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This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) tourism e-commerce platforms (VRTEPs) can provide trial experience for tourists, which is easy to trigger irrational impulse buying. Based on cognitive emotion theory and S-O-R model, this study innovatively explores the impact of the multi cue-interaction stimulation (the interactivity of VR display and the empathy of text display) of VRTEPs on consumers' impulse buying intention through the mediating effect of new cognitive and emotional responses, namely, interactive pleasure (perceived usefulness and immersion). The results reveal that: (1) The interactivity of VR display stimulates interactive pleasure, then positively affects impulse buying intention. (2) The empathy of text display only stimulates perceived usefulness to positively affect impulse buying intention. This study also finds that the interactive comparison has a negative moderating effect on the relationship between immersion and impulse buying intention. These findings contribute to developing the new theory for effectively marketing using highly interactive new technology on VRTEPs. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of output feedback adaptive tracking control for a class of uncertain nonlinear time-delay systems (NTDS) with both uncertain output function and unknown control directions. By introducing a linear state transformation, the original system is transformed into a new system for which output feedback control design becomes feasible. Without the precise information on output function, an input-driven high-gain state observer is presented to estimate the unavailable states of the new system. Moreover, some radial basis function neural networks (RBFNNs) are employed to approximate the lumped unknown nonlinear functions during the procedure of control design. Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems. As a result, all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error can remain in a small neighbourhood of zero. Finally, two simulation examples are given to validate the effectiveness and applicability of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper investigates the output feedback adaptive finite-time quantized tracking problem for a class of uncertain nonstrict-feedback nonlinear systems with asymmetric actuator saturation, where signals including the measurable output and the control input are quantized by the sector-bounded quantizers. In order to overcome the technical difficulties arising from asymmetric actuator saturation and sector-bounded quantization errors, the combination of some novel techniques, the Gaussian error function-based smooth model, and fuzzy logic system approximation-based method are utilized. Moreover, an observer involving the quantized output signal and the saturated input signal is designed to reconstruct the unknown states. Then an adaptive output feedback tracking control strategy is developed for this class of systems, so that the system output follows satisfactorily a desired reference signal in finite time while all signals in the closed-loop systems are semi-global practical finite-time stable. Finally, two illustrative simulation examples are provided to validate the effectiveness and applicability of the proposed design. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive tracking problem for a class of uncertain switched stochastic strict-feedback nonlinear systems with both sector-bounded quantization input and unknown time-varying delays under arbitrary switching. Major design difficulties for such systems, in which all the time-varying delays absolutely depend on the subsystem number, come from finding a common Lyapunov function (CLF) for all subsystems and dealing with the sector-bounded quantization error. The Lyapunov–Razumikhin method is employed to construct a suitable CLF for all subsystems and relax the restrictions on the time-varying delays. Moreover, some new techniques are presented to overcome the difficulty from the sector-bounded quantization error. With the application of backstepping technique, variable separation technique and fuzzy logic system approximation, a common adaptive tracking control scheme containing only one adaptive parameter is proposed for this class of systems. The derived quantized control scheme ensures that the tracking error remains in a small neighborhood of the origin via the appropriate adjustment of design parameters while all the signals in closed loop system are bounded in probability. Finally, two simulation examples are provided to illustrate the feasibility and applicability of the proposed control design. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of adaptive output feedback quantised tracking control for a class of stochastic nonstrict-feedback nonlinear systems with asymmetric input saturation. Especially, both input and output signals are quantised by two sector-bounded quantisers. In order to solve the technical difficulties originating from asymmetric saturation nonlinearities and sector-bounded quantisation errors, some special technique, approximation-based methods and Gaussian error function-based continuous differentiable model are exploited. Meanwhile, an observer including the quantised input and output signals is designed to estimate the states. Then, a novel output feedback adaptive quantised control scheme is proposed to ensure that all signals in the closed-loop system are 4-moment (2-moment) semi-globally uniformly ultimately bounded while the output signal follows a desired reference signal. Finally, the effectiveness and applicability of the design methodology is illustrated with two simulation examples. [ABSTRACT FROM AUTHOR]

