This paper aims to investigate the problems of secure reachability set synthesis and reliable event-driven mechanism design for Takagi-Sugeno (T-S) fuzzy semi-Markovian network control system (S-MNCS) subjected to external disturbances. To this end, important issues such as actuator failures, stochastic jump parameters, and stochastic deception attacks occurring in the transmission network are given priority consideration during the design of the controller scheme. At this time, the stochastic parameters are generated using a semi- Markov process where the transition rate varies depending on the sojourn time. Next, a novel mode-dependent fuzzy event trigger control (FETC) mechanism for S-MNCS is proposed to achieve reliable performance against actuator failure and deception attacks, reduce unnecessary communication, and improve communication efficiency. Then, a new integral inequality based on free-weight matrices is introduced to minimize model conservatism using the knowledge of the aperiodic sampling pattern. Meanwhile, for the purpose of reducing conservatism, an augmented double-sided looped -type Lyapunov functional (ADSLLF) using membership functions (MFs) information is introduced to take the available information of the actual sampling pattern. Then, by combing the ADSLLF and the proposed free-matrix integral inequality, some sufficient conditions in terms of linear matrix inequalities (LMIs) are derived such that all states of the systems included by an ellipsoid region. Finally, the efficiency and applicability of the approach proposed in this study are demonstrated through the simulation of networked autonomous electric vehicle systems, and its superiority is proven by comparing it with the existing method through the simulation of a well-known Rossler system.

Visual tracking has become widespread in swarm robots for intelligent video surveillance, navigation, and autonomous vehicles due to the development of machine learning algorithms. Discriminative correlation filter (DCF)-based trackers have gained increasing attention owing to their efficiency. This study proposes “context-aware environmental residual correlation filter tracking via deep convolution features (CAERDCF)” to enhance the performance of the tracker under ambiguous environmental changes. The objective is to address the challenges posed by intensive environment variations that confound DCF-based trackers, resulting in undesirable tracking drift. We present a selective spatial regularizer in the DCF to suppress boundary effects and use the target’s context information to improve tracking performance. Specifically, a regularization term comprehends the environmental residual among video sequences, enhancing the filter’s discrimination and robustness in unpredictable tracking conditions. Additionally, we propose an efficient method for acquiring environmental data using the current observation without additional computation. A multi-feature integration method is also introduced to enhance the target’s presence by combining multiple metrics. We demonstrate the efficiency and feasibility of our proposed CAERDCF approach by comparing it with existing methods using the OTB2015, TempleColor128, UAV123, LASOT, and GOT10K benchmark datasets. Specifically, our method increased the precision score by 12.9% in OTB2015 and 16.1% in TempleColor128 compared to BACF.

Recently, autonomous driving technology has been in the spotlight. However, autonomous driving is still in its infancy in the railway industry. In the case of railways, there are fewer control elements than autonomous driving of cars due to the characteristics of running on railways, but there is a disadvantage in that evasive maneuvers cannot be made in the event of a dangerous situation. In addition, when braking, it cannot be decelerated quickly for the weight of the body and the safety of the passengers. In the case of a tram, one of the railway systems, research has already been conducted on how to generate a profile that plans braking and acceleration as a base technology for autonomous driving, and to find the location coordinates of surrounding objects through object recognition. In pilot research about the tram’s automated driving, YOLOv3 was used for object detection to find object coordinates. YOLOv3 is an artificial intelligence model that finds coordinates, sizes, and classes of objects in an image. YOLOv3 is the third upgrade of YOLO, which is one of the most famous object detection technologies based on CNN. YOLO’s object detection performance is characterized by ordinary accuracy and fast speed. For this paper, we conducted a study to find out whether the object detection performance required for autonomous trams can be sufficiently implemented with the already developed object detection model. For this experiment, we used the YOLOv4 which is the fourth upgrade of YOLO.

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.