Unsupervised Camouflaged Object Detection (UCOD) presents a significant challenge due to the inherent similarity between camouflaged objects and their backgrounds, compounded by the absence of manual annotations. Although pixel-level pseudo-labeling has proven effective for unsupervised salient object detection (USOD), it is far less reliable for COD, where the concealed and ambiguous nature of camouflaged objects frequently produces noisy pseudo-labels, causing misjudgments, missed detections, and imprecise boundaries. To overcome this, we propose SAPNet, a novel self-anchored progressive framework for UCOD. Rather than depending on noisy pixel-level supervision, we leverage semantically reliable foreground and background regions as high-confidence anchors. This effectively transforms the unsupervised problem into a more robust weakly supervised paradigm, reducing learning difficulty and mitigating overfitting to noise. SAPNet learns camouflaged objects progressively by first emphasizing these confident regions and then exploiting DINO's contextual awareness to recover complete structures. Central to our framework is the semantic-driven region detector (SDRD), which employs cascaded convolutions and a residual attention projection mechanism to suppress background noise, filter erroneous information, and enhance spatial context, ensuring reliable supervision signals. Furthermore, a region-based context inference module (RCIM) is introduced to iteratively refine object boundaries by integrating multi-level semantic features under the guidance of these refined region-level anchors. Extensive experiments on four benchmark COD datasets demonstrate that SAPNet significantly outperforms state-of-the-art unsupervised methods. The source code of our SAPNet is available at https://github.com/ArloJie/SAPNet.

Online-expansion, online maintenance and fault tolerance are vital functions of rail signal monitoring system, which can guarantee the system always in working status and satisfy the sustainable development of rail transit. Due to the limitations of traditional centralized system structure, it is necessary to optimize the design of rail station signal monitoring system to make the system possess these functions. In this research, the novel concept of Role on Autonomous Decentralized System (RoADS) is adopted to optimize the design of rail signal monitoring system. On the one hand, RoADS has the characteristic of role dynamic conversion which is essential to realize the special function of online-expansion, online maintenance and fault tolerance. On the other hand, RoADS also can support the basic function of data collection. In order to verify the validity of the proposed method, a case study has been performed on ZPW-2000 track circuit monitoring system which is the subsystem of rail signal system mainly responsible to monitor the track circuit. The results clearly indicate that the optimized system based on RoADS can not only realize the function of data collection, but also possess the function of online-expansion, online maintenance and fault tolerance. Consequently, the proposed method based on RoADS can improve the safety level of rail transit and reduce the cost operation which is of great significance for maintaining the sustainable urban development of rail transit.

The communication system that is presently applied in the European Train Control System can only support data exchange between vehicles and ground, but the direct vehicle‐to‐vehicle communication is not available. The details of interlocking information and other vehicles’ movements are invisible to drivers who are the last defense to prevent unsafe scenarios. As connected vehicles have been envisioned to enhance transportation efficiency and improve safety, the direct vehicle‐to‐vehicle communication network is involved in this paper to increase the safety of railway transport. In this paper, a new train movement authority (MA+) is proposed. Apart from a wireless communication unit, this system does not require any other infrastructure. With the assistance of vehicle‐centric communication technology, MA+ can detect the condition of switches and trains within a certain scope. In this paper, the system structure of MA+ is proposed. Additionally, different implementation scenarios are also discussed. The detection range is estimated and validated based on mathematical calculation and experimental equations. An application demo of MA+ is presented on the Driver Machine Interface of the onboard equipment. The results indicate that MA+ can be a flexible and scalable system for furthering the improvement of railway safety.

Due to the inaccessibility to VOBC (Vehicle Onboard Controller) diagnosing data, urban railway transit operation suffers from transient failures and long time failures during operation time. This essay put forward the idea of a real time diagnosing data storage and cloud sharing system, to collect, store and sharing them, thus reproduce the transient failures for debugging, and proved a quick and effective solution to failures during operation time from a distance.

Vehicular technologies have been recently introduced to highway safety applications and caught much attention of governments and management authorities. The vehicular ad hoc network (VANET) uses cars as mobile nodes to form a mobile network. VANET offers potential and promising technical solutions to vehicular safety. Multi-hop broadcasting schemes are particularly preferred methods to transmit time-sensitive safety warning information to potentially influenced vehicles. However, broadcasting may lead to frequent contention and serious collision, thus causing broadcast storms. In order to alleviate broadcast storm and promptly disseminate safety warnings, the paper proposes an optimal forwarder selection scheme to minimize the number of rebroadcasting nodes and guarantee fast and efficient safety warning information dissemination. The proposed forwarder selection scheme is based on the analytic hierarchy process (AHP). The criteria include inter-vehicular lateral and longitudinal distances, vehicular communication ranges, and vehicles covered within the communication range of previous forwarder vehicles. The AHP-based forwarder selection model is established and model's validity is verified mathematically in this paper.

Because of the fast developing of the computer and communication technology, the railway control system is uprating very fast. And railway control system is the key to guaranteeing the safety of rail transportation. So more and more researchers and companies spend more and more resource in this field. The university is an inconvenient strength in the research. So university should have a very good experiment platform to support their research and education work. But the resource of the university is limited. On the other hand, the university needs to catch up the development of the technology. Then the experiment platform should have the extensible ability. And the Autonomous Decentralized System (ADS) has online expansion technology. This paper will propose a sustainable experiment platform for railway control system by using the online expansion technology of ADS to satisfy the demands of university.