The adaptive quantised tracking problem is addressed for a class of switched stochastic strict‐feedback non‐linear systems with asymmetrical input saturation in this study. With the aid of a Gaussian error function‐based continuous differentiable switching model and some special techniques, the technical difficulties from dealing with the switching asymmetric saturation non‐linearities and the sector‐bounded quantisation errors are overcome. Then, by combining the common Lyapunov function method, backstepping technique and neural network approximation‐based approach, a simple common adaptive tracking control scheme involving one adaptive parameter only is presented for such systems under arbitrary switching. The given quantised control scheme guarantees that all signals of the closed‐loop system are semi‐globally bounded in probability while the tracking error can converge to a small neighbourhood of the origin. Finally, simulation studies are provided to illustrate the effectiveness and applicability of the proposed control design. [ABSTRACT FROM AUTHOR]

The problem of output feedback adaptive tracking control is studied for a class of stochastic nonlinear time-delay systems in which the measured output and input signals are quantized by two sector-bounded quantizers respectively. An observer including the quantized input and output signals is designed to estimate the unknown system states, and the unknown system functions with less restrictions are dealt with by using the neural network’s (NN) approximation. By combining the backstepping technique and the Lyapunov–Krasovskii method, an observer-based adaptive neural quantized tracking control scheme is presented for this class of systems. The stability analysis indicates that the tracking error can converge to a small neighborhood of the origin while all closed-loop signals are 4-moment (or 2-moment) semi-globally uniformly ultimately bounded (SGUUB). Finally, two illustrative examples are provided to demonstrate the feasibility and effectiveness of the proposed design methodology. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with an adaptive tracking problem for a more general class of switched nonstrict‐feedback nonlinear time‐delay systems in the presence of quantized input. The system structure in a nonstrict‐feedback form, the discrete and distributed time‐varying delays, the sector‐bounded quantized input, and arbitrary switching behavior are involved in the considered systems. In particular, to overcome the difficulties from the distributed time‐varying delays and the sector‐bounded quantized input, the mean‐value theorem for integrals and some special techniques are exploited respectively. Moreover, by combining the Lyapunov‐Razumikhin method, dynamic surface control technique, fuzzy logic systems approximation, and variable separation technique, a quadratic common Lyapunov function is easily built for all subsystems and a common adaptive quantized control scheme containing only 1 adaptive parameter is proposed. It is shown that the tracking error converges to an adjustable neighborhood of the origin whereas all signals of the closed‐loop systems are semiglobally uniformly ultimately bounded. Finally, 2 simulation examples are provided to verify the feasibility and effectiveness of the proposed design methodology. [ABSTRACT FROM AUTHOR]

The problem of adaptive tracking control is studied for a class of switched non‐strict‐feedback non‐linear systems with unknown control direction and asymmetric saturation actuators under arbitrary switchings. The main technical difficulty comes from developing a common adaptive control scheme in the presence of unknown control direction and unknown asymmetric saturation non‐linearities. A linear state transformation is exploited to deal with the unknown control coefficients and a Gaussian error function‐based smooth model is used to approximate the asymmetric saturation non‐linearity. Based on an input‐driven observer, a common adaptive output‐feedback control strategy containing only one adaptive parameter is developed for such systems via a novel combination of common Lyapunov function method, backstepping technique, variable separation approach and neural network approximation. Under the proposed control scheme, the tracking error converges to an adjustable neighbourhood of the origin. Finally, two illustrative examples are included to verify the effectiveness of the proposed control design. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of adaptive tracking control for a class of switched strict-feedback nonlinear systems with unknown time-varying delays and asymmetric saturation actuators under arbitrary switching. Especially, the considered time-varying delays absolutely depend on the subsystem number. The main technical difficulties lie in finding an appropriate common Lyapunov function (CLF) for all subsystems and designing a common adaptive control scheme in the presence of unknown time-varying delays and asymmetric saturation nonlinearities. Based on a novel combination of Lyapunov–Razumikhin method, dynamic surface control (DSC) technique, variable separation approach and neural network (NN) approximation, a simple quadratical CLF is constructed and a common adaptive control scheme involving only one adaptive parameter is developed. The proposed controller guarantees that all signals of closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error converges to an adjustable neighborhood of the origin. Finally, the effectiveness of the design methodology is illustrated with two simulation examples. [ABSTRACT FROM AUTHOR]

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company’s net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies. [ABSTRACT FROM AUTHOR]

