The high sampling frequency of highway structural health monitoring systems brings a heavy burden on data storage. However, existing dynamic response identification approaches can guarantee either reduced data volume after identification or high accuracy of dynamic response identification. Motivated by this, we propose a real-time dynamic response identification method to filter meaningless data. Our method not only selects effective features from highway structural health monitoring data, but also designs a training data generation strategy for machine learning models within the dynamic response identification framework. Experimental results on real highway structural health monitoring data demonstrate that our proposed approach spends 0.4 ms to process the monitoring data generated in 1 s and saves around 91.63% storage space. Also, the recall value of our method achieves 0.91 on average.

Abstract In the process of responding to user applications on the highway structural health monitoring data sharing platforms, the objective is to decrease the network transmission costs and avoid a mass of redundant Input/Output operations between the storage server and local hard disks. Since none of existing job scheduling and structural health monitoring data analysis research has focused on this topic, we study the problem of cost-saving response scheduling for highway structural health monitoring data applications. To solve this problem, we develop a greedy response scheduler with (1+ $$\frac{1}{e-1})$$ -approximation ratio. Evaluation results demonstrate the effectiveness and efficiency of our proposed solution.

As the cornerstone of the Internet of Things, the deployment of wireless sensor networks remains a challenging problem, as it is difficult to balance coverage quality and computational efficiency, particularly when dealing with large-scale node deployments. To solve this problem, we propose a coverage optimization algorithm that integrates max-heap-based priority maintenance with resultant vector-driven dispersion metrics. Our approach organizes sensor nodes via a max-heap structure and introduces a novel resultant vector-based metric to evaluate coverage potential; the key optimization parameter α for this metric was calibrated through theoretical derivation based on optimal hexagonal geometry. This method implicitly transforms the Unit Disk Cover (UDC for brief) problem into a Discrete Unit Disk Cover (DUDC for brief), achieving real-time performance and high-quality coverage with reduced redundancy. Experimental results demonstrate that our method reaches the best coverage quality compared to baseline methods while maintaining almost the same computational complexity.

The rapid proliferation of emerging video services has substantially increased the demand for real-time transmission of high-definition video content. However, optimizing the trade-off between video transmission quality and communication bandwidth remains a significant challenge. To address this issue, we propose RAJSCC, a rate-adaptive video deep joint source-channel coding (DeepJSCC) framework. RAJSCC incorporates a spatio-temporal token merging mechanism to aggregate semantically similar tokens, effectively reducing redundancy both within and across frames. Additionally, an adaptive token merging predictor, designed based on simple statistical features of the input videos, enables dynamic rate control at the group of pictures (GoP) level, ensuring smooth and continuous variation in the overall video coding rate. Extensive experiments demonstrate that RAJSCC significantly outperforms traditional video transmission schemes, such as H.264 and H.265 with low-density parity-check (LDPC), as well as existing DeepJSCC methods, in terms of reconstruction quality. More importantly, the proposed adaptive spatio-temporal token merging mechanism reduces bandwidth consumption by 63.5% and computational cost by 13.0%, while incurring only a marginal 1–2 dB degradation in reconstruction quality. These findings highlight the effectiveness of RAJSCC in achieving a superior balance between transmission efficiency and video quality, making it a promising solution for real-time high-definition video communication in bandwidth-constrained environments.

The subsequence matching problem utilizing dynamic time warping as the similarity measurement has been recognized as a key operation in time series analysis for more than two decades. Existing index-free algorithms depend on DTW lower bounds to discard the unpromising candidate. However, these approaches typically cost O ( m ) time for each candidate, where m is the length of the query. Consequently, the overhead of computing the DTW lower bounds occupies a significant portion of the time in subsequence matching tasks. This paper proposes new algorithms capable of computing the DTW lower bounds in average O (log m ) time for each candidate, substantially alleviating this bottleneck of the subsequence matching problem. In addition, this paper designs novel DTW lower bounds according to the characteristics of the subsequence matching problem, which is more effective without introducing significant computational overhead. Based on the above improvements, an efficient subsequence matching algorithm called FSMDTW is designed. Experiments conducted on both real and synthetic datasets show that the proposed algorithm is about 2.6 times faster than SOTA on short and medium-length queries and up to one order of magnitude faster on longer queries.

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 43.72% compared with the most commonly used relational stores.

Existing knowledge graph stores are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating data, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Graph stores are efficient in processing complex knowledge graph queries, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine that when we transfer which data partitions from relational store to graph store. To address this problem, we derive a physical design tuner DOTIL based on reinforcement learning. Experimental results demonstrate that the dual-store structure improves query performance up to average 50.11% compared with the most commonly used relational stores.

The great amount and complex structure of graph data bring a big challenge to graph data management. However, traditional management approaches cannot tackle the challenge. Fortunately, reinforcement learning provides a new approach to solve this problem due to its automation and adaptivity in decision making. Motivated by this, we develop April, an automatic graph data management system, which performs storage structure selection, index selection, and query optimization based on reinforcement learning. The system selects storage structure, indices effectively and automatically, and optimizes the SPARQL queries efficiently. April also offers a friendly interface for users, which allows users to interact with the system in a customized mode. We demonstrate the effectiveness and efficiency of April with two graph data benchmarks.

Traditional pixel-based classification methods yield poor results when applied to SAR imagery because of the presence of speckle and limited information in backscatter coefficients. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for the classification of polarimetric SAR data (PolSAR). The polarimetric target decomposition is aimed at extracting physical information related to the scattering mechanism of targets for the classification of scattering data. The main purposes of the object-oriented image analysis are delineating objects and extracting various spatial and textural features. The decision tree algorithm provides an efficient way to select features and create a decision tree for the classification. A comparison between the proposed method and the Wishart supervised classification was made. The overall accuracies of these two methods were 89.34% and 79.36%, respectively. The results show that the proposed method is an effective method for the classification of PolSAR data.