Journal of Engineering Innovations & Technology

Aiming at the problem of accuracy degradation of laser velocimetry signals in high-speed flight scenarios due to complex noise interference, this study constructs a frequency estimation method based on the noise feature analysis of Butterworth filter and multi-domain spectrum fusion. Through the quantitative analysis of noise mean, variance and power spectral density, combined with the joint spectral analysis of Fourier Transform (FT) and Discrete Fourier Transform (DFT), the high-precision frequency estimation of noise-containing signals is achieved in MATLAB simulation environment, and the estimated frequency is 40999836.00Hz, with the relative error less than 0.01%. The proposed algorithm improves the signal-to-noise ratio by more than 35% while maintaining the signal integrity, which provides a reliable theoretical and methodological support for the noise suppression and parameter measurement of the laser speed measurement system under the high dynamic flight environment.

This paper proposes an accurate positioning method for rocket debris based on genetic algorithm. The rocket debris will produce supersonic sonic boom in the process of falling, this paper realises the high precision positioning of the sonic boom position and time of the rocket debris by arranging multiple vibration wave monitoring devices in the debris theoretical fallout area, constructing a set of multilateral measurement equations by using the difference in the arrival time of the sonic boom, and introducing genetic algorithms to optimally solve the hyperbolic equations to achieve high precision positioning of the sonic boom position and time of the rocket debris. The results show that the genetic algorithm can effectively avoid the local optimum problem of the traditional method and significantly improve the positioning accuracy. In addition, this paper also explores the error analysis model and the optimisation method of monitoring equipment arrangement, which further improves the reliability and adaptability of the positioning system.

With the rapid development and increased complexity of global financial markets, the effectiveness of risk management becomes more and more important. In this paper, a Bayesian network-based financial market risk prediction and decision support system (DSS) is proposed to enhance the accuracy and efficiency of financial decision making. The system architecture includes an input data module, an inference engine, and a user interaction interface, emphasizing data integration and real-time processing capabilities. The input module is responsible for collecting real-time and historical financial data, and the inference engine utilizes Bayesian inference algorithms to assess risks and generate decision recommendations. It is shown that the system is able to effectively capture the complex dependencies of variables in the financial market, provide accurate risk prediction, and thus help decision makers to make scientific judgments in a dynamic environment.

In addition, the user interaction interface is designed to make the system more user-friendly for quick understanding and application of risk assessment results. The integration and maintenance strategy of the system ensures the stability and scalability of the DSS and lays the foundation for future functionality enhancements. Despite the positive results of this study in financial market risk management, there are still some challenges, such as the dependence on the quality of input data and the increase in computational complexity. Future research could explore the integration with other machine learning techniques to further enhance the prediction capabilities

As a key link in the global supply chain, the aviation logistics supply chain is of great significance in protecting the security and development of China's supply chain, and in promoting the economy of air cargo and civil aviation. In recent years, the aviation logistics industry has been driven by both technological innovation and market demand, and has both opportunities and challenges. In order to make the aviation logistics supply chain more resilient when encountering risks and shocks, this paper constructs a set of factors influencing the resilience of aviation logistics supply chain, adopts the DEMATEL-ISM model, analyzes the causality and hierarchical structure between the resilience influencing factors, and puts forward the strategic suggestions to maintain the resilience of aviation logistics supply chain with respect to the important influencing factors, so as to enrich the related research in the field of resilience of aviation logistics supply chain.

In the context of globalization and rapid development of e-commerce, cross-border air logistics still becomes an important factor restricting cross-border e-commerce. On the basis of analyzing the constituent elements and functions of cross-border air logistics system as well as the multiple structures of cross-border air logistics system, this paper constructs a four-dimensional conceptual model based on Hall's three-dimensional model, and discusses in detail the dimensions of the model as well as the role modes between the dimensions, which provides theoretical foundations and practical guidance for the construction and optimization of cross-border air logistics system in China.

