Series Snake robots possess strong adaptability for unstructured environments, but their trajectory tracking control is hindered by nonlinear dynamics and model uncertainties. This article proposes an optimal tracking control strategy based on an actor–critic reinforcement learning framework. The method integrates line-of-sight guidance with serpentine gait generation, while a neural network identification system approximates the solution of the Hamilton–Jacobi–Bellman equation for unknown dynamics. Actor and critic networks are employed to update control policies and cost functions online, reducing dependence on precise models. Rigorous theoretical analysis proves that position and velocity errors achieve semi-global uniform ultimate boundedness. Both simulations and prototype experiments were conducted based on a servo-driven yaw-pitch linkage alternating series snake robot. The results verify that the proposed method can achieve accurate trajectory tracking, rapid convergence, and stable joint control, demonstrating its effectiveness and superiority compared to existing methods.

Emergencies often lead to multi-modal and unbalanced distribution of urban rail transit passenger flows. Aiming at this problem, a emergency control method of multi-modal passenger flow is proposed under the premise of comprehensively considering the operation cost and passenger travel efficiency of urban rail transit(URTS).The main work includes: 1) From the perspective of train scheduling, designing and developing a contingency strategy for multi-modal passenger flow by combining the train operation plan based on full-length and short-turn routing, station passenger flow restrictions, and dynamic train departure intervals; 2) From the perspective of train operation and passenger travel synergy, the emergency control model of passenger flow is established with the objective of minimizing the total train operation time and average passenger waiting time; 3) A multi-agent deep reinforcement learning(MDRL) method with a new action selection, reward and double loop mechanism-ARDQMIX is proposed to realize emergency autonomous perception and control of passenger flow in urban rail transit. The simulation results show that the emergency control method of multi-modal proposed in this study has a good utility for improving passenger travel efficiency and reducing operation cost.Note to Practitioners—Urban rail transit system(URTS) is punctual, fast, safe and large capacity, and has gradually become the preferred means of transportation for passengers. However, due to the rapid growth of travel demand, the URTS in the morning and evening peak hours traffic problems are particularly serious, will produce a multi-modal passenger travel demand, such as can not be quickly alleviated, the traffic problem will be caught in a vicious circle, leading to the urban rail transit system service capacity decline, and even lead to the potential safety problems. Aiming at this problem, a emergency control method of multi-modal passenger flow is proposed under the premise of comprehensively considering the operation cost and passenger travel efficiency of URTS.

Urban Rail Transit (URT) system is often disturbed by various types of security incidents during operation. Therefore, effective utilization of passenger in-transit information to dynamically assess the URT system’s abilities to maintain and resume normal operation under disturbances (operational resilience) is the key and prerequisite for URT safety management and emergency response. Based on typical operational disruption scenario, this paper study the multimodal data-driven URT operational resilience and its evaluation methods. The main contributions of this research includes: capturing key information about passengers in the travel process based on historical data, and quantifying the system’s service capabilities; proposing the concept of operational resilience oriented towards URT emergency management based on service level; employing the resilience curve to analyze the evolutionary patterns of URT service levels during safety events, and emphasizing the relativity and dynamism of operational resilience; constructing a two-stage “disruption-recovery” URT operational resilience evaluation model and taking Hangzhou URT as an example to verify the effectiveness of the model through experiments. The results provide a good reference for URT safety status assessment and emergency response.Note to Practitioners—With the expansion of urban rail transit scale, the passenger flow it undertakes is also increasing. Frequent security incidents can seriously disrupt the operation of urban rail transit, causing significant economic losses and even threatening the safety of passengers. If the ability of urban rail transit to resist safety incidents and restore normal operation can be understood in advance, it will help operation managers make reasonable judgments and emergency decisions. Therefore, this paper proposed an evaluation method for operational resilience with the service level of urban rail transit as the key.

The demand, technologies, and market all put forward the request for platformization of the vehicle logistics industry. In China, a few logistics companies have plans but only one has initially built a platform for vehicle logistics. This platform has also attracted a considerable number of service providers to join, which has led to competition between service providers and the platform. To promote the platformization of the vehicle logistics industry and attract more customers to use platform-based services, how to leverage the incentive effect of subsidies is a topic of concern for the government. Motivated by the above, this article discusses whether the exogenous subsidies (provided to customers for platform service or providers service) can accelerate the process of the vehicle logistics industry. We explore the role of subsidies by developing a model (a hotelling line) that describes the differentiated competition between the platform and service providers. Our study found that subsidies do not always work as expected. The effectiveness of the subsidy depends on service advantages and customer preferences. Subsidies may even be counterproductive if customers prefer service providers or if platform services lack advantages. This is a reminder for both the government and industry that to accelerate the platformization of the vehicle logistics industry, it is necessary to combine reality and not blindly subsidize.

