The analysis of optimal output efficiency with traditional data envelopment analysis (DEA) cannot be directly applied to a production strategy of promoting the sustainable development of Chinese banks, because both inputs and outputs increase during economic development. In this paper, a new DEA is presented with two innovations. The First innovation is that, the research data are extended to time-series data instead of cross-section data as before. The second is, the simultaneous increase in input and output from the input/output decrease/increase production is developed. As an application of the BCC (Banker, Charnes, Cooper) Bi-objective Generalized DEA (Bi-GDEA) model, the simulation strategies were well implemented in Chinese banks in 2017 using data from 2004 to 2015. The three inputs and one output of the banking sector have been adjusted to make each year’s decision making unit (DMU) efficient in the framework of 2004 to 2017, and both inputs and outputs are monotone increased. It may provide an alternative strategy for the effectiveness and sustainability of Chinese banks development.

Wireless mesh networks with directional antennas are expected to be a new promising technology and an economic approach for providing wireless broadband services in rural areas. In these networks, the effective use of multiple non-overlapping channels can significantly enhance the network capacity by allowing more concurrent transmissions. However, the performances of wireless mesh networks are severely degraded by interference between links with overlapping channels in nearby areas. In this paper, we address the issue of joint utilization and balance in channel assignment for rural wireless mesh networks. We design a new channel assignment framework with the goal of optimizing the channel resource utilization across the entire network while taking balanced allocation into account. This balanced channel assignment allows for the cost-effective reuse of channels without a consequent loss of quality in the rural wireless mesh networks. We also evaluate the proposed framework on some Cartesian product graphs that are popular interconnection network topologies. The framework guarantees that these topologies require a small number of channels for balanced allocation.

This study uses the hedonic price model to examine the determinants of house prices. It employs kernel density to estimate the spatial weight matrix and conducts spatial econometrics and instrumental variables quantile regression analysis. Taking a new building in Shanghai city as an example, this micro-perspective study shows that hedonic prices of houses are derived from consumer hedonic preferences and their changes in terms of inertia and spatial ripple effects. However, there are large differences in the drivers of the same hedonic attribute across quantile degrees. With house prices gradually decreasing from the city center to the surrounding areas, the ripple effect is significant in determining residents’ preferred consumption characteristics and presents several inverted U-shaped and inverted U-shaped relationships. Residents’ preference for housing consumption is mainly reflected in the average area of the house, property fee, location of the administrative area, and so on. Regional real estate price changes are mainly reflected in hedonic house prices, and real consumer demand for housing is mainly derived from hedonic preference. Therefore, government regulation and control of house prices should consider different regions and different consumer groups simultaneously.

Social perception influences product and brand evaluations. Consumers are especially susceptible to reference price effects when they make purchase decisions for a certain product. Meanwhile, the advertising and pricing are the determinable factors that have impact on consumers’ reference price which also are fundamental marketing strategies. Therefore, how to dynamically set advertising and pricing to maximize firms’ profits are essential tasks. We investigate a duopoly market in a mature product category where two firms compete through time using advertising and pricing as their dominated marketing tools. The firms make the advertising and pricing decisions to maximize their own profits in the planning period. The main results of this paper include the following. (i) The optimal retail price and advertising effort are positively correlated to the initial reference price and basic market size. (ii) When the two firms’ initial basic market size are different, the retail price difference is positive correlated to the initial basic market size difference, so is the advertising effort difference. This conclusion will result to that the strong firm is getting stronger and stronger, however, the weak firm is getting weaker and weaker, this is the situation which happen in the e-commerce market, that is winners take all. (iii) The value of the initial reference price can also determines the reference price effect on the consumer demand rate, that is, when the initial reference price is relatively low, the reference price will have a negative effect on the consumers’ demand of both firms in the whole planning period; when the initial reference price is relatively high, the reference price will have a positive effect on the consumers’ demand of both firms in the whole planning period. Whereas, a moderate initial reference price may lead to different effects on demand.

