the correlation was negative in the early growing season and positive in the late growing season; while the correlation between the NIR albedo and EVI2 was always negative in main part of Inner Mongolia (IM). The different albedo-EVI2 relationships in IM and TP may be related to differences in soil albedos. The higher sensitivity of the SW albedo to vegetation greenness change in IM, the stronger effect on land surface radiation budget. Our finding about vegetation-induced changes in albedo differ in space, time and spectral bands is expected to contribute to the improvement of land surface models.
TL;DR: In this article, the authors proposed that government support plays an important role in the Chinese economy and new energy industries, which involve innovation-driven sources and environmental protection, are also supported by the government.
Abstract: Government support plays an important role in the Chinese economy. New energy industries, which involve innovation-driven sources and environmental protection, are also supported by the government....
TL;DR: A new group decision making (GDM) model based on mathematical programming with incomplete hesitant fuzzy linguistic preference relations is established and a novel GDM model is constructed based on obtained multiplicative consistency LPRs in consideration of the group consensus reaching process.
TL;DR: A novel algorithm is proposed to determine the real-valued relative weights of criteria according to the given importance degrees of criteria, which are expressed by HFLTSs and this method is applied to the sustainable energy technology evaluation.
TL;DR: Li et al. as mentioned in this paper proposed the experimentally attainable 2D LiMX2 (M = Al, Ga, In; X = S, Se, Te) bilayers as ideal candidates to achieve this goal, taking advantage of the intrinsic electric field effect.
Abstract: Two-dimensional (2D) materials are highly promising for converting solar energy into valuable hydrogen fuels through photocatalytic water splitting. However, few single photocatalysts can intrinsically satisfy the requirements of high solar energy conversion efficiency and photocatalytic redox reactions. Using first-principles calculations, we propose the experimentally attainable 2D LiMX2 (M = Al, Ga, In; X = S, Se, Te) bilayers as ideal candidates to achieve this goal, taking advantage of the intrinsic electric field effect. They have direct band gaps, higher carrier mobility and a wide light absorption range, which are beneficial to the photocatalytic performance. The intrinsic electric field not only promotes the spatial separation of photogenerated carriers, but also contributes to high solar-to-hydrogen (STH) efficiency with an upper limit of 20.1–36.6%. Similar results have also been found for LiMX2 multilayers. More interestingly, the strong photocatalytic redox ability of the LiGaS2 bilayer makes the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) proceed, driven solely by the photogenerated electrons and holes in pure water (pH = 7). These results indicate that the LiMX2 bilayers could be utilized for efficient photocatalytic overall water splitting in pure water without the need for sacrificial reagents or cocatalysts.
TL;DR: A deep convolutional neural network is used to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images to obtain clearer and complete fusion images.
Abstract: Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fusion rules to obtain the fused images. In this paper, a wavelet is used for multiscale decomposition of the source and fusion images to obtain high-frequency and low-frequency images. To obtain clearer and complete fusion images, this paper uses a deep convolutional neural network to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images. In this paper, high-frequency and low-frequency images are used to train two convolutional networks to encode the high-frequency and low-frequency images of the source and fusion images. The experimental results show that the method proposed in this paper can obtain a satisfactory fusion image, which is superior to that obtained by some advanced image fusion algorithms in terms of both visual and objective evaluations.
TL;DR: Zhang et al. as discussed by the authors studied the effects of FDI on energy consumption in Shandong Province from both qualitative and quantitative analysis, and provided policy suggestions for the government to implement the transformation of new and old kinetic energy.
TL;DR: A consensus process of LGDM is proposed, in which multi-granular probabilistic fuzzy linguistic preference relations (MGPFLPRs) are used to represent sub-group’s preferences information, and an automatic iteration consensus reaching algorithm is implemented.
Abstract: As the number of participants involves in decisions getting complex and the heterogeneity could be produced among decision makers, a large-scale group decision making (LGDM) method with consensus constructing need to be considered. In order to demonstrate the complex relationship and reduce heterogeneity among decision makers, a consensus process of LGDM is proposed in this paper, in which multi-granular probabilistic fuzzy linguistic preference relations (MGPFLPRs) are used to represent sub-group's preferences information. First, mathematical programming is proposed to deal with MGPFLPR based on expected multiplicative consistency and obtain the priority weight vector. Second, collective priority weights of alternative are obtained by fusing sub-group's priority weights of alternative based on the weighted averaging operator. Then, an automatic iteration consensus reaching algorithm is implemented for the purpose of reaching a consensus in LGDM with MGPFLPRs. Finally, an emergency decision problem is applied to demonstrate the effectiveness of the proposed method.
TL;DR: The triaxial experimental test results show that the mechanical properties of BFRT increase with the increases of fiber length and content, particle size, dry density and confining pressure, and the interfacial interaction between fibers and tailings particles is mainly affected by particle shape.
