Complicated Dynamic Environment Research Articles

Acceleration of Oral Wound Healing under Diabetes Mellitus Conditions Using Bioadhesive Hydrogel

Oral wounds under diabetic conditions display a significant delay during the healing process, mainly due to oxidative stress-induced inflammatory status and abnormal immune responses. Besides, the wet and complicated dynamic environment of the oral cavity impedes stable treatment of oral wounds. To overcome these, a biomimetic hydrogel adhesive was innovatively developed based on a mussel-inspired multifunctional structure. The adhesive displays efficient adhesion and mechanical harmony on the oral mucosa through enhanced bonding in an acidic proinflammatory environment. The bioadhesive hydrogel exhibits excellent antioxidative properties by mimicking antioxidative enzymatic activities to reverse reactive oxygen species (ROS)-mediated immune disorders. Experiments on oral wounds of diabetic rats showed that this hydrogel adhesive could effectively protect against mucosal wounds and obviously shorten the inflammatory phase, thus promoting the wound-healing process. Therefore, this study offers a promising therapeutic choice with the potential to advance the clinical treatment of diabetic oral wounds.

A Novel Flexible Lane Changing (FLC) Method in Complicated Dynamic Environment for Automated Vehicles

Decision making and path planning in case of highly transient dynamics of the surrounding as well as the effect of road condition are the issues that are not completely solved in the previous researches. The goal is to perform a safe and comfortable lane change that includes flexible re-planning capabilities. In this paper, a novel structure for path planning and decision making part of a vehicle automatic lane change has been introduced which comprehensively considers both longitudinal and lateral dynamics of the vehicle. The presented method is able to perform re-planning even in the middle of a lane change maneuver according to new traffic condition. Inclusion of the dynamics of all involved vehicles and providing online performance are the other advantages of the proposed system. The algorithm is simulated and various scenarios are constructed to evaluate the efficiency of the system. The results show that the system has completely acceptable performance.

Quadrotor navigation in dynamic environments with deep reinforcement learning

PurposeThis work aims to combine the cloud robotics technologies with deep reinforcement learning to build a distributed training architecture and accelerate the learning procedure of autonomous systems. Especially, a distributed training architecture for navigating unmanned aerial vehicles (UAVs) in complicated dynamic environments is proposed.Design/methodology/approachThis study proposes a distributed training architecture named experience-sharing learner-worker (ESLW) for deep reinforcement learning to navigate UAVs in dynamic environments, which is inspired by cloud-based techniques. With the ESLW architecture, multiple worker nodes operating in different environments can generate training data in parallel, and then the learner node trains a policy through the training data collected by the worker nodes. Besides, this study proposes an extended experience replay (EER) strategy to ensure the method can be applied to experience sequences to improve training efficiency. To learn more about dynamic environments, convolutional long short-term memory (ConvLSTM) modules are adopted to extract spatiotemporal information from training sequences.FindingsExperimental results demonstrate that the ESLW architecture and the EER strategy accelerate the convergence speed and the ConvLSTM modules specialize in extract sequential information when navigating UAVs in dynamic environments.Originality/valueInspired by the cloud robotics technologies, this study proposes a distributed ESLW architecture for navigating UAVs in dynamic environments. Besides, the EER strategy is proposed to speed up training processes of experience sequences, and the ConvLSTM modules are added to networks to make full use of the sequential experiences.

A Feasible Fuzzy-Extended Attribute-Based Access Control Technique

Attribute-based access control (ABAC) is a maturing authorization technique with outstanding expressiveness and scalability, which shows its overwhelmingly competitive advantage, especially in complicated dynamic environments. Unfortunately, the absence of a flexible exceptional approval mechanism in ABAC impairs the resource usability and business time efficiency in current practice, which could limit its growth. In this paper, we propose a feasible fuzzy-extended ABAC (FBAC) technique to improve the flexibility in urgent exceptional authorizations and thereby improving the resource usability and business timeliness. We use the fuzzy assessment mechanism to evaluate the policy-matching degrees of the requests that do not comply with policies, so that the system can make special approval decisions accordingly to achieve unattended exceptional authorizations. We also designed an auxiliary credit mechanism accompanied by periodic credit adjustment auditing to regulate expediential authorizations for mitigating risks. Theoretical analyses and experimental evaluations show that the FBAC approach enhances resource immediacy and usability with controllable risk.

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.