This article proposes a robotic fish formation control scheme that is based on finite-time terminal sliding mode to achieve coordinated tracking of multiple target paths under external disturbances and internal parameter perturbations. The method explores the lateral sliding mechanism of each body in the surge and sway directions by constructing a directional compensation guidance strategy that is based on a finite-time disturbance observer, thus enabling the precise coordinated movement of multiple robotic fish. Furthermore, this article acknowledges that the highly coupled dynamics of the robotic fish are susceptible to external environmental influences and modeling accuracy. Hence, a rapid global terminal sliding mode fuzzy controller that considers tangential displacement is introduced, and fuzzy adaptive methods are utilized to fit complex uncertainties. This approach mitigates the chattering problems commonly associated with traditional sliding mode control and enhances the error convergence speed and accuracy of the robotic fish formation system.

Chronic disease patients often require revisits for long-term care. Online medical services shift revisits to online, which can improve the access to chronic care and reduce the burden on offline medical services. However, whether Internet healthcare can truly match the medical supply and demand, one of the critical issues is the efficient advance scheduling of the integrated online and offline systems. This study investigates the advance scheduling problem for the first visit and revisit patients in chronic care. The uncertainty of revisit status (i.e., online or offline) and heterogeneity of online and offline revisits (i.e., revisit interval, continuity of care violation penalty) are considered. A stochastic mixed-integer programming model is formulated for assigning patients to a specific physician on a specific day over the course of a finite planning period. The aim is to minimize the expected sum of three cost components related to offline and online services: overtime and idle time, continuity of care violation penalty, and fixed setup. This study proposes a modified progressive hedging algorithm and applies a sequential decision-making framework to obtain rolling time advance schedules. Results of the numerical analysis demonstrate the effectiveness of our algorithm compared to both the published state-of-the-art Lagrangian decomposition embedded with surrogate subgradient method and the commercial solver Gurobi. The insight obtained from the experiments is that a capacity allocation scheme with all physicians assigned with both offline and online capacities would be a good choice for considerable cost savings.Note to Practitioners—Internet healthcare is becoming increasingly popular. Operation and management issues have arisen in the integrated online and offline appointment systems. A sequential decision-making method embedded with a stochastic programming model and a modified PHA is proposed to help decision-makers generate the first visit and revisit advance schedules for chronic care. The performance of this approach and the system is thoroughly verified. Results show that the developed decision technique can lessen the operational cost generated by scheduling and realize the goal of continuity of care. This study offers a useful tool to help with intelligent patient advance scheduling in an integrated management system of online and offline chronic care.

Time granularity is a key parameter necessary for short-time passenger flow prediction of urban rail transit (URT); however, no universal method is available for its setting. This study investigates the time granularity setting principle for short-term passenger flow prediction in URT. First, a method to measure the autocorrelation of passenger flow time series is constructed, focusing on the comparison of time granularities. Second, based on the functional relationship between the first-order autocorrelation coefficients of the passenger flow time series under different time granularities, the time granularity setup principle is obtained for different passenger flow characteristics. Finally, the reasonableness and universality of the time granularity setting principle are verified by analyzing the passenger flow characteristics and autocorrelation magnitude of the actual inbound and origin-destination (OD) passenger flow data under different stations and dates at different time granularities.

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.

The emergency plan of the urban rail transit (URT) system is a guiding document for dealing with emergencies and formulating emergency plans. However, the emergency plan text described in natural language has some problems, such as poor visibility and enforceability. Effective help is difficult to provide for the rapid implementation of emergency response. Therefore, obtaining key emergency task information from the emergency response plan and visualizing the emergency disposal workflow are the main challenges. In this article, we propose a method of extracting emergency elements (EELs) from emergency plans and constructing a business process model. First, a nested entity extraction model incorporating adversarial training is proposed to extract EELs from the complex sentences in the emergency plan text. Second, the EELs are combined into emergency task units, and then the relations between emergency task units are identified to form the emergency task sequence flow, which is stored in matrix form. Finally, the emergency disposal workflow model is generated based on the emergency task sequence flow and the BPMN modeling method. Taking the actual emergency plan text as an example, the process from the extraction of EELs to the construction of the disposal workflow model is demonstrated. Experimental results prove that this method has advantages in comprehensively extracting EELs, visualizing the emergency disposal workflow, and improving the enforceability of emergency plans.

