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Research on financial data trend prediction based on deep learning 2

Research on financial data trend prediction based on deep le...

引用

2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Niu, Yue Lanzhou Vocational Technical College Lanzhou China

ISBN: (纸本)9781510689053

The complex market behavior mechanism makes the financial time series have the characteristics of noise, low signal-to-noise ratio, non-linearity and non-stationarity, and fluctuation agglomeration, which makes the traditional econometric forecasting model have great challenges in predicting the trend of financial data. It is of great practical significance and theoretical value to use neural network algorithm and deep learning technology to predict the time series of financial market. Therefore, based on the deep learning model LSTM(Long Short-Term Memory), this paper introduces the VAT(Virtual Adversarial Training) regularization method which can increase the robustness of the model, and proposes the VAT-LSTM financial data trend prediction model to solve the problems of nonlinear distribution characteristics and generalization error in financial data. Through the empirical analysis of the Shanghai Composite Index data from 2015 to 2022, it is found that the model performs well in short-term, medium-term and long-term prediction. Compared with the classical time series prediction models such as LSTM, RNN, GRU, BP, SVR and ARIMA, it has higher prediction accuracy. It is suitable as a prediction model for financial data trends and can be extended to other financial research fields. © 2025 SPIE.

关键词： Prediction models

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Modeling Quadcopter Stabilization 2nd

Modeling Quadcopter Stabilization

引用

2nd international conference on Information Technologies and Their Applications

作者： Pashayev, Adalat Sabziev, Elkhan Inst Control Syst AZ-1141 Baku Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

Problems of control of unmanned aerial vehicles (UAVs) are investigated. The problem of developing an automatic controller to maintain the spatial position of UAVs is addressed. The spatial position here is altitude, yaw, roll and pitch angles. It is assumed that the initial information comes from the MPU6000 type sensors. An approach to solving the problem of signal denoising and tuning is proposed. Sensors of this type output loads (accelerometers) and angular velocities. To calculate the current angle, it is necessary to integrate the angular velocities. In doing so, it becomes necessary to solve two problems. First, angular velocities are measured in the local coordinate system, while the spatial position of the UAV is calculated relative to the global coordinate system. Second, when integrating angular velocities, the effect of error accumulation occurs. The possibilities of refining gyroscope readings based on accelerometer readings are investigated. A simulation program is developed on the basis of a mathematical model of quadcopter motion. The source of forces and torques acting on the system is the Earth's gravity, the thrust force and torques generated by the motors, and the drag force. Numerical methods are used to solve the system of equations. A PD controller is proposed to maintain the spatial position of the quadcopter. The coefficients are found experimentally. The proposed approach is verified in a computer simulation program.

关键词： UAV PD controller MPU6000 flight stabilization orientation angles sensor data processing regulator filter

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Deep Learning-Based signal processing and Prediction for Gas Pressure Regulating Stations 2

Deep Learning-Based Signal Processing and Prediction for Gas...

引用

2nd international conference on the Frontiers of Intelligent Control, Measurement, and signal Systems, FICMSS 2025

作者： Jie, Hao Yuan, Chang Liu, Hongrui Liu, Qinghua Qiu, Xiulin Zhang, Ming Zhao, Mengdi School of Computer Jiangsu University of Science and Technology JiangSu China School of Automation Jiangsu University of Science and Technology JiangSu China Department of Materials Science and Engineering Suzhou University of Science and Technology JiangSu China

To improve the accuracy of load predictions in pressure regulation stations, this research presents a novel deep learning approach for signal analysis, based on a UNet-Bidirectional Long Short-Term Memory-Attention architecture. The framework starts with an extensive feature extraction from the input data using its convolutional component. Following this, it identifies the dependencies among data elements by employing a self-attention mechanism. The processed data is then fed into a Bidirectional Long Short-Term Memory layer (BiLSTM), where temporal analysis and forecasting are performed. Finally, the model’s predictions are enhanced through a further application of the attention mechanism. Comparative tests reveal that the model possesses superior learning capabilities and predictive accuracy, offering a significant improvement over current benchmark models. © 2025 Institute of Physics Publishing. All rights reserved.

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Proceedings - 2022 2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

Proceedings - 2022 2nd International Signal Processing, Comm...

引用

2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

ISBN: (纸本)9781665492713

The proceedings contain 76 papers. The topics discussed include: an intelligent blind glasses system based on stair & step recognition function;application of a new digital twin model in infrastructure construction;modeling and analysis of spatial-temporal relationship and risk evolution of emergencies based on big data from social media;safety monitoring system of south- to-north water diversion construction project based on big-data;predicting atmospheric humidity using artificial neural networks and BeiDou satellite navigation data;task offload optimization management of networked vehicles in edge computing environment;connected vehicle path planning and resource allocation in edge computing environment;study on influence of atmospheric turbulence over sea on laser communication between the ships;blockchain data privacy protection method based on intelligent collaborative computing;design of archives management information system based on blockchain technology;evaluation for warship satellite communication system;and evaluation method of wireless network communication anti-jamming performance based on rough set.