Artificial moment method using attractive points for the local path planning of a single robot in complicated dynamic environments

SUMMARYA novel path planner is presented for the local path planning of a single robot (represented with R) in a complicated dynamic environment. Here a series of attractive points are computed based on attractive segments for guiding R to move along a shorter path. Each attractive segment is obtained by using the full environmental knowledge and will be used for several sampling times in general. A motion controller, which is designed based on artificial moments and a robot model that has a principal motion direction line(PMDline), makes R move closely to attractive points while away from obstacles. Attractive and repulsive moments are designed, which only make R's PMDline face toward attractive points and opposite to obstacles in general, as in most cases, R will move along its PMDline with its full speed. Because of the guidance of attractive points and R's full-speed motion, the global convergence is guaranteed. Simulations indicate that the proposed path planner meets the requirements of real-time property while can optimize R's traveling path.

An improved GT algorithm for solving complicated dynamic function optimization problems

An improved Guo Tao algorithm (IGT algorithm) is proposed for solving complicated dynamic function optimization problems, and a function optimization benchmark problem with constrained condition and two dynamic parameters has been designed. The results achieved by IGT algorithm have been compared with the results from the Guo Tao algorithm (GT algorithm). It is shown that the new algorithm (IGT algorithm) provides better results. This preliminarily demonstrates the efficiency of the new algorithm in complicated dynamic environments.

Landmark tracking based autonomous navigation schemes for landing spacecraft on asteroids

Autonomous optical navigation schemes for pinpoint landing of spacecraft on asteroids are considered. Due to the long communication delay and complicated dynamic environment close to asteroids, traditional spacecraft navigation and control using the deep space network (DSN) is not suitable for the precise landing of spacecraft on asteroids. Then it is necessary to develop new autonomous navigation algorithms for future asteroid sample and return missions. To meet this requirement, this paper presents two landmark tracking navigation algorithms based on nonlinear least squares (NLS) and extended Kalman filter (EKF). The validity of the proposed navigation schemes is confirmed by numerical simulation.

A vector-format fuzzy logic approach for online robot motion planning in 3D space and its application to underwater robotic vehicle

SUMMARYDue to its inherent advantages such as reasoning in the format of heuristic rules based on human experience and less stringent requirement on environmental description, fuzzy logic is a promising tool for the robot motion planning in 3-dimensional dynamic environment. In general, in the Cartesian space, the variables used in characterizing the motion of a mobile robot, such as position, velocity, and force relative to other objects or coordinate frames, contain both the magnitude and the pointing information. In previous studies, the fuzzy reasoning on the pointing information was often developed based on the decomposition of the pointing vector followed by conventional fuzzy logic technique on individual vector components. Consequently, when multiple pointing variables are involved, the number of fuzzy variables that need to be considered simultaneously becomes large and the rule base may become very complex, which diminishes the advantages of the fuzzy reasoning approach. In this research, we tackle this issue by implementing a new fuzzy reasoning approach based on vector-format fuzzy variables. To achieve this, a set of new membership functions is defined for the vector-format fuzzy variables, followed by the establishment of a series of new vector-based fuzzification, fuzzy inference, and defuzzification procedures. By treating the multidimensional variables as unitary linguistic variables, the number of fuzzy variables in the fuzzy propositions and therefore the scale of the rule base can be reduced considerably. As an application example, the proposed new fuzzy reasoning approach for motion planning is applied to an Underwater Robotics Vehicle (URV) operating in an oceanic environment, where the pointing of the goal and the pointing vectors of the obstacles are treated as vector-type fuzzy variables, which leads to a compact and significantly simplified rule base. The motion planner can successfully guide the URV to move in the complicated dynamic environ-ment in a real-time fashion, which clearly demonstrates the effectiveness and robustness of the new fuzzy logic approach.

Real-time multitarget tracking by a cooperative distributed vision system

Target detection and tracking is one of the most important and fundamental technologies to develop real-world computer vision systems such as security and traffic monitoring systems. This paper first categorizes target tracking systems based on characteristics of scenes, tasks, and system architectures. Then we present a real-time cooperative multitarget tracking system. The system consists of a group of active vision agents (AVAs), where an AVA is a logical model of a network-connected computer with an active camera. All AVAs cooperatively track their target objects by dynamically exchanging object information with each other With this cooperative tracking capability, the system as a whole can track multiple moving objects persistently even under complicated dynamic environments in the real world. In this paper we address the technologies employed in the system and demonstrate their effectiveness.