TNLRS: Target-Aware Non-local Low-Rank Modeling with Saliency Filtering Regularization for Infrared Small Target Detection

Recently, infrared small target detection problem has attracted substantial attention. Many works based on local low-rank model have been proven to be very successful for enhancing the discriminability during detection. However, these methods construct patches by traversing local images and ignore the correlations among different patches. Although the calculation is simplified, some texture information of the target is ignored, and targets of arbitrary forms cannot be accurately identified. In this paper, a novel target-aware method based on a non-local low-rank model and saliency filter regularization is proposed, with which the newly proposed detection framework can be tailored as a non-convex optimization problem, therein enabling joint target saliency learning in a lower dimensional discriminative manifold. More specifically, non-local patch construction is applied for the proposed target-aware low-rank model. By combining similar patches, we reconstruct them together to achieve a better generalization of non-local spatial sparsity constraints. Furthermore, to encourage target saliency learning, our proposed saliency filtering regularization term based on entropy is restricted to lie between the background and foreground. The regularization of the saliency filtering locally preserves the contexts from the target and surrounding areas and avoids the deviated approximation of the low-rank matrix. Finally, a unified optimization framework is proposed and solved with the alternative direction multiplier method (ADMM). Experimental evaluations of real infrared images demonstrate that the proposed method is more robust under different complex scenes compared with some state-of-the-art methodsacceptedVersion1057-7149 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information

Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning

Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.Comment: 4 pages, 6 figures, 2024 5th International Conference on Information Science, Parallel and Distributed System

Applications of Explainable AI in Natural Language Processing

This paper investigates and discusses the applications of explainable AI in natural language processing. It first analyzes the importance and current state of AI in natural language processing, then focuses on the role and advantages of explainable AI technology in this field. It compares explainable AI with traditional AI from various angles and elucidates the unique value of explainable AI in natural language processing. On this basis, suggestions for further improvements and applications of explainable AI are proposed to advance the field of natural language processing. Finally, the potential prospects and challenges of explainable AI in natural language processing are summarized, and future research directions are envisaged. Through this study, a better understanding and application of explainable AI technology can be achieved, providing beneficial references for the development of the natural language processing field

Treatment of lumbosacral spinal tuberculosis by one-stage anterior debridement and fusion combined with dual screw-rod anterior instrumentation underneath the iliac vessel

BACKGROUND: There has been no consensus regarding what is the optimal means of treating lumbosacral segment tuberculosis. The aim of this study was to evaluate the clinical outcomes of our newly developed one-stage anterior debridement and fusion combined with dual screw-rod construct anterior instrument underneath the iliac vessels for lumbosacral spinal tuberculosis. METHODS: We retrospectively reviewed 22 patients with lumbosacral spinal tuberculosis who underwent one-stage anterior debridement and fusion combined with dual screw-rod anterior instrument underneath the iliac vessels between January 2004 and June 2013. We assessed the visual analogue scale (VAS), erythrocyte sedimentation rates (ESR), neurological performance, kyphotic angles, fusion rates, and computed tomographic angiography (CTA) before and after surgery. RESULTS: All patients were followed-up for a mean of 46.59 months. There were no instances of spinal tuberculosis recurrence. The mean VAS scores and ESR decreased significantly from the preoperative levels both postoperatively and at the final follow-up (all P <0.001). The mean kyphotic angle significantly increased from the mean preoperative angle both postoperatively and at the final follow-up (both P <0.001). All patients had bone fusion at a mean of five months after surgery. No postoperative vascular complications were observed. CONCLUSIONS: Our findings suggest that anterior radical debridement, fusion combined with dual screw-rod anterior instrument underneath the iliac vessels can be an effective and safe treatment option for lumbosacral segment tuberculosis

Automatic News Generation and Fact-Checking System Based on Language Processing

This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rapid development of Natural Language Processing (NLP) and deep learning technologies, automatic news generation systems are capable of extracting key information from massive data and generating well-structured, fluent news articles. Meanwhile, by integrating fact-checking technology, the system can effectively prevent the spread of false news and improve the accuracy and credibility of news. This study details the key technologies involved in automatic news generation and factchecking, including text generation, information extraction, and the application of knowledge graphs, and validates the effectiveness of these technologies through experiments. Additionally, the paper discusses the future development directions of automatic news generation and fact-checking systems, emphasizing the importance of further integration and innovation of technologies. The results show that with continuous technological optimization and practical application, these systems will play an increasingly important role in the future news industry, providing more efficient and reliable news services

Noise reduction performance and maintenance time of porous asphalt pavement

The noise reduction performance of porous asphalt (PA) pavement is significantly influenced by the presence of air voids, which act as sound absorbers. However, factors such as traffic-induced compaction and clogging due to the accumulation of dust, sand, and debris can alter the morphology and quantity of these voids, consequently compromising noise reduction effectiveness. In this study, a comprehensive investigation into the noise reduction capabilities and maintenance requirements of porous asphalt pavement was conducted by a novel approach. Initially, a three-dimensional structural model of the PA mixture was constructed using X-ray computed tomography. Subsequently, this model was integrated into finite element analysis software to develop a “tire-pavement-air” coupled model. It is important to note that in our numerical approach, we have made several simplifications. For instance, the “tire-pavement-air” coupled model was simplified within the air model is set to fully absorbing boundary condition (*Nonreflecting), and the pavement material was assumed to be linear elastic to ensure computational efficiency and convergence of the model. The findings revealed that as the air-void content increased, the average sound pressure level (SPL) of tire/pavement noise exhibited a gradual decrease. Furthermore, when air voids became obstructed due to factors like rainwater and clogging materials, the SPL of tire/pavement noise initially increased with rising air-void content before subsequently decreasing. Notably, porous asphalt with an air-void content of 20 % demonstrated relatively effective noise reduction capabilities. Based on simulation results obtained under varying conditions, including rainfall and clogging, critical thresholds for air-void content were identified to determine recommended maintenance time for PA pavements. Specifically, an air-void content of 17 % was proposed as the threshold for cleaning maintenance, while a content of 14 % was suggested as an indicator for failure alert, respectively. These findings contribute valuable insights into optimizing the design and maintenance strategies for porous asphalt pavements to enhance their noise reduction performance and prolong their functional lifespan

Artificial disc and vertebra system: a novel motion preservation device for cervical spinal disease after vertebral corpectomy

OBJECTIVE: To determine the range of motion and stability of the human cadaveric cervical spine after the implantation of a novel artificial disc and vertebra system by comparing an intact group and a fusion group. METHODS: Biomechanical tests were conducted on 18 human cadaveric cervical specimens. The range of motion and the stability index range of motion were measured to study the function and stability of the artificial disc and vertebra system of the intact group compared with the fusion group. RESULTS: In all cases, the artificial disc and vertebra system maintained intervertebral motion and reestablished vertebral height at the operative level. After its implantation, there was no significant difference in the range of motion (ROM) of C3-7 in all directions in the non-fusion group compared with the intact group (p>;0.05), but significant differences were detected in flexion, extension and axial rotation compared with the fusion group (