This work provides a graph network solution for pilot brain fatigue state inference based on electroencephalography (EEG) fatigue indicators. Two graph methods are built as follows. The first one uses a single EEG signal sample as a node, and fatigue detection as a node classification task in a graph network. The developed graph network is then utilized to extract the correlation among different samples to achieve multisample joint decision making. The second method uses a single EEG signal sample as a graph structure, and EEG fatigue prediction as a graph classification task. Electrode position correlation is used to construct a graph. The feature fusion of adjacent electrodes is obtained through the connection relationship among nodes in a graph structure to improve network learning accuracy. In addition, a Bayesian optimization method is proposed to model the randomness of attention weights, and a Bayesian graph attention network is built. This work constructs a based-graph deep learning structures to achieve a pilot fatigue detection model with high accuracy, good generalization, and strong adaptability. Experimental results demonstrate the effectiveness of the proposed model.

Three-dimensional spiral gait enables a snake robot to climb over obstacles, cross caves, and adapt to complex environments. This article reports an antidisturbance path-following control method for a snake robot with a spiral gait. This method reduces the deviation of the robot's position in following the ideal path by estimating the time-varying parameters, the external disturbances, and the viscous friction coefficients. The estimations are used to compensate for the control inputs of the system, which can improve the adaptability of the robot to the environment. Then, the attitude and position errors can rapidly converge to the origin. An appropriate Lyapunov function is adopted to explore the stability of following errors. Experimental results show that the proposed method can accelerate the convergence rate of errors, reduce the fluctuation peak, and improve the following stability of snake robots.

Flight Trajectory Prediction (TP) is an essential task in Air Traffic Control (ATC). Currently, the TP task is usually achieved by regression approaches, which concatenates several scalar attributes of the observation into a low-dimensional vector as the inputs. However, it is difficult to accurately model aircraft motion patterns using low-dimensional features in complex and time-varying ATC environments. To improve the performance of the TP task, in this paper, a novel framework, called FlightBERT, is proposed based on Binary Encoding (BE) representation, which enables us to tackle the TP task as a multi binary classification problem. Specifically, the scalar attributes of the flight trajectory are encoded into binary codes and transformed into a high-dimensional representation by the attribute embedding module. Considering the prior knowledge among flight attributes, an Attribute Correlation Attention (ACoAtt) block is designed to explicitly capture the correlations among the specific attributes. A stacked Transformer block is applied to serve as the backbone network, which is followed by the predictor to generate the outputs. Considering the nature of flight trajectory, a hybrid constrained loss, i.e., combining the mean square error loss with the binary cross-entropy loss, is innovatively designed to optimize the proposed framework. The proposed method is validated on a large-scale dataset, which is collected from the real-world ATC environment. The experimental results demonstrate that the proposed method outperforms other baselines by quantitative and qualitative evaluations.

To eliminate the influence of nonlinear state terms in the highly-coupled unmanned aerial vehicle (UAV) model and improve the aircraft’s ability to suppress wind field interferences, this work presents a path-following scheme for UAVs. This method uses the radial basis neural network (RBNN) to develop an adaptive approximation law for the gyroscopic effect function to balance for the influence of system uncertainty and nonlinear state terms on UAV modeling and reduce the dependence of the UAV’s roll and pitch control orders on attitude velocity information. In addition, the adaptive update laws of the disturbance predictions are designed to compensate for the control input and repress the chattering and deviation of the drone. The stability of the proposed controller was proven by using the Lyapunov theorem. Simulations and experiments have shown that the controller can perform faster convergence speed and higher following accuracy of the flight position and attitude errors.

In the context of establishing emission control areas (ECAs) in many ports to meet the challenges posed by air pollution, the use of drone-carrying sniffers to perform emission monitoring missions has become a new monitoring mode for ECAs. The operational management problem of drones in ECAs, namely, drone scheduling problem (DSP), is eliciting the attention of researchers. To consider the influence of vessel traffic on the demand for drones, this study proposes a rental-based drone operation model. In the model, the number of drones used depends on the load of monitoring missions. Maximizing the cumulative monitoring reward and minimizing the use number of drones within the minimum monitoring rate constraint are used as optimization objectives to maximize the cost return of the rental-based operation model. The rental-based drone operation model is modeled as a multi-objective DSP (MDSP). Furthermore, we horizontally compare the characteristics of MDSP with those of many classical models in the field of operations research. Afterward, we reveal the similarities and differences between MDSP and previous models. We find that MDSP has the non-first-in-first-out property, whereas most of the advanced models have the first-in-first-out property, which leads to the failure of the developed efficient algorithms in solving MDSP. Therefore, this study innovatively designs four feasible multi-objective optimization methods for MDSP. Numerical experiments are conducted to evaluate the performance of the four methods in solving MDSP with different scales. In terms of theoretical implications, experimental results prove that the proposed methods for solving MDSP are feasible and effective. In terms of practical implications, the proposed rental-based vessel monitoring operation model shows great potential for practical engineering.