Industry clusters provide not only economic benefits, but also promote technological innovation through networking within a cluster. In this study, we analyze the mechanism of explicit and tacit knowledge diffusion in a cluster and how the network structure and the strength of ties influence the process of explicit and tacit knowledge diffusion. By focusing on four representative real-world networks – scale-free, small world, regular, and random – and the strength of ties between firms, the knowledge diffusion performance of entire organizations in a cluster is examined by the simulation method. We find that the network structure of clusters and the strength of ties are important for the knowledge diffusion performance in clusters. Among the four networks, the scale-free network shows the best knowledge diffusion performance, irrespective of the proportion of strong and weak ties present. In addition, the network with a greater number of strong ties leads to the explicit and tacit knowledge diffusion performance.

Stock returns play an important role in the empirical study of asset pricing, and are often applied in portfolio allocation and performance evaluation. The effect of macroeconomic and financial variables on stock returns is a hot topic and many studies have utilized these variables in time series models to improve the forecasts of stock returns. This study imposes macroeconomic and financial variables as weighting factors on kernel density and establishes a new prediction model – the time-varying factor weighted density model. We apply this model to monthly price data of the Chinese stock index and employ the rolling window strategy for out-of-sample forecasting. The result shows that this method improves both statistical and economic measures of out-of-sample forecasting performance.

Establishing unified industrial technical standards for a single enterprise in a highly global integrated market is becoming increasingly difficult. In recent years, leading enterprises have often built technical standards alliances around a key core technology to develop industrial standards cooperatively in order to learn from each other and optimize their resource allocation. Although such technical standards alliances result in huge gains to their members, their internal and external risks threaten both the alliances and their members. As compared to other forms of strategic alliances, the risk of such an alliance has fuzzy characteristics and is difficult to fully and accurately identify. This paper uses a fuzzy pattern-recognition method to evaluate and summarize the risks of technical standards alliances. A fuzzy analytic hierarchy process (AHP) evaluation and back propagation (BP) logic fuzzy neural network methods are used to construct a risk-evaluation model of technical standards alliances while considering an alliance around new-energy automobiles in Zhejiang as an empirical example. The two evaluation models are then contrastively analyzed, and cross validation of the evaluation results is performed in order to provide theoretical guidance and support for the application of two fuzzy evaluation models in practice.

Modeling the effect of uncertainty shock usually employs VAR method. The approach however often leads to the results unstable with different structural equations. Especially it is modeled without a microscopic basis which often implies wrong policy advice. A new method to model the effect of interest rate uncertainty is proposed in this paper that overcomes this limitation. A stochastic volatility model is embedded into a dynamic stochastic general equilibrium framework to study the influence of interest rate uncertainty on the residents’ consumption. Using the third-order perturbation method to identify the impact of interest rate uncertainty on consumption. It is found by simulation that with the interest rate uncertainty increased, the consumption of residents in the current period has obviously decreased due to the preventive saving mechanism. Variance analysis shows that interest rate uncertainty shocks can explain 8% share of consumption volatility. The empirical results are robust when changing the parameter values and the prior distribution of the parameters. The conclusion shows the government should strengthen to guide the public reasonable expectations, to avoid the negative impact of interest rate uncertainty.

This paper explores the role of entrepreneurial marketing (EM) and environmental uncertainty (EU) in driving new ventures’ performance (NVP). Using data from a survey of 883 small ventures based in Anhui province, China, we find that EM is an important drive of NVP. Only 4 EM dimensions (customer value creation, pro-activeness, innovations, and opportunity-focus) have positive effects on growth performance (GP), and only 5 EM dimensions (customer value creation, pro-activeness, innovations, opportunity-focus, and resource leveraging) have positive effects on profit performance (PP) as well. On the other hand, we found that environmental uncertainty (EU) has a partly moderating role between EM and GP, EU only negatively moderates the relationship between pro-activeness and GP, and EU positively moderates the relationship between opportunity-focus, risk-taking and GP. In addition, EU does not have a moderating role between EM and PP.