Abstract: As one of the largest artificial geotechnical structures on earth, the tailings dams are classified as one of the high-risk sources in China’s industry. How to improve the stability and safety of tailings dams remains a challenge for mine operators currently. In this paper, an innovative method is presented for improving the stability of tailings dams, in which the basalt fiber is used to reinforce tailings. The mechanical properties of tailings used for dam-construction have a great influence on the stability of tailings dam. In order to investigate the mechanical performance of basalt fiber-reinforced tailings (BFRT), a series of laboratory triaxial tests were conducted. The effects of five parameters (fiber length, fiber content, particle size, dry density and confining pressure) on the mechanical properties of BFRT were studied. The microstructure and the behavior of interfaces between basalt fibers and tailings particles were analyzed by using scanning electron microscopy (SEM). The triaxial experimental test results show that the mechanical properties of BFRT increase with the increases of fiber length and content, particle size, dry density and confining pressure. The SEM results indicate that the interfacial interaction between fibers and tailings particles is mainly affected by particle shape.
TL;DR: A hybrid time-series predictive neural network (HTPNN) that combines the effection of news and time series that captures the potential law of stock price fluctuation and has more advantages in running speed.
Abstract: Stock price volatility forecasting is a hot topic in time series prediction research, which plays an important role in reducing investment risk. However, the trend of stock price not only depends on its historical trend, but also on its related social factors. This paper proposes a hybrid time-series predictive neural network (HTPNN) that combines the effection of news. The features of news headlines are expressed as distributed word vectors which are dimensionally reduced to optimize the efficiency of the model by sparse automatic encoders. Then, according to the timeliness of stocks, the daily K-line data is combined with the news. HTPNN captures the potential law of stock price fluctuation by learning the fusion feature of news and time series, which not only retains the effective information of news and stock data, but also eliminates the redundant information of the text. Compared with the state-of-the-art methods, our method combines more abundant stock characteristics and has more advantages in running speed. Besides, the accuracy is averagely improved by nearly 5%.
TL;DR: In this article, a three-layer adsorption model (TLAM) was proposed to investigate methoxy propanol gas sensing properties on a SnO2 (110) surface, where the first layer illustrates the decoration of metal Ag on SnO 2 (110).
Abstract: Methoxy propanol has been widely used in modern industry and consumer products. Inhalation or skin exposure to methoxy propanol for a long period would bring about safety challenges on human habitat and health. Ag decorated SnO2 mesoporous material has been synthesized and shown to exhibit high sensitivity and good selectivity to methoxy propanol among other interferential VOC gases. Density Functional Theory study were conducted to yield insight into the surface–adsorbate interactions and therefore the gas sensing improvement mechanism by presenting accurate energetic and electronic properties for the Ag/SnO2 system. Firstly, an electron transfer model on Ag and SnO2 grain interface was put forward to illustrate the methoxy propanol gas sensing mechanism. Then, a three-layer adsorption model (TLAM) was proposed to investigate methoxy propanol gas sensing properties on a SnO2 (110) surface. In the TLAM method, taking SnO2 (110) surface for the basis, layer 1 illustrates the decoration of metal Ag on SnO2 (110) surface. Layer 2 represents the adsorption of molecular oxygen on metal Ag decorated SnO2 (110) surface. Layer 3 indicates the adsorption of methoxy propanol, and for comparison, three other VOC gases (namely, ethanol, isopropanol and p-xylene) on Ag decorated SnO2 (110) surface with oxygen species pre-adsorbed consecutively. All the adsorption processes were calculated by means of Density Functional Theory method; the adsorption energy, net charge transfer, DOS, PDOS and also experimental data were utilized to investigate the methoxy propanol gas sensing mechanism on Ag decorated SnO2 (110) surface with oxygen species pre-adsorbed.
TL;DR: This paper considers enhancing the classification performance of CSP by making explicit use of information of the phase synchronization, and introduces an index, termed as rank-weighted phase lag index (rWPLI), which is introduced to qualify the intrinsic phase synchronization.
TL;DR: In this article, an analysis of the progress of organic magnetic materials and organic magnetoelectric complexes is provided, thus paving the way for the understanding of organic magnetism and magnetolectric coupling mechanisms.
Abstract: Over the past years, the development of magnetic materials has been intensively explored, both for fundamental research and technological applications. Particularly, several materials with large magnetoresistance effect have received significant interest. In this study, we provide an analysis of the progress of organic magnetic materials and organic magnetoelectric complexes, thus paving the way for the understanding of organic magnetism and magnetoelectric coupling mechanisms. In addition, this analysis provides us a critical guide for future organic magnetic material design and fabrication.