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2022 2nd international conference on Artificial Intelligence and signal processing, AISP 2022

2022 2nd International Conference on Artificial Intelligence...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

ISBN: (纸本)9781665442909

The proceedings contain 108 papers. The topics discussed include: adaptive super-resolution on-chip imaging;a new dynamic method of multiprocessor scheduling using modified crow search optimization;metamaterial loaded miniaturized microstrip annular slot antenna with increased gain;hiding sensitive information in surveillance video without affecting nefarious activity detection;a study on secret data sharing through coverless steganography;design of a state derivative optimal control law using LMI technique for diving motion of autonomous underwater vehicle;forecasting carbon dioxide levels using autoregressive integrated moving average model;extended packet combining schemes for error control in wireless networks;performance evaluation of speech radio band extension technique using Simulink;and enhancement of higher order modes using characteristic mode analysis.

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Parallelized Collision Detection Algorithm for Non-contact Machining Simulation Based on Space Partitioning and Path Point Equalization 2

Parallelized Collision Detection Algorithm for Non-contact M...

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2nd international conference on Engineering of Optical Systems and Precision Instruments, ICEOP 2024

作者： Zhang, Ying Zhang, Shijin Ren, Zhongwei Wu, Yuqiang Jiang, Fengyang School of Software Northwestern Polytechnical University Xi’an710129 China AVIC United Technology Center for Multi-axis CNC Machining and Simulation AVIC Information Technology Co. Ltd. Beijing100020 China Suzhou Key Lab of Aerospace Industry Software and Data Science Yangtze River Delta Research Institute of NPU Taicang215400 China

High-energy beam CNC machining, such as laser, water jet, and plasma machining, is widely applied in modern manufacturing due to its non-contact processing, precise energy control, and broad adaptability. However, potential collisions between tools and machine tools can cause serious accidents, making pre-processing simulation *** tasks become more complex, the increase in triangular facets of models and tool path points leads to a significant rise in computational overhead for CNC machining simulations. Although multi-core processors provide powerful computing capabilities, traditional serial collision detection methods fail to fully utilize these *** address this issue, we propose a parallelized collision detection algorithm for high-energy beam CNC machining simulations. By dividing the tool paths into sub-tasks and leveraging multi-core processors for concurrent processing, the algorithm enhances efficiency while maintaining accuracy. Experiments in water jet and laser machining scenarios demonstrate that our approach effectively reduces computation time and improves real-time performance and *** study introduces parallel computing strategies to improve collision detection efficiency, combines the parallelization idea with the collision detection algorithm in CNC machining simulation offering an efficient solution for complex high-energy beam machining tasks. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Laser beam machining

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Enhancing data Utility in Personalized Differential Privacy: A Fine-Grained processing Approach 2nd

Enhancing Data Utility in Personalized Differential Privacy...

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2nd international conference on data Security and Privacy Protection, DSPP 2024

作者： Liu, Zhenhua Wang, Wenxin Liang, Han Yuan, Yujing School of Mathematics and Statistics Xidian University Xi’an710071 China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China

ISBN: (纸本)9789819785452

Personalized differential privacy (PDP) offers accurate privacy protection by considering individual differences and personalized privacy preferences, making it suitable for various data release scenarios. However, in traditional PDP mechanism, the coarse-grained personalized sampling method often brings excessive randomness, leading to large sampling errors and low data utility. In this paper, we present a novel mechanism termed as the fine-grained personalized differential privacy (FG-PDP) mechanism, which performs approximating and filtering processes on the un-sampled dataset after the personalized sampling phase within the PDP framework. Then the refined mechanism integrates the newly processed dataset with the sampled dataset and performs noise processing. Furthermore, the privacy and utility analysis shows that the proposed mechanism effectively meets privacy requirements and maximizes the use of un-sampled data, thereby reducing sampling errors and boosting data utility. Finally, the simulating experiments are performed on both synthetic and real-world datasets to compare the FG-PDP mechanism with traditional mechanism. The experimental results show that the proposed mechanism provides significantly enhanced query result accuracy under the same privacy budget. In real-world dataset, the maximum error of the proposed mechanism is smaller than the minimum error of the PDP mechanism when evaluated under average function, while simultaneous exhibiting robust performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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In-depth development of temperature insensitive refractive index Fabry-Perot optical sensor 2

In-depth development of temperature insensitive refractive i...

