Accurately predicting the remaining useful life of a bearing is essential for predictive maintenance of industrial apparatus. Conventional Transformer models struggle to capture the local temporal relationships and essential time-step information present in vibration signals. This research introduces the PEC-GAT Transformer model for predicting the remaining useful life of bearings, which combines multi-scale positional encoding convolutions with a gated attention mechanism to tackle these issues. Multi-scale 1D convolutions capture local temporal data across diverse ranges, while trainable position embeddings and global scaling factors provide adaptive position encoding. A dual-gate attention mechanism amalgamates fundamental traits with overarching contextual information, dynamically modifying attention weights. Experiments demonstrate that the PEC-GAT method significantly improves the accuracy of predicting the remaining useful life of bearings and effectively overcomes the limitations of conventional Transformer models. This methodology offers a comprehensive and efficient intelligent prediction system for the control of industrial equipment health.

Abstract With the development of location-based services, smart cities, and intelligent transportation, route planning has evolved beyond shortest path finding to satisfy user’s flexible travel purposes through the Optimal Routes with Collective Spatial Keywords (ORCSK) routing. Because different Points of Interest (POIs) contain different sets of keywords, the user usually needs to visit multiple POIs to fulfill all needs. Moreover, the POIs’ stop hardness (time and cost) also influences user experience, but it was ignored by the existing solutions. Therefore, this work proposes to extend the ORCSK problem into Skyline Optimal Routes with Collective Spatial Keyword (Sky-ORCSK) by considering both distance and stop hardness. Specifically, we first propose the IG-Sky algorithm from the spatial keyword search perspective by extending the IG-Tree. Then we propose the DA-Sky algorithm from the path enumeration perspective by extending our previous DA-CSK. Furthermore, five optimization strategies are proposed to improve efficiency by pruning the search space. Extensive experimental evaluations on real-world datasets demonstrate the algorithms’ efficacy and reliability, marking a significant step forward in refined route planning for modern urban environments.

As more detailed POI (Point of Interest) information has been incorporated into road network, routing has evolved from finding paths from one place to another, to satisfying users’ needs (keywords) along the trip. However, the existing solutions either only support one keyword per POI, or require a fixed visiting order, or only provide one option to choose from. Therefore, we study the top-k Optimal Routes with Collective Spatial Keywords (k-ORCSK) problem, which is the most general keyword-aware routing problem that supports multiple keywords, arbitrary orders, and top-k results. To solve this problem, we apply an enumeration framework and reduce the complexity by contracting non POI-related vertices and taking the keywords into account. After that, we propose a best-first path expansion method DA-CSK based on deviation to convert the enumeration paradigm from the distance-oriented to the keyword-oriented. Finally, several optimization techniques are provided to further improve the query efficiency. Extensive experiments conducted on multiple real-life road networks show that our method can provide higher quality results more efficiently.

We propose a rate-adaptive multicast (RAM) routing protocol for mobile ad-hoc networks (MANETs) that is multirate-aware. During the process of path discovery, the quality of wireless links is estimated to suggest optimal transmission rates, which are then used to calculate the total transmission time incurred by the mobile nodes on a path. Among several considered paths from a source to a destination, RAM selects the path with the lowest total transmission time. Simulation results have shown that RAM outperforms single-rate multicast in terms of packet delivery ratio, packet end-to-end delay, and throughput of the multicast group. In this paper, we apply RAM to effectively support video multicast in MANETs

A current trend in wireless communications is to enable wireless devices to transmit at different rates. That multirate capability has been defined in many standards such as 802.11a, 802.11b, 802.11g, and HiperLAN2. We propose a rate-adaptive multicast (RAM) protocol that is multirate-aware. During the process of path discovery, the quality of wireless links is estimated to suggest optimal transmission rates, which are then used to calculate the total transmission time incurred by the mobile nodes on a path. Among several considered paths from a source to a destination, RAM selects the path with the lowest total transmission time. Our work is the first that proposes the use of the multirate capability in multicast. The proposed RAM protocol works with any multirate standards, and does not require any modifications to the standards. Our simulation results show that RAM outperforms single-rate multicast in terms of packet delivery ratio, packet end-to-end delay, and throughput of the multicast group.