The redirected walking (RDW) method for multi-user collaboration requires maintaining the relative position between users in a virtual environment (VE) and physical environment (PE). A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration. When a user approaches and interacts with a target user in the VE, the user is expected to approach and interact with the target user in the corresponding PE as well. Existing methods of multi-user RDW mainly focus on obstacle avoidance, which does not account for the relative positional relationship between the users in both VE and PE. To enhance the user experience and facilitate potential interaction, this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking (DA-RDW) in a shared PE where the target user and other users are moving. This algorithm adopts improved artificial potential fields, where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles. For the best alignment, this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction. Moreover, this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment. To balance obstacle avoidance and alignment, this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector. The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments. The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.

In recent years, multi-user collaboration has emerged as a trend in virtual reality. However, the existing redirected walking methods for multi-user VR scenarios usually are devoted to the obstacle avoidance, neglecting the users’ relative positions in both virtual environment(VE) and physical environments (PE), as well as the natural visual relationships. In order to enhance the collaborative experience, this paper presents a novel Alignment-based Redirected Walking for Multi-User VR scenarios, Align-RDW. Our algorithm will dynamically selects an optimal moving target for the current user’s alignment. Our approach integrates concepts such as user state loss and dynamic guiding points, applying auxiliary forces to enhance user alignment performance. Simultaneously, we utilize the calculated optimal deviation angle to adjust the user’s orientation. We evaluated the algorithm through simulations and Live-User experiments. The results demonstrate that our strategy achieves a longer reset distance, as well as smaller alignment distance errors and angle errors than others.

Accurate delineation and characterization of tree branch structures are vital for assessing the status of trees, forecasting the risks of diseases, and formulating conservation strategies. Current segmentation methods struggle to effectively handle the intricacies and diversity present in branch structures, particularly branches that are slender or obscured by dense leaves. To address these issues, this paper introduces the Occlusion-perception and Dense-dilation Network (ODNet), an innovative network designed to enhance tree branch segmentation and reduce occlusion impacts. Firstly, the Dense Dilated Module within ODNet enhances adaptability to diverse tree scales, addressing species variations and complex branch structures. Secondly, drawing insights from tree growth patterns that usually grow upwards and outwards, we designed Orthogonal Perception Module (OPM) . OPM offers a comprehensive perspective of tree branch structures, which can mitigate the prediction discontinuities caused by leaves occlusion. The quantitative analysis and visual comparisons indicate that ODNet achieves better results than existing methods.

A ReID-based multi-object tracking (MOT) network with adjacent frame enhancement is proposed, which is constructed with center-based detection network. To enrich the ReID features, our method treats the adjacent frame as data augmentation of the current frame, implicitly uses the network to achieve data alignment for each object at different frames, and then uses the aligned adjacent features to enhance the ReID. In the training phase, the method adds a location prediction branch to guide the network to learn the feature correspondence of the same object in two frames. In the inference phase, our method uses historical tracking to construct a heatmap of interest of the adjacent frame to inform the network of the locations of all objects in adjacent frames, then the network autonomously mines the features of each object in these two frames. Our approach performs better in MOT challenges than the existing multi-object tracking network.

Redirected walking (RDW) algorithms subtly manipulates users’ movements in virtual environments through three gains, distorting their trajectories in the physical space, thus allowing them to explore infinite virtual environments within limited physical spaces. However, collisions with physical boundaries or obstacles in physical spaces are inevitable. At this point, explicit redirected techniques, such as reset, are needed to pause users’ roaming experience and redirect them to open areas. Currently, most reset strategies wait until a collision occurs before resetting the user, while these reset algorithms can achieve certain effectiveness in simple physical environments. However, in complex physical environments, users may walk into narrow areas before collisions occur. Therefore, no matter which direction they turn, further collisions are likely to happen over short distances. To enhance obstacle avoidance capability while adapting to various complex physical layouts, this paper adopts the concept of pre-reset and presents a novel method of intelligent pre-reset IPR for redirected walking through reinforcement learning, which determines whether an early reset should be performed and the optimal reset direction based on the current physical environment, as well as the user’s position and walking trajectory. Through simulation and human experiments compared with other reset algorithms in several environments, we demonstrate the superiority of our approach, achieving higher average walking distances between resets while adapting to various nuances of reset techniques. At the same time, IPR is applicable to various RDW algorithms and environments of different complexities.

