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Model Predictive Guidance: A Fixed-Point algorithm

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 723-735页

作者： Lu, Ping Davami, Christopher San Diego State Univ Dept Aerosp Engn San Diego CA 92182 USA

Tracking a reference trajectory is a frequent task in aerospace guidance. The technical approaches for tracking guidance design so far are largely application-dependent and heavily rely on labor-intensive practices such as manual gain generation. In this paper the model predictive control (MPC) method is used as the foundation for a systematic tracking guidance design, dubbed appropriately model predictive guidance (MPG). Unlike most existing work utilizing MPC, the method developed in this paper does not require a quadratic program (QP) solver. Instead, the constrained QP from the discretization of the MPC problem is strategically formulated in a novel way so that it has only box constraints. An exceedingly simple but globally convergent fixed-point algorithm with a theoretical guarantee is developed to solve the QP. The relatively general formulation of the MPG problem, the automated tracking command generation, and the very simple yet highly reliable algorithm render this method appealing to many tracking guidance problems and autonomous operations. The application of MPG is demonstrated in planetary powered descent guidance for a high-mass Mars mission.

关键词： optimization algorithm Powered Descent Guidance Spacecraft Guidance Autonomous Guidance and Control Convex optimization Derivative Free optimization Numerical optimization

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optimization algorithms in wireless monitoring networks: A survey

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NEUROCOMPUTING 2022年第0期489卷 584-598页

作者： Xia, Na Wang, Cong Peng, Huaizhen Zhao, Zhongqiu Chen, Yuqing Wang, Peipei Du, Huazheng Ding, Sheng Yu, Yongtang Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China China Jikan Res Inst Engn Invest & Design Co Ltd Xian 710043 Peoples R China

Wireless network has emancipated people from the bondage of wired network and enhanced the quality of human life. However, there remain a few areas pertaining to wireless network that require attention, such as network congestion, low communication reliability and security. With the increasing scale and extended applications of Internet of Things (IoT), there is a growing demand for reliability, stability and security within the network. Wireless network monitoring is an effective system put in place which involves distributed sniffers that capture the transmitted data of wireless users, facilitating status analysis, fault diagnosis and resource management of the network system. Due to the limited number of sniffers, optimization of hardware configuration and channel assignment of sniffers is paramount, which can significantly improve the amount of captured data and the monitoring quality of the wireless network. Primarily, the concept, classification and characteristics of wireless network monitoring are introduced. Secondarily, the application of optimization algorithms in wireless network monitoring, particularly the channel selection algorithms during the data collection process and the channels and time-slot scheduling algorithms during the data aggregation process are summarized. Finally, the challenges faced when building a wireless network monitoring are discussed and to conclude prospects aimed towards the development of this research field are put forward.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Channel assignment optimization algorithm Wireless sniffer Wireless network monitoring

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Machine learning-based strength prediction for circular concrete-filled double-skin steel tubular columns under axial compression

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ENGINEERING STRUCTURES 2025年 325卷

作者： Li, Shou-Zhen Wang, Jin-Jin Jiang, Liming Deng, Ran Wang, Yu-Hang Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400000 Peoples R China Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

Circular concrete-filled double skin steel tubular (CFDST) columns show great potential in various infrastructures due to their excellent structural performance and easy construction process. As the rapid development of infrastructure towards large scales and manifold functions, CFDST columns within an extended scope of dimensions and sectional configurations will be applied in engineering practice. This trend poses challenges to both the accuracy and efficiency of traditional strength prediction methods including design equations, experimental fitting and numerical simulations. Based on experimental datasets obtained from existing research, machine learning (ML) methods pave a path for understanding the complex relations between key parameters and the axial compressive ultimate strength of CFDST columns. This paper attempts to develop reliable ML-based models for predicting the axial compressive ultimate strength of CFDST columns. Three advanced ML algorithms, namely Back Propagation Neural Network (BPNN), Support Vector Regression (SVR) and Gaussian Process Regression (GPR), were adopted to develop prediction models by training an experimental database of 162 CFDST specimens from existing literature which covers a wide scope of parameters including high diameter-to-thickness ratios and large hollow ratios. Particularly, Particle Swarm optimization (PSO) method was used to optimize the hyperparameters of ML algorithms for enhancing the prediction accuracy. The performances of the developed prediction models were evaluated through an in-depth comparison analysis. It is found that the ML-based models could predict the strengths of CFDST columns with higher accuracies and wider applicable ranges than existing design methods;PSO-enhanced GPR (PSO-GPR) model achieved the most improved performance for strength prediction. Further validation on PSO-GPR model was conducted based on experimental data from recently tested large-dimension CFDST specimens and the results demon