This work reports an adaptive path-following controller for a multijoint snake robot (MSR) to improve the adaptability of the robot to the environment. The new strategy estimates the time-varying parameters of the system and the external interference to adjust the motion state of the robot in real time. Estimations are used to compensate for the joint torque of an MSR, thus reducing the fluctuation peak of path-following errors. In addition, this work designs an anti-sideslip line-of-sight (LOS) guidance strategy to avoid the deviation of the direction angle. The method can improve the tracking accuracy of an MSR, and the position errors enable the system to achieve uniformly ultimate boundedness (UUB). The angle errors converge to the origin to achieve stability. Experimental results demonstrate that the novel method can accurately estimate the time-dependent parameters, sideslip, and interference, raise the convergent speed of errors, and reduce the fluctuation peak.

How to effectively organize drones to monitor pollutants from vessels is an important operational problem in port management. It is defined as the drone scheduling problem (DSP). The effectiveness of precise algorithms and heuristic algorithms in solving DSP has been reported in previous studies. In previous studies, the speed of the vessel was assumed to be constant. However, since the influence of sea waves and vessel power, such an assumption is difficult to satisfy in actual scenarios. The actual position of the vessel may deviate from the position information obtained through prior calculations. As the cumulative position deviation increases, it is possible to make the original feasible monitoring scheme infeasible. It is necessary to consider the emission monitoring dispatching of drones under vessel speed fluctuation in actual monitoring activities of the vessel. To deal with the problem, a dynamic dispatching strategy based on reinforcement learning (RL) is proposed. Considering the vessel speed fluctuation, the monitoring window is divided into multiple sub-time windows. The route information of the vessel in each sub-time window is updated according to the vessel speed fluctuations to reduce the accumulation of deviations between the prior position and the actual position. Then, a lightweight RL strategy is adopted to quickly (re)organize the monitoring scheme in each sub-time window. Numerical experiments illustrate the above division-conquer approach could effectively reduce the possibility of drone monitoring failure caused by vessel speed fluctuations. Also, the superiority of the RL-based dispatching strategy is illustrated by comparing it with multiple dispatching schemes.

Multiple-View Stereo (MVS) aims to reconstruct the dense 3D representations of scenes. MVS has potential applications in the fields of autonomous driving (unstructured environment construction) and robotic navigation (visual-inertial navigation). To mitigate the error of depth estimation in low-textured or occluded regions, this work proposes a two-stage multi-view stereo network for fast and accurate depth estimation. The improvements of this work over the state of the art are as follows: 1) Sparse costs are constructed to jointly predict the initial depth map and surface normal by cost regularization, which proves that the surface normals can be estimated in this way with low memory consumption. 2) A new edge refinement block is developed to refine the coarse surface normal to obtain a fine-grained surface normal map. 3) Instead of using the general variance-based metric to equally aggregate cost, a new content-adaptive cost aggregation mechanism based on the similarity of the neighboring surface normal is designed for reliable cost aggregation. To the best of our knowledge, the proposed work is the first trainable network that leverages surface normal as guidance to capture neighboring pixel-visibility, which is an effective supplement to existing depth/normal estimation frameworks. Experimental results indicate that our method can not only achieve accurate depth estimation for scene perception but also make no concession to the real-time performance and limited memory bottleblock. Multiple-view stereo (MVS) aims to reconstruct the dense 3D representations of scenes. It is widely used in the fields of industrial measurement, autonomous driving, and robotic navigation. To mitigate the error of depth estimation in challenging scenarios, this work proposes a two-stage multi-view stereo network for fast and accurate depth estimation. Our method is the first trainable network that leverages surface normal as pixel-visibility guidance to aggregate reliable cost, which could achieve accurate depth estimation and provide the perception ability for the robot. The proposed method has great potential in the fields of 3D reconstruction, industrial measurement, and robotic navigation to estimate real-time and accurate depth with limited memory consumption.