Multi-user redirected walking (RDW) is widely used in large-scale virtual scenes because it allows more users to move synchronously in both virtual and physical environments. To ensure the freedom of virtual roaming, which can be used in various situations, some redirected algorithms have been dedicated to non-forward movements, such as vertical movement and jumping. However, the existing RDW methods still mainly focus on forward steps, ignoring sideward and backward steps, which are also common and necessary in virtual reality. RDW algorithms for non-forward steps can enrich the movement direction of users' virtual roaming and improve the realism of VR roaming. In addition, the non-forward motions have a larger curvature gain, which can be used to better reduce resets in RDW. Therefore, this paper presents a new method of multi-user redirected walking for supporting non-forward steps (FREE-RDW), which adds the options of sideward and backward steps to extend the VR locomotion. Our method adopts a user collision avoidance strategy based on optimal reciprocal collision avoidance (ORCA) and optimizes it into a linear programming problem to obtain the optimal velocity for users. Furthermore, our method uses APF to expose the user to repulsive forces from other users and walls, thus further reducing potential collisions and improving the utilization of physical space. The experiments show that our method performs well in virtual scenes with forward and non-forward steps. In addition, our method can significantly reduce the number of resets compared with reactive RDW algorithms such as DDB-RDW and APF-RDW in multi-user forward-step virtual scenes.

Abstract Corner‐based detectors usually generate a large number of false detection boxes because of insufficient attention to the detection area. Recent corner‐based detectors can achieve good performance, but the training equipment requirements have greatly increased. For example, due to the dense network structure and the large input image size, CenterNet requires expensive equipment for network training (e.g. Tesla V100). Its performance will be greatly reduced when a more mainstream and cheaper device is used for fine‐tuning. The high equipment requirements make it difficult for most researchers to follow up these studies. In this work, CenternessNet, a detector that adds additional box‐edge length constraints to CenterNet is proposed, thereby allowing the network to be trained on more general devices and obtain a better performance. It simply introduces the box as a constraint into the corner‐based network. In this way, the method improves the ability to aggregate corners during training and enhances the model's ability to discriminate the corners of objects in the same category to some extent. The method achieves a better performance than other corner‐based detection networks trained on similar low‐memory devices.

With more attention being paid to large scale virtual environments, the demand for more users to collaborate in the same physical space is growing rapidly. Due to the complex collision problem caused by the limitation of physical space, the multi-user redirected walking methods are devoted to improving the ability of multi-user navigation in large-scale virtual environments by reducing the disturbance of resets. Because the existing multi-user redirected walking methods do not consider the density of users in the physical space, there are more boundary conflicts in the real walking for the multi-user virtual environment. In order to decrease the boundary conflicts, this paper presents a novel method of dynamic density-based redirected walking towards multi-user virtual environments. This method dynamically adjusts the user distribution to a state with high center density and low boundary density through the density force, which is generated by the density difference between standard density and actual density. In our method, the users in high-density areas are guided by a repulsive force away from the central area while the users in low-density areas are guided by the gravitational forces towards the central area. Our method can select a double-density optimal gravitational point as the turning target, so all users can move to the area of minimum density to make better use of the whole physical space. Our method also adopts the artificial potential field (APF) forces to prevent user collisions caused by usergathering. The users are guided to move in the direction of the resultant force vector of density force, gravitational force and APF force. In addition, this paper introduces a matching resetting method to further adjust the density distribution while dealing with user conflicts. The results of experiments show that our method successfully reduces the potential conflicts about 30% on average compared with the existing reactive multi-user redirection algorithms. Especially as the number of users increases, our method can avoid more boundary conflicts by using the adjustment of density.

In virtual environments (VEs), users can explore a large virtual scene through the viewpoint operation of a head-mounted display (HMD) and movement gains combined with redirected walking technology. The existing redirection methods and viewpoint operations are effective in the horizontal direction; however, they cannot help participants experience immersion in the vertical direction. To improve the immersion of upslope walking, this study presents a virtual climbing system based on passive haptics. This virtual climbing system uses the tactile feedback provided by sponges, a commonly used flexible material, to simulate the tactile sense of a user's soles. In addition, the visual stimulus of the HMD, the tactile feedback of the flexible material, and the operation of the user's walking in a VE combined with redirection technology are all adopted to enhance the user's perception in a VE. In the experiments, a physical space with a hard-flat floor and three types of sponges with thicknesses of 3, 5, and 8 cm were utilized. We recruited 40 volunteers to conduct these experiments, and the results showed that a thicker flexible material increases the difficulty for users to roam and walk within a certain range. The virtual climbing system can enhance users' perception of upslope walking in a VE.