The problem of quickly reducing operating errors during recovery from a feedback disruption is considered. The objective is to design controllers that reduce operating errors as quickly as possible, once feedback has been restored. It is shown that robust optimal feedback controllers that achieve this objective do exist. Furthermore, it is shown that the performance of optimal controllers can be approximated as closely as desired by controllers that generate bang–bang input signals for the controlled system. Controllers that generate bang–bang signals are relatively easy to derive and implement, since bang–bang signals are characterised by a finite list of scalars – their switching times. [ABSTRACT FROM AUTHOR]

This paper presents the feedback control and output feedback control for the stabilisation of switched Boolean network. A necessary condition for the existence of a state feedback controller for the stabilisation of switched Boolean networks under arbitrary switching signal is derived first, and constructive procedures for feedback control and output feedback control design are provided. An example is introduced to show the effectiveness of this paper. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of adaptive tracking control for a class of uncertain pure-feedback nonlinear time-delay systems with unknown asymmetric saturation actuators. The considered problem is challenging due to the existence of unknown distributed time-varying delays and asymmetric saturation actuator. In particular, the difficulties from distributed time-varying delays and unknown asymmetric saturation nonlinearity are processed by using the mean value theorem for integrals and a Gaussian error function-based continuous differentiable model, respectively. Then, based on a novel combination of mean value theorem, Razumikhin functional method, variable separation technique and Neural Network (NN) parameterization, an adaptive neural controller which involves only one parameter to be updated is presented for such systems via Dynamic Surface Control (DSC) technique. Moreover, the DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design. All signals in the closed-loop system remain semi-globally uniformly ultimately bounded (SGUUB), and the tacking error converges to a small neighborhood of the origin. Finally, simulation results are given to verify the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design. [ABSTRACT FROM AUTHOR]

This paper first focuses on the problem of adaptive output feedback stabilization for a more general class of stochastic nonlinear time-delay systems with unknown control directions. By using a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then a novel adaptive neural network (NN) output feedback control strategy, which only contains one adaptive parameter, is developed for such systems by combining the input-driven filter design, the backstepping technique, the NN's parameterization, the Nussbaum gain function method and the Lyapunov-Krasovskii approach. The proposed control design guarantees that all signals in the closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly bounded. Finally, two simulation examples are given to demonstrate the effectiveness and the applicability of the proposed control design. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the problem of output-feedback adaptive stabilization for a class of stochastic nonlinear time-delay systems with unknown control directions. First, based on a linear state transformation, the unknown control coefficients are lumped together and the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of an observer, an adaptive neural network (NN) output-feedback control scheme is presented for such systems by using dynamic surface control (DSC) technique and Lyapunov–Krasovskii method. The designed controller ensures that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed control design. [Copyright &y& Elsevier]

SUMMARY This paper addresses the problem of adaptive neural control for a class of uncertain stochastic pure-feedback nonlinear systems with time-varying delays. Major technical difficulties for this class of systems lie in: (1) the unknown control direction embedded in the unknown control gain function; and (2) the unknown system functions with unknown time-varying delays. Based on a novel combination of the Razumikhin-Nussbaum lemma, the backstepping technique and the NN parameterization, an adaptive neural control scheme, which contains only one adaptive parameter is presented for this class of systems. All closed-loop signals are shown to be 4-Moment semi-globally uniformly ultimately bounded in a compact set, and the tracking error converges to a small neighborhood of the origin. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is presented for this class of systems, based on a combination of the Razumikhin functional approach, the backstepping technique and the neural network (NN) parameterization. The proposed adaptive controller guarantee that all the error variables are 4-Moment semi-globally uniformly ultimately bounded in a compact set while the system output converges to a small neighborhood of the reference signal. Two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. [Copyright &y& Elsevier]

Selecting thepotential brand spokesperson on social network (SN), who has the huge growth potential and will own the largest number of fans in the future, can providehigher returns at lessrisks. In this study, smart link prediction algorithm (SLPA) is proposed to predict the evolvements of SNs and the brand spokesperson with the great future potential is selected based on the prediction results of SN evolution. In SLPA, mean roughness classification uniformity (MRCU) is developed to select the high efficient link prediction algorithm (LPA) for node pairs to be predicted from the local similarity based and quasi-local similarity based LPAs. MRCU uses the rough set theory and granular computing to describe the similarity of LPAs, consequently the LPA selected with MRCU can share as much similarity as possible with the other base LPAs. Furthermore, SLPA adopts the branch and bound method to smartly cluster node pairs by adaptively selecting optimal LPA for given node pairs and excluding the ones with the least possibility of linking, and consequently the most reliable results of node pairs with the highest linkable possibility are acquired. The experimental results on three SN datasets confirm the validity of SLPA in selecting band spokesperson. [ABSTRACT FROM AUTHOR]