This work designs an adversarial Bayesian deep network to solve the cognitive detection of pilot fatigue. Batch normalization and data enhancement are adopted in the posterior inference of the proposed model parameters to effectively improve the generalization of neural networks. The generator is used to enhance the brain power map generated from three cognitive indicators and improve the accuracy of fatigue state recognition. This work also adds adversarial noise in the vicinity of each brain electrode to form an adversarial image, which further reveals the correlation between the cognitive state of brain and the location of brain regions. Compared with other deep models and parameter optimization methods, our model achieves better detection accuracy.

In this paper, a novel bacterial foraging algorithm (BFA)-Gaussian support vector classifier machine (GSVCM) model was proposed to improve the fatigue classification accuracy of electromyography (EMG) signals. This optimization mechanism involves the kernel parameter setting in the GSVCM training procedure, which significantly influences the classification accuracy. Experiments were conducted based on the EMG signal to differentiate the normal and fatigue status. In the proposed method, the EMG signals were decomposed into intrinsic mode functions by ensemble empirical mode decomposition (EEMD) before the mean instantaneous frequency could be obtained by Hilbert transform (HT). Finally, the fatigue statistical features can be extracted from fast Fourier transform and EEMD-HT. The application of this model to the fatigue status recognition of EMG signal indicated that further significant enhancement of the classification accuracy can be achieved by the proposed BFA-GSVCM classification system. The diagnostic method is effective and feasible.

This study examined the relationships among perceived editorial responsiveness, perceived journal quality, and review time of submissions for authors in mainland China. Online review data generated by authors who have experienced the submission process in 10 Chinese academic journals were collected. The results of Spearman correlation analysis show that Chinese authors' perceived responsiveness of an editorial office is positively correlated with perceived quality of the journal, and the total review time does not affect perceptions of the quality of a journal and its editorial responsiveness.

From the perspectives of psychology, marketing and information technology, this study developed a theoretical framework describing the purchasing-intention model in C2C e-commerce of China. The model demonstrated that trust, perceived risk, perceived benefit and their antecedents would have an influence on costumers' intention of purchase. Then this study tested the proposed model using Structural Equation Modeling on Internet consumer purchasing behavior data collected via web-based surveys. The results of the study show that Internet consumers' trust, perceived benefit and perceived risk have strong impacts on their intention of purchase. Reputation of sellers, website security, familiarity with websites and group purchase have strong effects on consumers' trust. However, only seller accessibility has a significant impact on perceived risk. Moreover, group purchase has a strong impact on perceived benefit. The results and implications of this research are discussed.

A fast coreset minimum enclosing ball kernel algorithm was proposed. First, it transfers the kernel methods to a center-constrained minimum enclosing ball problem, and subsequently it trains the kernel methods using the proposed MEB algorithm, and the primal variables of the kernel methods are recovered via KKT conditions. Then, detailed theoretical analysis and rigid proofs of our new algorithm are given. After that, experiments are investigated via using several typical classification datasets from UCI machine learning benchmark datasets. Moreover, performances compared with standard support vector machines are seriously considered. It is concluded that our proposed algorithm owns comparable even superior performances yet with rather fast converging speed in the experiments studied in this paper. Finally, comments about the existing problems and future development directions are discussed.

Object-oriented technology, emerged in response to the growing needs for developing and maintaining complex software system, has aroused a great deal of interests in both academia and industry. After more than a decade, there is still no clear result describing the extent to which the technology is used. A recent survey found that the adoption rate of object-oriented technology in systems analysis and design is much lower than expected. This study applies the theory of diffusion of innovations to investigate the extent to which the technology is used and the reasons as to why it is being adopted or not adopted. Based on the survey findings, an instrument is proposed for organizations to assess whether they are ready to adopt this new paradigm for their systems development. The purpose of this study is to help management make informed decisions on the adoption of object-oriented systems development methodologies in their organizations.

The wide establishment of hotel websites has recently drawn the attention of many tourism and hospitality researchers to investigate the factors that contribute to a successful hotel website, and the ways of measuring the performance of hotel websites. The published articles in the hospitality and tourism literature, however, have largely overlooked the decision making approach which involved the development of weighing and rating scales from website users. To fill in such a void, this research makes an attempt to develop a weighing model for contents of hotel websites. The model was built on the basis of input from hotel website users who were international visitors to Hong Kong. Empirical findings indicated that Reservations Information and Website Management were the dimensions with the largest and smallest weights; whereas Transportation and Special Request Forms were the attributes with the largest and smallest overall weights. The value of a weight thus directly reflects its perceived importance.