We propose a novel stereo vision method based on a fast template matching strategy to improve localization accuracy and efficiency of substation fittings. First, considering the salient features of the substation fittings that can be recognized easily, the method searches for features that are similar to the ones in the matching template related to the sub-image of the substation fittings from the global image. When the substation fittings are confirmed, the method repeatedly searches for the one of screw holes in the local region of the substation fittings. It then computes the centering coordinates of the template in the source images until the screw holes are matched. The experimental results show that the proposed template matching method increases the accuracy and efficiency of the substation fitting localization from the global to local search area. Correspondingly, the accuracy and efficiency of stereo vision localization of substation fittings is improved.

A high accuracy rate of street objects detection is significant in realizing intelligent vehicles. Algorithms based on convolution neural network (CNN) currently exhibit reasonable performance in general object detection. For example SSD and YOLO can detect a wide variety of objects in 2D images in real time; however the performance is not sufficient for street objects detection, especially in complex urban street environments. In this study, instead of proposing and training a new CNN model, we use transfer learning methods to enable our specific model to learn from a generic CNN model to achieve good performance. The transfer learning methods include fine-tuning the pretrained CNN model with a self-made dataset, and adjusting the CNN model structure. We analyze the transfer learning results based on fine-tuning SSD with self-made datasets. The experimental results based on the transfer learning method show that the proposed method is effective.

With the development of computer technology, data mining, artificial intelligence, and image-processing technology have been applied to medical diagnosis. Image classification is one of the main technologies of medical image processing, which can be used to determine whether a patient suffers from breast cancer according to x-ray images of the breast. To achieve reliable classification of breast images, an image classification method combined with a fusion Gaussian–Hermite moments feature and improved nonlinear support vector machine (SVM) classifier is proposed. The proposed fusion Gaussian–Hermite moments features can improve the robustness and distinguish the ability of features by constructing Gaussian–Hermite invariant moments according to invariant moment theory and constructing a Gaussian–Hermite Fisher moment according to Fisher’s idea. The proposed improved nonlinear SVM classifier can improve the efficiency and accuracy of the classifier through eigen decomposition and sample learning. Experimental results demonstrate that the proposed method has a high accuracy rate for breast x-ray image classification.

In order to provide a unified platform for fuzzy inference and fuzzy rule learning with noise-corrupted data, a method is proposed for reducing noise in learning data on the basis of a fuzzy inference method called α-GEMINAS (α-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points). It is expected to prevent fuzzy rules from overfitting to noise in learning data, especially when there is less learning data available for fuzzy rule optimization. The proposed method is named α-GEMI-ES (α-GEMINAS-based local-evolution toward slight linearity for global smoothness) in this paper. α-GEMI-ES iteratively performs α-GEMINAS and reduces the noise in each iteration. This paper mathematically proves that α-GEMI-ES effectively reduces the noise. The noise-reduction process is decisive and thus relies less on trial-and-error-based progress. The noise is reduced by a large amount in the early iterations and the amount of its reduction is decelerated in the later iterations where the deviation in the learning data is suppressed to a great extent. This property makes it easy to determine the termination conditions for the iterative process. Simulation results demonstrate that α-GEMI-ES properly reduces noise as the mathematical proof suggests. The above-mentioned properties indicate that α-GEMI-ES is feasible in practice for the unified platform.