Immersive Virtual Reality (VR) systems that combine Head Mounted Displays (HMDs) and a position tracking system support the multiple users or participants to collaborate in the same physical space for a large-scale virtual environment. Because the multiple users sharing the same physical space are in a dynamic state, the key technique of multi-user VR system is how to solve the problem of potential collisions among the users who are moving both virtually and physically. In order to better solve the collision problem caused by such dynamic changes, this work presents a new strategy of multi-user redirected walking using dynamic artificial potential fields, which generates repulsion to ‘push’ users away from obstacles and other users, and uses gravity to ‘attract’ users to an open or unobstructed space. In this method, the users not only get repulsive forces from walls, but also from other users and their future states that are called avatars. At the same time, the users will get gravitational force from the steering target. The target selection considers the size of open space, the distance between the steering target and the boundary of physical space, and the distance between the steering target and the center of the physical space. Therefore, the system can steer users to an open area in the physical space to further reduce collisions. To verify the validity of our method, we developed a software to statistically analyze the influence of different factors, such as the physical space size and the number of users. Data from experiments shows that our method reduces the potential user resets by about 20%.

Shifting from single-person experiences to multi-user interactions is an inevitable trend of virtual reality technology. Existing methods primarily address the problem of one- or two-user redirected walking and do not respond to additional challenges related to potential collisions among three or more users who are moving both virtually and physically. To apply redirected walking to multiple users who are immersed in virtual reality experiences, we present a novel algorithm of three-user redirected walking in shared physical spaces. In addition, we present the steps to apply three-user redirected walking to multiplayer VR scene, where the users are divided into different groups based on the users' motion states. Therefore, this strategy can be applied to each group to address the challenges of redirected walking when there are more than three users. The results show that sharing a space using our three-user redirected walking algorithm is completely feasible.

Nowadays, although there exists many ridesharing services and dynamic matching algorithms for passengers and drivers, there is no service or algorithm that can balance the benefit of passengers and drivers while taking their time and cost constraints into consideration. In this paper, we try to solve the dynamic ridesharing problem by considering all above factors for all the participants. To this end, we present URoad, an efficient algorithm for large-scale dynamic ridesharing service, where a new price cost model is carefully designed to make up for the shortcomings of existing algorithms, and in the meantime a corresponding efficient matching algorithm is proposed to satisfy both the time and cost constraints of passengers and drivers. Specifically, for a given passenger, URoad will find out the optimal driver who can satisfy all the constraints of the passenger and the driver with the minimum detour distance. We design a series of data structures to speed up URoad for large scale ridesharing service application, e.g., Time Index, Grid Index and Greedy Strategy. Through extensive experiments, we prove that URoad can find the optimal driver for a given passenger from more than one hundred thousand drivers within 0.5 second in average.

In order to solve the poor performance problem of massive rules reasoning, as well as the inconsistency problem of working memory in distributed rule matching, this article presents the formal definition of interference relations between rules, and proposes a novel approach to distributed rule matching and multiple firing based on MapReduce. This approach adopts the way of access request control to detect and exclude interference rules, then selects several rule instantiations to perform multiple firing and concurrent execution, thus reducing the number of inference cycles effectively. By detecting the interferences between rules, this method selects and executes compatible rule sets, and avoids the inconsistency problem of system working memory. In order to verify the validity of the authors' approach, this article developes a production system based on MapReduce, and applied this approach in the master server of a distributed production system. The experimental results show that their method can promote the performance of massive rules reasoning effectively.

