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Currently, in most traditional VSLAM (visual SLAM) systems, static assumptions result in a low accuracy in dynamic environments, or result in a new and higher level of accuracy but at the cost of sacrificing the real–time property. In highly dynamic scenes, balancing a high accuracy and a low computational cost has become a pivotal requirement for VSLAM systems. This paper proposes a new VSLAM system, balancing the competitive demands between positioning accuracy and computational complexity and thereby further improving the overall system properties. From the perspective of accuracy, the system applies an improved lightweight target detection network to quickly detect dynamic feature points while extracting feature points at the front end of the system, and only feature points of static targets are applied for frame matching. Meanwhile, the attention mechanism is integrated into the target detection network to continuously and accurately capture dynamic factors to cope with more complex dynamic environments. From the perspective of computational expense, the lightweight network Ghostnet module is applied as the backbone network of the target detection network YOLOv5s, significantly reducing the number of model parameters and improving the overall inference speed of the algorithm. Experimental results on the TUM dynamic dataset indicate that in contrast with the ORB–SLAM3 system, the pose estimation accuracy of the system improved by 84.04%. In contrast with dynamic SLAM systems such as DS–SLAM and DVO SLAM, the system has a significantly improved positioning accuracy. In contrast with other VSLAM algorithms based on deep learning, the system has superior real–time properties while maintaining a similar accuracy index. [ABSTRACT FROM AUTHOR]

The traceability of blockchain is crucial to the quality and safety of agricultural products, primarily when bacterial contamination occurs in the agricultural supply chain. To gain a deeper understanding of the impacts on the quality and safety of agricultural products, we built a three-level agricultural supply chain model comprising one retailer, one manufacturer, and one supplier. We aimed to explore the impact of blockchain traceability and retailers' altruistic preference on pricing decisions and channel member profit, and to then examine the motivation of enterprises to participate in blockchain technology. Our study showed that: (1) blockchain technology has the potential to improve the prices and profits of supply chain members without considering the cost of blockchain; and (2) blockchain technology has the potential to improve the retailer's price when the unit variable cost of blockchain is higher than a certain threshold. It can also reduce the manufacturer's price and the supplier's output. However, the excessive cost of blockchain can also aggravate the double marginalization effect of the supply chain. When both the unit variable cost and the fixed input cost of the blockchain are low, each enterprise has the incentive to participate in the blockchain. (3) In the scenario of the altruistically-preferred retailer, when the blockchain variable cost is lower than a certain threshold, the retailer's altruistic preferences can improve the manufacturer's and supplier's profit, but it will reduce the retailer's profit. [ABSTRACT FROM AUTHOR]

Aiming at the trajectory generation and optimization of mobile robots in complex and uneven environments, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) is proposed in this paper. In order to describe the uneven environment with various obstacles, a 2D-H map is introduced in this paper. Then an adaptive ant colony algorithm based on simulated annealing (SA) is proposed to generate initial trajectories of mobile robots, where based on a de-temperature function of the simulated annealing algorithm, the pheromone volatilization factor is adaptively adjusted to accelerate the convergence of the algorithm. Moreover, the length factor, height factor, and smooth factor are considered in the comprehensive heuristic function of ACO to adapt to uneven environments. Finally, a mutual learning algorithm is designed to further smooth and shorten initial trajectories, in which different trajectory node sequences learn from each other to acquire the shortest trajectory sequence to optimize the trajectory. In order to verify the effectiveness of the proposed scheme, MuL-ACO is compared with several well-known and novel algorithms in terms of running time, trajectory length, height, and smoothness. The experimental results show that MuL-ACO can generate a collision-free trajectory with a high comprehensive quality in uneven environments. [ABSTRACT FROM AUTHOR]

Featured Application: This paper fuses different sensors to form a general high-precision SLAM framework for multi-scene applications. The algorithm framework in this paper can be extended to the fields of autonomous driving, robot navigation, and 3D reconstruction. Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In 2014, more than 43 cities in China were racing to construct their urban rail systems (including metro and light rail systems), recognizing that an urban rail system will be a good solution to the tough problems that they are faced with, including traffic congestion and PM2.5 air pollution. On 22 August 2012, the first electric double-layer capacitor (EDLC) energy storage-type rail vehicle in the world was unveiled at Zhuzhou Electric Locomotive Co., China. The EDLC rail system has been considered a promising sustainable urban rail system, which is expected to further improve the energy efficiency and to reduce environmental pollution. The first commercial EDLC tram produced by Zhuzhou Electric Locomotive Co. has been applied at Guangzhou Metro Corp. recently. From the view point of scientific research, the system design and energy management of EDLC rail systems have been extensively studied in the literature, while the stationary charging station design for the EDLC energy storage-type urban rail vehicles has been rarely reported. Thus, the aim of this paper is to report a stationary charging station that has been successfully applied in the EDLC rail system produced by Zhuzhou Electric Locomotive Co., China. [ABSTRACT FROM AUTHOR]