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

Computer vision techniques have been widely applied in Intelligent Transportation Systems (ITSs) to automatically detect abnormal events and trigger alarms. In the last few years, many abnormal traffic events, such as illegal parking, abandoned objects, speeding, and overloading, have occurred on the highway, threatening traffic safety. In order to distinguish illegal parking and abandoned object events, we propose an effective method to classify these types of abnormal objects. First, abnormal areas are detected by feature point extraction and matching. The transformation relation, between the world and image coordinate systems, is then established by camera calibration. Next, different-height inverse projection planes (IPPs) are built to obtain the inverse projection maps (IPMs). Finally, the 3D information describing the abnormal objects is estimated and used to distinguish illegally parked vehicles and abandoned objects. Experimental results from traffic image sequences show that this method is effective in distinguishing illegal parking and abandoned objects, while its low computational cost satisfies the real-time requirements; furthermore, it can be used in vehicle classification.

In the classical context of map relative localization, map-matching (MM) is typically defined as the task of finding a rigid transformation (i.e., 3DOF rotation/translation on the 2D moving plane) that aligns two maps, the query and reference maps built by mobile robots. This definition is valid in loop-rich trajectories that enable a mapper robot to close many loops, for which precise maps can be assumed. The same cannot be said about the newly emerging vision only autonomous navigation systems, which typically operate in loop-less trajectories that have no loop (e.g., straight paths). In this paper, we address this limitation by merging the two maps. Our study is motivated by the observation that even when there is no loop in either the query or reference map, many loops can often be obtained in the merged map. We add two new aspects to MM: (1) map retrieval with compact and discriminative binary features powered by deep convolutional neural network (DCNN), which efficiently generates a small number of good initial alignment hypotheses; and (2) map merge, which jointly deforms the two maps to minimize differences in shape between them. A preemption scheme is introduced to avoid excessive evaluation of useless MM hypotheses. For experimental investigation, we created a novel collection of uncertain loop-less maps by utilizing the recently published North Campus Long-Term (NCLT) dataset and its ground-truth GPS data. The results obtained using these map collections confirm that our approach improves on previous MM approaches.

Traditional filter tuning methods mainly entail tuning by electromagnetic simulation technology, which treats the tuned filter as an ideal model. However, the structure of the actual filter is relatively complex, and filter tuning becomes affected by the loss of resonant cavity, phase loading and high-order mode. In this study, to solve these problems, the tuning process was divided into four stages. First, the passband and suppression of the filter could be tuned to a reasonable range by using the phase attribute of the reflection characteristics. Secondly, the tuning model parameters (coupling matrix) were extracted by curve fitting and the improved Cauchy method. Thirdly, the tuning model of the actual filter was established by a complex neural network. Finally, the mapping relationship between the surrogate model and the actual tuning model was established by the improved space mapping algorithm. By optimizing the parameters of surrogate model, we quickly obtained the optimal position of the screws. The results of the tuning experiment with the eighth coaxial cavity filter revealed that the method had high accuracy and fast convergence speed.

The objective of this research is to investigate the features of an inter-firm trade structure model. We have developed this model through agent-based simulation. In order to adapt to rapid changes in the business market, each firm must deal with changes in requirements and the required volume changes. Thus, the model has two combined functions. The first function allows a firm to freely trade with other firms as a decentralized trade structure. The second function allows a firm to mediate other firms as a centralized trade structure. The combined model has worked well in various test cases so far. However, other trading environments have not been fully investigated. Therefore, this study focuses on the detailed environmental conditions: (1) the number of similar firms and (2) the number of procurement items, and deals with intensive experiments and detailed analysis of the two functions of the model in the agent-based simulations. The simulation results suggest that the two functions in the combined model work particularly well under the conditions of a trading environment in which there exist both (1) a large number of similar business firms and (2) firms with a certain number of procurement items, compared to the decentralized trade structure, where each firm’s size is neither particularly large nor small.

This study identifies and analyzes the influence factors for learning outcomes at a university with a Bayesian network. It is based on a fact-finding survey on university student life and learning. Suitable constraints and a score metric for the Bayesian network learning are determined via cross-validation, and the learning outcome variables are categorized into subsets according to six abilities: cooperativeness, expressiveness, foreign language, collecting and organizing information, logical thinking, and sociability. The learned network suggests that two to seven factors influence each ability. In addition, it is confirmed that the probability distributions of all most of the identified factors shift to high agreement/experience levels, as self-knowledge levels for the acquired abilities increase, i.e., positive effects exist for most factors for each identified ability.