关键词： Ultimate strength prediction Machine learning optimization algorithm CFDST Parameter effects

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A new fractional Abel polynomials neural network method for nonlinear models of heat transfer

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 162卷

作者： Hassani, Hossein Avazzadeh, Zakieh Turan-Dincel, Arzu Katani, Roghayeh Anand Int Coll Engn Dept Math Jaipur 303012 Rajasthan India Anhui Univ Stony Brook Inst Hefei 230601 Anhui Peoples R China Yildiz Tech Univ Dept Math Engn TR-34220 Istanbul Turkiye Univ Yasuj Fac Sci Yasuj Iran Univ South Africa Dept Math Sci Florida South Africa

Nowadays, the study and advancements in neural networks and optimization techniques are the most interesting research topics. This progress has empowered researchers to address more intricate and realistic challenges associated with nonlinear models. This research presents a new approach called the fractional Abel polynomials neural network (FAPNN) for solving nonlinear heat transfer models. The mentioned NN consists of three layers (input layer, hidden layer, output layer). Fractional Abel and arcsinh(t) functions have been used as activation functions in the hidden and output layers, respectively, to construct the FAPNN. To mitigate storage and computational costs, new operational matrices (OMs) are devised. Based on FAPNN and OMs for fractional Abel polynomials, a series solution is achieved for the nonlinear heat transfer models. The Lagrange multipliers method is adopted so that the nonlinear heat transfer models can be transformed into a class of nonlinear algebraic system of equations and we solve these equations using Matlab and Maple software. Ultimately, four sets of numerical examples are provided along with comparisons from the literature such as the variational iteration, homotopy perturbation, differential transformation, and Chebyshev wavelets methods, to confirm the effectiveness of the suggested FAPNN approach. Experimental results demonstrate the effectiveness of our proposed method.

关键词： Fractional Abel polynomials neural network Optimal solutions Nonlinear models of heat transfer optimization algorithm

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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning algorithm

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JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第4期13卷 767-767页

作者： Yang, Yang Hou, Huiwen Yao, Gang Wu, Bo Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Chongqing Jiaotong Univ Sch Civil Engn Chongqing 400074 Peoples R China

The span of a double-deck cross-sea bridge that can be used for both highway and railway purposes is usually 1 to 16 km. Compared with small-span bridges and single-layer main girder forms, its lightweight design and low damping characteristics make it more prone to vortex-induced vibration (VIV). To predict the VIV performance of a double-deck steel truss (DDST) girder with additional aerodynamic measures, the VIV response of a DDST bridge was investigated using wind tunnel tests and numerical simulation, a learning sample database was established with numerical simulation results, and a prediction model for the amplitude of the DDST girder and VIV parameters was established based on three machine learning algorithms. The optimization algorithm was selected using root mean square error (RMSE) and the coefficient of determination (R2) as evaluation indices and further improved with a genetic algorithm and particle swarm optimization. The results show that for the amplitude prediction of the main girder, the backpropagation neural network model is the most effective. The most improved algorithm yields an RMSE of 0.150 and an R2 of 0.9898. For the prediction of VIV parameters, the Random Forest model is the most effective. The RMSE values of the improved optimal algorithm are 0.017, 0.026, and 0.295, and the R2 values are 0.9421, 0.8875, and 0.9462. The prediction model is more efficient in terms of computational efficiency compared to the numerical simulation method.