The maintenance and strategy operation after patent licensing can bring great market competitiveness and benefits to enterprises. But the large time span from patent licensing to market application makes it challenging to discern the benefits of patent competition strategy. Besides, artificial intelligence (AI) is an emerging industry without ready-to-use experience to formulate patent competition strategy, and particularly current researches have not designed patent competition strategy from the micro patent management perspective of AI enterprises to solve the uncertainty caused by the lag of market application relative to patent licensing. This research builds an expert group discriminant system based on the system dynamics method to address this problem. It integrates expert tacit knowledge to determine the fuzzy variable value and the fuzzy relationship. The patent competition strategy subsystem in national dimension, industry dimension and enterprise dimension for capturing the market from the perspective of enterprise technology competitiveness are constructed. By combining the three subsystems, the enterprise patent competition strategy system dynamics model with evolution analysis is established. Finally, taking typical Chinese AI enterprise iFLYTEK as an example, the multi-scenario simulation is carried out and the results under four different scenarios can provide effective decision supports for managers to formulate reasonable patent competition strategy and gain high market share. This research sheds light on modeling and evolution analysis of the patent competition strategy which comprehensively and systematically considers the operation mechanism of patent management and contributes to dealing with the uncertainty and ambiguity in the system dynamics model effectively. [ABSTRACT FROM AUTHOR]

In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research. [ABSTRACT FROM AUTHOR]

This work addresses the adaptive output feedback quantized control for a class of switched stochastic nonstrict‐feedback nonlinear time‐delay systems with input and output quantization and actuator saturation under arbitrary switching. A novel state observer including the quantized output signal and the saturated input signal is designed to estimate the unknown system states. Then, the command‐filtered backstepping method is introduced to avoid the "explosion of complexity" in the adaptive backstepping recursive design. Moreover, the design challenges from input and output quantization errors and actuator saturation are overcome by combining the auxiliary variable approach and some special techniques. Instead of the traditional quartic Lyapunov–Krasovskii functional, the common quadratic Lyapunov–Krasovskii functional is constructed to develop the adaptive quantized tracking control scheme for such systems. By means of the proposed common controller independent of switching signal, all closed‐loop signals are mean‐square semi‐globally uniformly ultimately bounded, while the tracking error will remain in a small neighborhood of the origin. Finally, stability analysis and simulation results are provided to show the effectiveness and performance of the proposed design methodology. [ABSTRACT FROM AUTHOR]

One of the main problems encountered by social networks is the cold start problem. The term "cold start problem" refers to the difficulty in predicting new users' friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using "betweenness centrality," and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the "products rapidly attracting users" in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles. [ABSTRACT FROM AUTHOR]

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm. [ABSTRACT FROM AUTHOR]

Detecting Sulphur hexafluoride (SF6) gas leaks using infrared video images is the latest technology. However, the current algorithm for autonomous real-time detection is imprecise and incapable of performing the real-time monitoring task effectively. In response to the aforementioned issues, we present a temporal–spatial feature fusion network (TSFF-Net) specifically designed for video real-time detection of SF6 gas leaks. Compared with the existing SF6 gas leak detection model based on deep learning, the biggest feature of TSFF-Net is the introduction of foreground pixel images with SF6 gas movement characteristics, which greatly enhances the model's ability to detect SF6 gas leaks. Simultaneously, by designing a new attention feature fusion module, introducing the Neck module of motion features, and the Backbone module to enhance small target feature extraction, we achieve high-precision SF6 gas leak detection in various complex scenarios, such as sparse leaks, complex backgrounds, camera shake, etc. The mean average precision (mAP) of TSFF-Net in the validation set was 56.3%, which is 13.2% higher than that of Yolov8s. In addition, the model proposed in this paper has parameters that are only 2.6 M, making it suitable for deployment on mobile or embedded devices. The code and test results are at https://github.com/1jianyao/TSFF-Net/tree/main. • A feature-fusion network is presented for the detection of SF6 gas leaks. • The two-branch features are fused by the attention mechanism. • The Neck module to enhance moving target detection is devised. • The Backbone module to enhance tiny object detection is proposed. [ABSTRACT FROM AUTHOR]

