In[1], inaccuracies in several critical equations along with their accompanying descriptions appear in the article. Furthermore, some references are missing, and certain analyses of experiments are flawed.

The purpose is to optimize the Beetle Antenna Search (BAS) algorithm and apply it to the Intelligent Transportation System (ITS) to process traffic data in time and solve traffic congestion. This work studies the development status of ITS and the application status of the BAS algorithm. It optimizes BAS to converge to local optimization prematurely in high-dimensional space, affecting the prediction accuracy. Then, combined with the Least Squares Support Vector Machine Algorithm (LSSVM), the algorithm with quadratic interpolation optimization is proposed. The proposed algorithm is named the Quadratic Interpolation Beetle Antenna Search (QIBAS). On this basis, a traffic flow prediction model based on QIBAS-LSSVM is established. Finally, the improved QIBAS algorithm and Traffic Flow Prediction (TFP) model are verified. The results show that the test Mean Square Error (MSE) of the TFP model based on QIBAS-LSSVM increases by 4.28%, 7.38%, and 18.23%, respectively compared with the other three models. The test Mean Absolute Percentage Error (MAPE) increases by 0.09%, 0.06%, and 0.36% respectively. The proposed QIBAS algorithm has a good effect and high accuracy in short-term TFP. The research has important reference value for the digital transformation of transportation systems in modern smart cities.

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

This article investigates the problem of model-free tracking control for a class of continuum manipulators. A new type of robust control strategy is provided to address the problem, which primarily consists of two parts. First, to achieve output tracking with the desired accuracy, an error transformation scheme is constructed. Moreover, because of the robustness of the error transformation scheme against model uncertainties, no knowledge of the manipulator model is required, leading to a model-free control solution. Then, a tuning function is used to modify certain error variables to relax the requirements on the initial conditions of the error transformation scheme. In this way, the global stability of the closed-loop system can be guaranteed. Finally, both simulation and experimental results validate the expected performance of the presented control method.

This article is concerned with the fault-tolerant tracking control problem for pneumatic continuum manipulator (PCM) systems with actuator faults. Conventional control strategies applied to PCMs have difficulty addressing the tracking control issue for systems subject to actuator faults. In this article, we provide a robust fault-tolerant control strategy for solving this issue. We first present an error transformation approach that possesses potential robustness against model uncertainties and unknown actuator faults. To relax certain restrictions in the error transformation approach, we then adopt a tuning function to adjust the error variables. By doing so, global stability of the closed-loop system is ensured. Finally, the effectiveness of this control strategy is validated through experiments on a PCM.

The growth in the remote sensing data available has led to information overload, as seeking out the precise data has become more and more challenging. In this paper, a similarity evaluation modal is proposed to assist the user in his/her quest for accurate data. In the modal, correlation degree and correlation functions combining topology are proposed to evaluate the similarity degree between user's requirement and data quantitatively. Therefore, the advantages and disadvantages of different schemes can be compared.

Within IEEE 802.11 Wireless Local Area Networks (WLANs), a handover between wireless Access Points (APs) is inevitable for the mobile stations to maintain network connectivity as they move. Handover occurs frequently due to the limited coverage range of APs and the mobility of stations (STAs). High handover latency causes interruption to data flow and degrades the quality of service (QoS) experienced by the mobile stations. Therefore, minimizing handover latency is critical for maintaining seamless communications and improved QoS. In this paper, we build upon recent advances in mobile networks whereby Self-Organizing Network (SON) has been shown to work well in handling mobility. Inspired by SON, we propose an algorithm that combines: (i) scanning timers optimized with Genetic Algorithm (GA) and (ii) self-adapting timers to reduce the handover latency for 802.11 WLANs. We use the IEEE 802.11k standard to retrieve radio parameters from measurement reports and feed these parameters to a GA to find the best fit values to reduce scanning delay. We compare the proposed algorithm performance with IEEE 802.11 standard and our previous work neighbor list mechanism (NLM). Evaluation results demonstrate that our proposed algorithm reduce the handover latency by 78.79% compared with standard 802.11, the packet loss is 3.37% lower than standard 802.11 while the throughput improves up to 3.93%.

Atomic force microscopy (AFM) is a powerful measurement instrument, which has been widely implemented in various fields. To enhance the maneuverability, an AFM can be modified and its cantilever can be controlled as a robotic end-effector. To precisely control the nanorobots, their scanners inherent hysteresis, which is the main disadvantage, should be compensated sufficiently. Mostly, hysteresis compensators used in AFM control system only employ the symmetric models, which cannot well represent the asymmetric hysteresis phenomena of piezo-scanners. However, under many cases, the smart material based scanners possess asymmetric hysteresis slightly or seriously, which is far more complex than the regular symmetric case; in addition, for commercial or customized AFMs, the scanners are usually controlled without feedback. These drawbacks tackle further improvement of positioning accuracy of AFM systems. To effectively describe and further reduce the hysteresis of general cases, we propose a new type of generalized Prandtl-Ishlinskii (PI) operator based superposition model, named unparallel PI (UPI) model. The flexible edge of the UPI operator can be tilted freely within a defined range, which enables it to capture asymmetric hysteresis efficiently. To cancel the hysteresis effect in an open-loop system, three inverse compensation approaches are proposed, compared, and the corresponding stability is analyzed. Experiments with AFM based nanorobot verified the proposed approaches with convincing performance.

Robot teaching is necessary for the current industrial robot applications. Because work stations have to be stopped to perform teaching processes, the manufacturing efficiency is decreased. In this paper we propose to utilize an uncalibrated vision system mounted on a mobile robot (“Adult” robot) with learning capability to supervise a group of fixed robots (“Child” robots) to accomplish a robot teaching task automatically without stopping work stations. To increase the system flexibility, hand-eye calibration and calibration between the robots are eliminated. A Partial Observable Markov Decision Process(POMDP) is formulated and solved using the Successive Approximation of the Reachable Space under Optimal Policies (SARSOP) algorithm to enable the teaching process using image features with uncertainties. The proposed algorithm was tested using the “adult” robot to teach a “child” robot to perform a high accuracy peg-in-hole assembly process. The experimental results verify the effectiveness of the proposed approach. The proposed method can also be used in other areas to enable robot teaching.