The Parlung Zangbo River Basin, located in the Tibet region, is a typical area prone to frequent debris flows. As a highly destructive geological hazard, debris flows pose a serious threat to the ecological environment and human activities in this basin. This paper aims to comprehensively assess the risk factors of debris flow in the Parlung Zangbo River Basin by integrating Geographic Information System (GIS) and Analytic Hierarchy Process (AHP), and to generate a debris flow risk zoning map. Firstly, the paper analyzes the main factors influencing the occurrence of debris flows in the basin, including topographic features (such as slope aspect, slope gradient, and Melton ratio), precipitation intensity, vegetation cover, and the nearest distance to faults. Based on this analysis, the AHP model is employed to assign weights to these factors and conduct consistency checks to ensure the scientific validity and reliability of the assessment results. The study results indicate that the Melton ratio, precipitation index, and slope aspect and gradient are the primary factors affecting the occurrence of debris flows, with varying degrees of influence across different areas. By applying the weights of these factors to the GIS platform, this study generates a debris flow risk zoning map for the Parlung Zangbo River Basin, providing a scientific basis for disaster prevention and planning in the region. The findings of this study not only hold significant implications for disaster control in the Parlung Zangbo River Basin but also offer valuable insights for debris flow risk assessment in similar geological environments.

This study explores the application and comparison of the Minimum Steiner Tree (STP) algorithm and the Minimum Spanning Tree (MST) algorithm in optimizing the communication and transportation network in the central area of Kanazawa City. Through a detailed analysis of the classic STP problem and related algorithms, this paper proposes an optimized Ant Colony Optimization algorithm (IACO) and verifies its efficiency on standard datasets. The results show that this algorithm significantly reduces the total connection cost and improves network efficiency and robustness when connecting key nodes. Therefore, this paper suggests that the IACO algorithm has significant application value in urban network planning and recommends further exploration of its potential in larger and more complex networks to optimize the design of urban communication and transportation networks.

This paper aims to investigate the analysis of audio waveforms, spectrograms, amplitude spectra after Fast Fourier Transform (FFT), and Mel spectrograms of different genres of music in the Python programming environment. By quantifying music, the paper seeks to achieve classification of various music genres. Regression models in machine learning are employed to analyze the amplitude spectra and classify different genres of music. The paper trains the model using several common music genres, validates its feasibility through testing, and concludes by applying the insights from the analysis to guide the composition of music and classify musical compositions.

In network system construction, the issue of network traffic security is of paramount importance. Against the backdrop of strengthening national cybersecurity, traffic inspection plays a significant role in continuously enhancing the security capabilities of network systems. However, as more and more traffic is transmitted using the HTTP protocol over SSL/TLS encryption, this not only provides protection for legitimate communications but also facilitates covert attacks by malicious actors. Traditional traffic detection systems (such as Snort and Suricata) struggle with the challenge of inspecting encrypted traffic, and their detection rules are typically configured based on known attack patterns, making them less effective against evolving new types of attacks.

To address the aforementioned issues, this paper innovatively proposes a comprehensive traffic detection frame. The main contributions of this work are summarized in three parts:

1.Real-time Traffic Decryption and Detection Framework: We have designed and implemented an efficient real-time traffic decryption and detection architecture that cleverly integrates man-in-the-middle proxy technology to enable real-time decryption of HTTPS encrypted traffic.

2. Model Training and Validation Using an SQL Injection Dataset: To validate the effectiveness of the framework, we utilized a dataset containing SQL injection attacks to train the detection model and applied it within the framework to evaluate its capability to detect unknown attack traffic.

3. For the development and testing of the SnortML plugin: To enable real-time traffic feature extraction in SnortML, we added feature extraction capabilities to the SnortML detector and used the tcpreplay tool to simulate complex network traffic scenarios.

Through our research, we have achieved automatic detection and efficient defense against real-time, encrypted, and potentially unknown attack traffic. Experimental result clearly demonstrate that the framework has significantly improved the accuracy of encrypted traffic detection, providing strong technical support and innovative ideas for building a safer and more robust network environment.

This paper focuses on predicting the development of new energy vehicles (NEVs) using random forest model and gray Prediction models. New energy vehicles, including hybrid, pure electric, and fuel cell electric vehicles, have seen rapid growth due to their low pollution, low energy consumption, and strong peak load capacity. This research aims to analyze the factors influencing the NEV industry and forecast its future development. The gray Prediction method, known for addressing small sample sizes and incomplete data, is used to forecast the conservative quantity of NEVs in this paper. Meanwhile, the random forest model regression model evaluates the impact of various factors on the market share of traditional fuel vehicles. Key variables include production, fuel prices, charging stations, subsidies, and sales volumes. Results indicate that the NEV market in China will experience rapid growth over the next decade, with increasing market penetration and sales. Factors such as government subsidies and technological advancements significantly influence the traditional fuel vehicle market.