In the social business platform, continuous marketing to consumers can fully explore the consumer purchasing potential. However, since consumers can be influenced by their social friends, their tastes often change, which resulting in the cold start problem of familiar users (CSPFU), and the traditional product recommendation methods are difficult to achieve satisfactory results because they focus on identifying the preferable new products instead of boring familiar products. Therefore, a consumer multi-stage compensation product evaluation model (CMCPEM) based on the multidimensional correlation of products and customers to identify the products that consumers may feel tired is proposed. Specifically, the multidimensional correlation indexes are firstly proposed to depict the preferences of the consumer for the target product to be identified, other consumers who have social contagion and structural equivalence relationships with the consumer and other consumers of homogeneous products. After the direct linear, non-linear and indirect fusion of these multidimensional correlation indexes, the compensation indexes (CIs) are proposed to comprehensively describe the first stage of product evaluation process of consumers. Then, J test in the non-nested model is used to screen out the non-nested CIs that consumers focus on. Finally, in the third stage, the final decision result is given by comprehensively considering CIs that consumers focus on and the indexes that represent consumers' favorite. Experiment results on YELP data confirm the effectiveness of CMCPEM in successfully launching the continuous marketing campaign. [ABSTRACT FROM AUTHOR]

Mining user preferences from online reviews to understand the representative preferences of different customer groups plays a critical role in product development and improvement, especially in personalized product design. Previous research on mining user preferences usually assumes that all consumers' preferences are homogenous and does not take differences in consumers' personalities into account. Besides, traditional online review deep mining methods are too broad to focus on precise and detailed mining of customer preferences. To fill the gaps in existing research, our study develops a template matching deep mining method to segment customers and narrow the mining scope of customer group preference, and then proposes a product family lean improvement model. Firstly, K-means and structural change model are applied to cluster customers reliably based on the similarity of user preferences. Secondly, in order to decrease down mining scope of customer group preference, the Improved Deep Structured Semantic Model is designed to determine sentimental polarity sentimental polarity of different groups by matching the standard sentimental polarity review templates and online reviews. Finally, a KANO mapping model is developed to decide the user preferences for product attributes in each customer group according to their sentimental polarity and further summarize the common preferences and personalized preferences of various groups according to the Preference Commonality Measurement Function. Accordingly, product family lean improvement strategies are proposed to provide product developers with improvement directions. An empirical study is carried out on laptop data on JD.COM to verify the validity of the proposed model and product family lean improvement suggestions are put forward. [ABSTRACT FROM AUTHOR]

Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands the users' AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter. [ABSTRACT FROM AUTHOR]

The ultra-short-term forecasting of wind speed is of great significance to the stable power supply of the power system. Current wind speed forecasting methods aim to improve forecasting precision while disregarding model training speed and model deployment complexity. This research proposes a lightweight hybrid model named SLF-EMD-MGM-NS for wind speed forecasting. EMD-MGM is designed as the network's fundamental structure for reducing the hybrid model's training time and ensuring the hybrid model has high forecasting precision. The study presents the switching loss function (SLF) mechanism. When the quantile is 0.5, an MSE-based loss function is employed for training all subsequences. When the quantile is 0.5 and 0.95, first use the wind speed fluctuation threshold to select primary subsequence, and then use the Log-Cosh-based loss function for training primary subsequences. The SLF mechanism can increase point prediction precision and interval prediction boundary stability. Moreover, a novel subsequence superposition (NS) mechanism is proposed for getting high confidence level and narrow-width interval prediction results. The NS mechanism superimposes the interval prediction results of the fluctuation subsequence with the point prediction results of the model to generate the final interval prediction results. According to the experimental results, the SLF-EMD-MGM-NS model has a high confidence level, acceptable prediction results, a narrow-width interval prediction result, and a significantly shorter training time than the other hybrid models. • Proposed EMD-MGM as the base network architecture to do wind speed forecasting. • Three point regression loss functions were extended to wind speed interval regression. • A new mechanism named SLF was proposed to do quantile regression. • A new mechanism named NS was proposed to superpose subsequences' forecasting results. [ABSTRACT FROM AUTHOR]

3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry. [ABSTRACT FROM AUTHOR]