关键词： vortex-induced vibration machine learning optimization algorithm additional aerodynamic measures double-deck steel truss girder prediction model

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Multiphase Iterative algorithm for Mixed-Integer Optimal Control

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 757-770页

作者： Pei, Chaoying You, Sixiong Di, Yu Dai, Ran Purdue Univ W Lafayette IN 47907 USA Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65409 USA Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Purdue Univ Dept Stat W Lafayette IN 47907 USA

Mixed-integer optimal control problems (MIOCPs) frequently arise in the domain of optimal control problems (OCPs) when decisions including integer variables are involved. However, existing state-of-the-art approaches for solving MIOCPs are often plagued by drawbacks such as high computational costs, low precision, and compromised optimality. In this study, we propose a novel multiphase scheme coupled with an iterative second-order cone programming (SOCP) algorithm to efficiently and effectively address these challenges in MIOCPs. In the first phase, we relax the discrete decision constraints and account for the terminal state constraints and certain path constraints by introducing them as penalty terms in the objective function. After formulating the problem as a quadratically constrained quadratic programming (QCQP) problem, we propose the iterative SOCP algorithm to solve general QCQPs. In the second phase, we reintroduce the discrete decision constraints to generate the final solution. We substantiate the efficacy of our proposed multiphase scheme and iterative SOCP algorithm through successful application to two practical MIOCPs in planetary exploration missions.

关键词： optimization algorithm Powered Descent Guidance Mixed Integer Optimal Control Quadratically Constrained Quadratic Programming

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Deep Belief-MobileNet1D: A novel deep learning approach for anomaly detection in industrial big data

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INTERNET OF THINGS 2025年 31卷

作者： Chen, Tzu-Chia Tamkang Univ Dept Artificial Intelligence New Taipei 251301 Taiwan

Early fault or unusual behavior detection can reduce the risk of equipment failure improve performance and increase safety. Anomaly detection in industrial big data involves identifying deviations from normal patterns in large-scale datasets. This method assists in preventing equipment failures optimizing maintenance schedules and raising overall operational efficiency in industrial settings by identifying anomalous behaviors or outliers. Through the utilization of deep learning procedures, this investigation endeavours to apply are fined procedure for anomaly detection in industrial big data. Pre-processing, feature selection and Anomaly detection are three steps of a process that are used. The input data is first fed into MapReduce framework where it is divided and pre-processed. Imputation of missing data and Yeo-Jhonson transformation are then applied to eliminate noise from data. After pre-processed data is generated, it is put through a feature selection phase using Serial Exponential Lotus Effect optimization algorithm (SELOA). The algorithm is created newly by combining Lotus Effect optimization algorithm (LOA) with Exponential Weighted Moving Average (EWMA). Finally, anomaly detection is done using the features that are selected by means of Deep Belief-MobileNet1D, which combines MobileNet1D and Deep Belief Network (DBN). With a recall of 96.2 %, precision of 92.8 %, F1 score of 94.5 % and accuracy of 95.9 %, results show that the proposed strategy surpasses standard approaches. These findings demonstrate Deep Belief-MobileNet1D model's ability to detect anomalies in industrial big data.

关键词： Anomaly detection Deep learning Big data Feature selection optimization algorithm Industrial application

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Deep Learning-Based Risk Analysis and Prediction During the Implementation of Carbon Neutrality Goals

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2025年第1期37卷

作者： Long, Huizhen Li, Meng Dong, Zhen Meng, Yuan Zhang, Fengrui Hong Kong Polytech Univ Hong Kong & Dongguan Univ Technol Sch Tourism & Hospitality Management Hong Kong Peoples R China Henan Open Univ Acad Marxism Kaifeng Peoples R China Sichuan Tourism Univ Sch Marxism Chengdu Peoples R China Jinan Ecol Environm Monitoring Ctr Jinan Peoples R China Sichuan Agr Univ Coll Life Sci Chengdu Peoples R China

Risk prediction has become increasingly crucial in today's complex and dynamic environments. However, existing forecasting methods still face challenges in terms of accuracy and reliability. Therefore, it is imperative to explore new approaches to better address risks. In response to this need, our study introduces an innovative risk prediction model known as WOA-FPALSTM. What sets this model apart is its seamless integration of deep learning and heuristic algorithms, designed to overcome the limitations of existing approaches. The core component of deep learning, LSTM, excels in sequence modeling by capturing long-term and short-term dependencies in time series data, thereby enhancing the model's ability to model temporal data. Meanwhile, the heuristic algorithm, WOA (Whale optimization algorithm), equips our model with global search capabilities, facilitating the discovery of optimal model configurations and significantly improving predictive performance.