The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system. [ABSTRACT FROM AUTHOR]

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

With continuous development in the scales of cities, the role of the metro in urban transportation is becoming more and more important. When running at a high speed, the safety of the train in the tunnel is significantly affected by any foreign objects. To address this problem, we propose a foreign object intrusion detection method based on WiFi technology, which uses radio frequency (RF) signals to sense environmental changes and is suitable for lightless tunnel environments. Firstly, based on extensive experiments, the abnormal phase offset between the RF chains of the WiFi network card and its offset law was observed. Based on this observation, a fast phase calibration method is proposed. This method only needs the azimuth information between the transmitter and the receiver to calibrate the the phase offset rapidly through the compensation of the channel state information (CSI) data. The time complexity of the algorithm is lower than the existing algorithm. Secondly, a method combining the MUSIC algorithm and static clutter suppression is proposed. This method utilizes the incoherence of the dynamic reflection signal to improve the efficiency of foreign object detection and localization in the tunnel with a strong multipath effect. Finally, experiments were conducted using Intel 5300 NIC in the indoor environment that was close to the tunnel environment. The performance of the detection probability and localization accuracy of the proposed method is tested. [ABSTRACT FROM AUTHOR]

A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of sparse anchor nodes. Firstly, a hybrid channel model was constructed to reflect the relationship between distance and receiving signal strength, which incorporates the underground complex communication characteristics, including the analytical ray wave guide model, the Shadowing effect, the tunnel size, and the penetration effect of obstacles. Secondly, a trustable anchor node selection algorithm with node movement detection is proposed, which solves the problem of high-precision node location in anisotropic networks with sparse anchor nodes after the disaster. Consequently, according to the node location and the obstacles, the optimal guidance path is developed by using the modified minimum spanning tree algorithm. Finally, the simulations in the 3D scene are conducted to verify the performance of the proposed method on the localization accuracy, guidance path effectiveness, and scalability. [ABSTRACT FROM AUTHOR]

Driving style recognition plays a key role in ensuring driving safety and improving vehicle traffic efficiency. With the development of sensing technology, data-driven methods are more widely uesd to recognize driving style. However, adequately labeling data is difficult for supervised learning methods, while the classification accuracy is not sufficiently approved for unsupervised learning methods. This paper proposes a new driving style recognition method based on Tri-CatBoost, which takes CatBoost as base classifier and effectively utilizes the semi-supervised learning mechanism to reduce the dependency on data labels and improve the recognition ability. First, statistical features were extracted from the velocity, acceleration and jerk signals to fully characterize the driving style. The kernel principal component analysis was used to perform nonlinear feature dimension reduction to eliminate feature coupling. CatBoost is an ensemble of symmetric decision trees whose symmetry structure endows it fewer parameters, faster training and testing, and a higher accuracy. Then, a Tri-Training strategy is employed to integrate the base CatBoost classifiers and fully exploit the unlabeled data to generate pseudo-labels, by which the base CatBoost classifiers are optimized. To verify the effectiveness of the proposed method, a large number of experiments are performed on the UAH DriveSet. When the labeling ratio is 50%, the macro precision of Tri-CatBoost is 0.721, which is 15.7% higher than that of unsupervised K-means, 1.6% higher than that of supervised GBDT, 3.7% higher than that of Self-Training, 0.7% higher than that of Co-training, 1.5% higher than that of random forest, 6.7% higher than that of decision tree, and 4.0% higher than that of multilayer perceptron. The macro recall of Tri-CatBoost is 0.744, which is also higher than other methods. The experimental results fully demonstrate the superiority of this work in reducing label dependency and improving recognition performance, which indicates that the proposed method has broad application prospects. [ABSTRACT FROM AUTHOR]

Recent research has indicated that using the mobility of the actuator in wireless sensor and actuator networks (WSANs) to achieve mobile data collection can greatly increase the sensor network lifetime. However, mobile data collection may result in unacceptable collection delays in the network if the path of the actuator is too long. Because real-time network applications require meeting data collection delay constraints, planning the path of the actuator is a very important issue to balance the prolongation of the network lifetime and the reduction of the data collection delay. In this paper, a multi-hop routing mobile data collection algorithm is proposed based on dynamic polling point selection with delay constraints to address this issue. The algorithm can actively update the selection of the actuator's polling points according to the sensor nodes' residual energies and their locations while also considering the collection delay constraint. It also dynamically constructs the multi-hop routing trees rooted by these polling points to balance the sensor node energy consumption and the extension of the network lifetime. The effectiveness of the algorithm is validated by simulation.