Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm

In view of the improvement measures of the optimization control algorithm for the ignition system of the hydrogen-fueled engine, the L-M neural network algorithm, Powell neural network algorithm and the traditional BP neural network algorithm are used to optimize the ignition system. The results showed that L-M algorithm not only can accurately predict the hydrogen-fueled engine ignition timing, but also has high precision, high convergence speed, a simple model and other outstanding advantages in the training process, which can greatly reduce the workload of human engine bench tests. Only a small amount of engine bench test is carried out, and the obtained sample data can be used to predict the ignition timing under the whole working conditions. The mean square error of the optimization results based on L-M algorithm arrives at 0.0028 after 100 times of calculation, the maximum value of absolute error arrives at 0.2454, and the minimum value of absolute error arrives at 0.00426

A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering

The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction

AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents

Evaluating large language models (LLMs) as general-purpose agents is essential for understanding their capabilities and facilitating their integration into practical applications. However, the evaluation process presents substantial challenges. A primary obstacle is the benchmarking of agent performance across diverse scenarios within a unified framework, especially in maintaining partially-observable environments and ensuring multi-round interactions. Moreover, current evaluation frameworks mostly focus on the final success rate, revealing few insights during the process and failing to provide a deep understanding of the model abilities. To address these challenges, we introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents. AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit that features easy assessment of agents for multi-faceted analysis through interactive visualization. This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront. Ultimately, AgentBoard serves as a significant step towards demystifying agent behaviors and accelerating the development of stronger LLM agents.Comment: Preprin

Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations

Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where any object in the reconstructed images is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Further, leveraging the insensitivity of high-frequency components to human vision, we introduce Imperceptibility Constraint (IC) to ensure that the perturbations remain inconspicuous. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness. In addition, we provide several findings and suggestions for designing future defenses.Comment: 7 page