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2nd international conference on Optical Communication and Optical Information processing, OCOIP 2024

作者： Gao, Yuda Luo, Yanhua Institute of Fiber Optics Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Sensors based on optical fiber interference structures offer benefits such as high sensitivity, simple fabrication and good compatibility, and are widely used in fields like biological monitoring, chemical engineering, and environmental monitoring. However, these sensors often face unavoidable cross-sensitivity issues between temperature and refractive index. Therefore, developing a new optical fiber refractive index sensor that is insensitive to temperature and has a broad refractive index measurement range remains a significant research issue. In this work, proposing a temperature-insensitive refractive index sensor featuring a two-stage Fabry-Perot (F-P) cavity made from single-mode fiber (SMF), capillary fiber (CF), and no core fiber (NCF) based on the principle of mode interference. The research experimentally investigates the sensor's sensitivity to temperature and refractive index. Results indicate that the sensor's amplitude correlates linearly with the refractive index in both optical and Fourier domains, covering a refractive index range of 1.3164 to 1.4215. In the Fourier domains, sensitivities of - 0.7184/refractive index unit (RIU) and - 0.2672/RIU are recorded at two distinct positions, respectively. A direct relationship between the amplitude and the refractive index is established. Additionally, the proposed sensor is almost insensitive to temperature in both the wavelength and Fourier domains. At the same time, experimental validation using water, ethanol, isopropanol, and glycerin solutions with varying refractive indexes, yielding results that align well with the established refractive index relationship. © 2025 SPIE.

关键词： Fabry-Perot interferometers

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Intelligent greenhouse motor real-time monitoring system based on McgsPro configuration 2

Intelligent greenhouse motor real-time monitoring system bas...

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2nd international conference on the Frontiers of Intelligent Control, Measurement, and signal Systems, FICMSS 2025

作者： Wang, Jiping Wang, Tianyun Wang, He Deng, Dachong Xu, Wensong Nanjing University of Science and Technology ZiJin College No.89 Wenlan Road Nanjing210023 China Ningbo Kaogongji Product Innovation Co. Ltd China China Telecom Nanjing Branch No. 2 ZhongYang RD XuanWu District Nanjing City China

With the continuous development of intelligent greenhouses, people all hope to appear a kind of intelligent greenhouses that can realize unmanned supervision. In this paper, an intelligent greenhouse motor condition monitoring system based on McgsPro is proposed. The system collects the operating data of each motor in the greenhouse in real time through the sensor network, including key parameters such as temperature, torque and speed, and sends the data to PLC. After signal processing and data analysis by PLC, the processed data is sent to McgsPro. McgsPro then conducts real-time evaluation and prediction of the operating state of the motor, finds potential faults in time and issues early warnings to ensure the efficient and stable operation of the intelligent greenhouse automatic control system. The application of the system not only improves the precision and reliability of the environmental control in the greenhouse, but also reduces the maintenance cost, and provides effective support for the intelligent management of modern agriculture. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Automatic guidance (agricultural machinery)

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Multi-Factor Prediction of Welding Deformation of Pump Truck Arm Based on Hybrid Deep Learning Model 2

Multi-Factor Prediction of Welding Deformation of Pump Truck...

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2nd international conference on the Frontiers of Intelligent Control, Measurement, and signal Systems, FICMSS 2025

作者： Cao, Shen Liu, Zehui Chen, Weitao Hunan International Economics University Changsha410205 China College of Mechanical and Electrical Engineering Shihezi University Shihezi832003 China

During the welding process of the pump truck arm, due to the influence of multiple factors such as materials, processes, and structures, deformation is often difficult to control, affecting the welding quality and subsequent processing accuracy. Therefore, how to accurately predict welding deformation and formulate effective control measures has become a difficult problem that needs to be solved in the engineering field. To this end, this paper analyzes the multi-factor characteristics of pump truck arm welding deformation, identifies the main factors affecting deformation, including welding heat input, cooling rate, material properties and process parameters, and this paper uses a combination of CNN and LSTM networks to predict welding. First, CNN is used to extract features from image data of the welding process. Then LSTM is used to capture dynamic changes in time series data. Finally, the advantages of these two models are combined to optimize prediction through an integrated learning method. Specifically, the mean squared error (MSE) of the hybrid model is 0.023, and the root mean squared error (RMSE) is 0.151, which is significantly better than the DNN and GRU models. In addition, the model maintains high prediction accuracy under noise interference and has good real-time response capability. In the above data conclusions, the hybrid deep learning model based on CNN and LSTM can effectively handle the multiple factors affecting the welding process, provide more accurate and stable welding deformation prediction results, and provide strong support for the precise control and quality improvement of the pump truck arm welding process. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Deep learning

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