关键词： Artificial Intelligence LSTM optimization algorithm Risk Emergency Management and Treatment Risk Forecasting WOA

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Evaluation of machine learning applications in building life cycle processes for energy efficiency: A systematic review

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ENERGY REPORTS 2025年 13卷 4900-4916页

作者： Kaya, Gevher Nesibe Beyhan, Figen Ilerisoy, Zeynep Yesim Cudzik, Jan Siirt Univ Fac Fine Arts & Design Dept City & Reg Planning Siirt Turkiye Gazi Univ Fac Architecture Dept Architecture TR-06530 Ankara Turkiye Gdansk Univ Technol Fac Architecture Dept Urban Architecture & Waterside Spaces Gdansk Poland

In recent years, machine learning has been increasingly applied to achieve energy efficiency in buildings. This study analyzes the utilization of machine learning across the building life cycle by reviewing literature on building energy efficiency. In this context, a systematic literature search was conducted using the Web of Science (WOS) search engine, and 868 publications were found. The publications were analyzed according to their year, subject scope, and qualification results, and 84 publications were selected. These publications were discussed under five categories: objective function and control variables, programs, simulations, machine learning, and optimization algorithms. The relationships between these categories and each phase of the building life cycle were examined. The findings suggest that machine learning can effectively optimize factors related to energy efficiency and building sustainability throughout the life cycle, and it is anticipated that interdisciplinary studies incorporating machine learning will experience exponential growth in the future.

关键词： Machine learning Life cycle process Energy efficiency Building design optimization algorithm Systematic analysis

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A data-driven model for predicting metal ductile fracture at various stress states

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ENGINEERING FAILURE ANALYSIS 2025年 175卷

作者： Liu, Wen Chen, Geng Liu, Jianhui Zhang, Lele Lanzhou Univ Technol Sch Mech & Elect Engn 36 Pengjiaping St Lanzhou 730050 Gansu Peoples R China Beijing Jiaotong Univ Sch Mech & Elect Control Engn 3 Shangyuan Village Beijing 100044 Peoples R China Beijing Jiaotong Univ Natl Int Sci & Technol Cooperat Base 3 Shangyuan Village Beijing 100044 Peoples R China

Metal ductile fracture often occurs in stamping forming and collision accidents, and accurate prediction of material fracture is essential in the design of stamping parts and passive safety structures. The existing fracture models are based on the general fracture law of materials and can predict fracture in all stress states with fewer parameters. However, the fracture prediction at certain stress states is not accurate enough due to configuration limitations. To this end, a datadriven fracture model was proposed in this paper, which determines the relationship between the fracture strain and the stress triaxiality entirely based on the material fracture behaviour. Thus, high accuracy of fracture prediction under all stress states can be achieved, but the model has more parameters. To calibrate these parameters, tensile experiments were conducted with a series of notched specimens, and the optimization framework of parameters inverse identification was established, including optimization problem establishment, parameter normalization, transformation to an unconstraint problem and solving with the simplex method. The calibration results show that the simulation error of the data-driven model is reduced from 30.64 % to 10.59 % in the 59 iterations, much lower than 18.20 % of the Johnson-Cook model. In the simulation of the modified compact-tension specimen, loads decrease earlier in the Johnson-Cook model, which further highlights the ability of the data-driven model describing the fracture under complex stress states. The accurate fracture prediction of the data-driven fracture model provides a basis for the reasonable design of stamping parts and passive safety structures.

关键词： Data-driven fracture model Stress state Johnson-Cook model Inverse parameter identification framework optimization algorithm

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