Representing the positive, possible, and boundary regions of clusters, rough set-based C-means clustering methods, such as generalized rough C-means (GRCM) and rough set C-means (RSCM), are promising for analyzing vague cluster shapes and realizing reliable classification. In this study, we consider rough set-based clustering approaches that utilize probabilistic memberships as variants of GRCM and RSCM, including π generalized rough C-means (πGRCM), π rough set C-means (πRSCM), and rough membership C-means (RMCM). πGRCM and πRSCM assign equal probabilities of cluster belonging according to Laplace’s principle of indifference, whereas RMCM assigns the probabilities according to rough memberships, which represent conditional probabilities based on the object’s neighborhood derived from a binary relation. In addition, we discuss the theoretical validity of our RMCM approach and compare it with other methods considered in this study. Furthermore, we conducted numerical experiments for evaluating the classification performances of the abovementioned methods. Based on our experimental results, the methods were found to be effective.

In this article, contrary to popular studies on human motion learning, we focus on addressing the problem of humanoid robot motions directly. Performances of different kernel functions with principal components analysis (PCA) in Gaussian process dynamical models (GPDM) are investigated to build efficient humanoid robot motion models. A novel kernel-PCA-GPDM method is proposed for building different types of humanoid robot motion models. Compared with the standard-PCA-GPDM and auto-encoder-GPDM methods, our proposed method is more efficient in humanoid robot motion modeling. In this work, three types of NAO robot motion models are studied: walk-model, lateral-walk model, and wave-hand model, where motion data are collected from an Aldebaran NAO robot using magnetic rotary encoder sensors. Using kernel-PCA-GPDM method, the motion data are first projected from the high 23-dimension observation space to a 3-dimension low latent space. Then, three types of humanoid robot motion models are learned in the 3D latent space. Compared with other kernel-PCA-GPDM or auto-encoder-GPDM methods, our proposed novel kernel-PCA-GPDM method performs efficiently in motion learning. Finally, we realize humanoid robot motion representation to verify the motion models that we build. The experimental results show that our proposed kernel-PCA-GPDM method builds efficient and smooth motion models.

With the rapid development of the Internet and travel market, the availability of online short-term rental rooms has emerged, making good use of limited room resources. In current research, only structured data of room characteristics, such as the location of the room, are considered in the price of the room. As unstructured textual data, online user’s reviews containing the emotional responses of users influence the price of online short-term rental rooms. In this research, user reviews are considered in a hedonic price regression model to improve the performance of regression. First, structured room characteristics are input to build a traditional hedonic price regression model. Then, fusion of emotion scores transformed from unstructured user reviews is input to build a fusion hedonic price regression model. Finally, the traditional model and the fusion model are compared statistically. Experimental results indicate that the fusion of user reviews can improve the performance of hedonic price regression model.

The development of robot partners for supporting human life has been growing for many years. One main feature that should be considered in developing such robots is the conversation system. In this study, a conversation system called iPhonoid-C is introduced. The iPhonoid-C is a robot partner based on a smart device. A conversation is a form of communication in which two or more people exchange words and information. Therefore, one important part of judging the effectiveness of the interaction must be to evaluate if the appropriate amount of information is provided by the robot. In this research, we focused on a time-dependent utterance system to adjust the amount of conversation based on Grice’s maxim of quantity. By utilizing Grice’s theory, it is possible to tailor the robot’s communication by selecting the Grice value to correspond to the human’s condition. Using this method, the robot partner can control the amount of information it communicates to adapt to the human’s situation based on Grice’s maxim of quantity. An experimental result with the robot partner is presented to validate the proposed time-dependent conversation system.