Detecting difference between process models is important for many business process management scenarios, such as process version control and process merging. However, it is far from trivial to detect the process difference. Existing work suffers from drawbacks like inappropriate data structure support or expensive computation. In this paper, we propose FB-Diff, a feature-based difference detection approach. Firstly, a semi-ordered tree model called task based process structure tree (TPST) is used to represent a process model, which can correctly describe the structure as well as the behavior (the execution sequence of task nodes). Then FB-Diff adopts a divide and conquer strategy to find the similar parts of two TPSTs. Specifically, we divide the TPST into fragments that are represented by feature vectors. A feature vector consists of six features, and each feature describes a key characteristic of the fragment. Based on the similar parts, the edit script that can transform one TPST into the other is generated. The extensive experimental evaluation shows that our method can meet the real requirements in terms of precision and efficiency.

The structural similarity between two process models is usually considered as the main measurement for ranking the process models for a given query model. Current process query methods are inefficient since too many expensive computations of the graph edit distance are involved. To address this issue, using Petri-net as the modeling method, this paper presents the Hungarian algorithm based similarity query method. Unlike previous work where the non-task nodes (i.e., place nodes in the Petri-net) were lightly studied or even ignored, we think these non-task nodes also play an essential role in measuring the structural similarity between process models. First, we extract the context for each place and define the similarity for a pair of place nodes that are from different process models from two perspectives: commonality and the graph edit distance. Then, the place mapping is transformed to classical assignment problem that can be solved by Hungarian algorithm efficiently. Furthermore, we propose a new process similarity measurement on the basis of the place similarity. The extensive experimental evaluation shows that our Hungarian based methods outperform the baseline algorithm in both retrieval quality and query response time.

Efficient query processing over a large amount of business process models is important for managing the business process model repository. The structural similarity between two process models is considered as the main measurement for ranking the process models for a given search model. Current business process query methods are inefficient since too many expensive computations of the graph edit distance are involved for constructing the elements mapping as well as deriving the structural similarity. To address this, using Petri-net as the modelling method, this paper presents the Hungarian algorithm based query method, where we firstly define the context similarity for a pair of place nodes that are from different process models by taking into account both the common paths and common transitions, then transform the elements (e.g., The transitions and the places) mapping to classical assignment problem that can be solved by Hungarian algorithm efficiently. In this way, we can save a lot of time for searching the best combination of elements mapping. Finally, we use the common method of the graph edit distance to measure the structural similarity based on the found best combination of elements mapping.

Business Process Modeling (BPM) is widely acknowledged as the specific technology to analyze and control workflow with flexibility and convenience. With the development of SOA theory and practical exploration, it is challenging for BPM to involve web services and to maintain complex interaction information. Since the lack of method to involve web service semantic in business process, we present the Semantic Event-driven Process Chains (SEPCs) that describe the business process of semantic web services composition. A function metamodel is defined to solve semantic inadequacy in a modeling phase and transforming phase. A mapping rule is defined to identify structure patterns and transform the SEPCs model into WS-BPEL business process model. The experiment takes purchase process of one shipbuilding company as an example to show the practical usability of the integrated modeling approach.

Due to the problem that the forest growth simulation system has relatively fixed system architecture and difficulty in reconstruction, this paper presents a rapid reconfiguration platform of dynamic forest growth simulation system based on service component. The key technologies of the rapid reconfiguration platform such as the flowchart of reconfiguration, the design of service component and knowledge representation methods are described. This platform adopts the basic idea of service-oriented and component-oriented technology, and it is composed of supporting environment layer, simulation resource layer, simulation runtime supporting service layer, simulation integration layer and application layer. In this rapid reconfiguration platform, different simulation systems can be reconfigured rapidly according to the simulation tasks and some simulation systems have been reconfigured successfully.

This paper analyses the operational coordination mechanisms between the core enterprise and the partners within a supply chain having private local information. For a make to order production setting, a hybrid model of synergic production scheme based on negotiation is proposed. After analyzing the characteristic of synergic production process, this paper establishes the hierarchy of synergic production process monitoring. In the production process monitoring, four monitoring modes are defined according to the significance of the items in the whole product. A Web-based synergic production management system for supply chain has been implemented with programming in ASP and Ms SQL server. The application shows that the approach proposed in this paper is applicable to practical production in supply chain

The CAD model simplification technology has been paid more and more attentions for the requirement of seamless integration of CAD/CAE/CAM. This paper presents a new approach for feature suppressing of Brep models using local error evaluation. The improvement is that several features can synchronously be suppressed, and time overhead will be reduced at initial step without recognizing among most of them.