Composite Algorithm Research Articles

HiQ-FPAR: A High-Quality and Value-added MODIS Global FPAR Product from 2000 to 2023

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is essential for assessing vegetation’s photosynthetic efficiency and ecosystem energy balance. While the MODIS FPAR product provides valuable global data, its reliability is compromised by noise, particularly under poor observation conditions like cloud cover. To solve this problem, we developed the Spatio-Temporal Information Composition Algorithm (STICA), which enhances MODIS FPAR by integrating quality control, spatio-temporal correlations, and original FPAR values, resulting in the High-Quality FPAR (HiQ-FPAR) product. HiQ-FPAR shows superior accuracy compared to MODIS FPAR and Sensor-Independent FPAR (SI-FPAR), with RMSE values of 0.130, 0.154, and 0.146, respectively, and R² values of 0.722, 0.630, and 0.717. Additionally, HiQ-FPAR exhibits smoother time series in 52.1% of global areas, compared to 44.2% for MODIS. Available on Google Earth Engine and Zenodo, the HiQ-FPAR dataset offers 500 m and 5 km resolution at an 8-day interval from 2000 to 2023, supporting a wide range of FPAR applications.

Analysis of intelligent traffic system and its processing algorithm

With the continuous development of modern cities, traffic jams have become a growing problem. This paper analyzes and discusses the Intelligent Traffic System (ITS) produced by solving traffic jams. The purpose of this paper is to study the composition algorithms of Intelligent Traffic System, including path planning algorithm, network routing algorithm, and speed guidance algorithm. These three different algorithms make up the Intelligent Traffic System. In this paper, the above three algorithms are introduced and analyzed, and some typical algorithms are selected for analysis and introduction. The challenges encountered in the practical application of Intelligent Traffic System are analyzed, including the limitations of technology, management and policy. The future application trend of Intelligent Traffic System in urban construction is forecasted. Finally, through the integration and application of Intelligent Traffic Systems, the efficiency and quality of urban construction and development can be significantly improved to support the realization of safer and environmentally sustainable Traffic systems.

Binary composite crossover genetic algorithm for locating critical slip surface

Solving slope stability problems requires determining the critical slip surface (CSS) of a slope and its corresponding minimum factor of safety (Min. F), and determining the CSS is a complex optimisation problem. In this paper, we propose a real-coded binary composite crossover genetic algorithm (RGA-BCC) to locate the CSSs of slopes. A reasonable combination of parameters for the binary composite crossover (BCC) operator in the RGA-BCC is determined through six benchmark function experiments. On this basis, the stability of five soil slopes from the literature was analysed using the RGA-BCC in combination with the Morgenstern and Price method, and the results obtained were compared with those in the published literature to demonstrate that the RGA-BCC is able to efficiently determine the CSSs of slopes and their minimum factors of safety. The results of the sensitivity analyses show that having a reasonable setting for the number of control variables is the key to effectively determining the CSS. Moreover, the effects of slope structural characteristics should be considered when setting the number of control variables and population size. Algorithm comparison results show that RGA-BCC has better performance in solving the slope CSS problem compared with differential evolution (DE) and sparrow search algorithm (SSA).

Blockchain-based lightweight trusted data interaction scheme for cross-domain IIoT

To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a solution, current techniques still suffer from issues related to efficiency, security, and privacy. Our research aims to address these challenges by proposing a lightweight, trusted data interaction scheme based on blockchain, which reduces redundant interactions among entities. We enhance the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm to support lightweight distributed consensus in large-scale IIoT scenarios. Introducing a composite digital signature algorithm and incorporating veto power minimizes resource consumption and eliminates ineffective consensus operations. The experimental results show that, compared with PBFT, our scheme reduces latency by 27.2%, thereby improving communication efficiency and resource utilization. Furthermore, we develop a lightweight authentication technique specifically for cross-domain IIoT, leveraging blockchain technology to achieve distributed collaborative authentication. The performance comparisons indicate that our method significantly outperforms traditional schemes, with an average authentication latency of approximately 151 milliseconds. Additionally, we introduce a trusted federated learning (FL) algorithm that ensures comprehensive trust assessments for devices across different domains while protecting data privacy. Extensive simulations and experiments validate the reliability of our approach.

High-fidelity prediction of forming quality for self-piercing riveted joints in aluminum alloy based on machine learning

The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:LCh1Ah2A、LAh1Ah2B、LAh1Ah2B and LBh1Ah2B. A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization Algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the model consistently achieved mean absolute percentage error (MAPE) values below 10%, correlation coefficients (R²) exceeding 0.98, and stable mean squared error (MSE) values around 0.0002 across the prediction of three metrics. These results underscore the model's high precision and stability. Consequently, the proposed enhanced model offers a solution that is more stable, accurate, and robust for the prediction of forming quality in self-piercing riveted joints within engineering applications.

Composite Improved Algorithm Based on Jellyfish, Particle Swarm and Genetics for UAV Path Planning in Complex Urban Terrain.

Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

Gossip-based asynchronous algorithms for distributed composite optimization

The distributed composite optimization problem associated a multi-agent network is investigated in this paper. Different from conventional optimization issues, the cost function of composite optimization consists of a convex function and a regularization function (possibly nonsmooth). The gossip protocol is also introduced to enhance the robustness of the network, and a gossip-based distributed composite mirror descent algorithm is presented to deal with the previous problem, which adopts the asynchronous communication method. Moreover, the algorithm performance is analyzed and the theoretical results on the corresponding error bounds are obtained. Finally, the distributed logistic regression is provided as an example to validate the practicability of the proposed algorithm.

An IoT-enhanced automatic music composition system integrating audio-visual learning with transformer and SketchVAE

With the rapid development of artificial intelligence and the Internet of Things technology, the automatic music composition system has become a hot topic of research. This paper presents the TransVAE-Music composition system to achieve efficient multimodal data perception and fusion. Through the introduction of the Internet of Things technology, the system can collect and process audio, video and other data in real time, and improve the diversity and artistry of music generation. At the same time, the Bayesian optimization mechanism is used to finely adjust the hyperparameters in the system to further improve the model performance. Experimental results show that TransVAE-Music has 1.10 and 1.12 reconstruction errors on the POP909 and FMA datasets, respectively, which significantly outperforms other mainstream automatic music generation models. In addition, the model reached 4.8 and 4.9 in perceived quality score (PQS), and 4.4 and 4.5 in user satisfaction score (USS), respectively. These results demonstrate that the proposed system has significant advantages in terms of the accuracy of music generation and the user experience. This study not only provides an effective method for automatic music generation, but also provides important references for future studies on multimodal data fusion and high-quality music generation.

Design and multi-objective optimization of hairpin motor cooling system based on orthogonal design method and composite algorithm

The new thermal management structure of the motor is designed to ensure effective heat dissipation while maintaining low pressure loss, reduce the required power of the cooling pump. The dimensions of the new thermal management structure of the motor are optimized through the establishment of a multi-objective optimization platform. Computational Fluid Dynamics (CFD) simulations were conducted through single-factor analysis to compare and preliminarily determine the number of turns in the spiral channel. The key structural dimensions of the flow channel were adjusted using orthogonal design method for CFD simulations. The obtained complete orthogonal table was used as a dataset for comparing and analyzing the optimization of the Backpropagation (BP) neural network using the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. Results showed a higher fitting accuracy after optimization with the Particle Swarm Optimization algorithm. The Non-dominated Sorting Genetic Algorithm III (NAGA III) was employed for multi-objective optimization of the key flow channel dimensions. The optimal solution identified from the Pareto optimal solution set graph, which is the lowest point combining the highest stator temperature and maximum pressure in the flow channel, was verified through simulation to have a prediction accuracy of 99% for the comprehensive optimization algorithm. Furthermore, the simulation results of the cooling system obtained from multi-objective optimization were compared with the original cooling system, the comparison results show that the maximum pressure has decreased by 43.13%. Orthogonal design methods and composite algorithms are employed to replace traditional optimization methods for multi-objective optimization of motor cooling system in this study, offers a new perspective for the structural design of motor cooling systems.

Masked Time-Frequency Representation and Composite Priors Enabled Mixed-Mode Signal Decomposition Algorithm

Masked Time-Frequency Representation and Composite Priors Enabled Mixed-Mode Signal Decomposition Algorithm

A Positioning and Tracking Performance–Enhanced Composite Control Algorithm for the Macro–Micro Precision Stage

In the macro–micro composite motion process, the macro stage achieves rapid motion in a large workspace, while the micro stage realizes precise positioning within a small displacement. The large-stroke and high-speed motion of the macro stage is influenced by nonlinear friction, overshooting, and vibrations, making it challenging to achieve rapid stabilization within the travel range of the micro stage, thereby impacting equipment operation efficiency. This paper proposes a composite controller structure designed to solve the fast point-to-point positioning of the macro stage driven by a linear motor and enhance the trajectory tracking performance of the stage. The proposed composite control algorithm (CCA) includes velocity feed-forward, gain-scheduled proportional integral differential (PID) control, and a plug-in repetitive control method. By employing a tracking differentiator, velocity feed-forward, and a gain-scheduled PID controller, the control algorithm can realize rapid stabilization and positioning for the macro stage. Through velocity feed-forward, gain-scheduled PID control, and a plug-in repetitive controller, the control algorithm can reduce the trajectory tracking error of the stage. Simulations and experimental studies of point response and sinusoidal trajectory tracking are carried out on a macro–micro stage to verify the effectiveness of the composite controller for the linear motor. The experimental results demonstrate that the proposed composite controller effectively reduces the macro stage’s settling time and overshoot, and improves the accuracy of sinusoidal trajectory tracking, which lays a foundation for submicron positioning accuracy in high-speed macro–micro motion stages.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Process integration technique for targeting carbon credit price subsidy

Mitigating climate change requires a portfolio of strategies and the use of carbon dioxide removal techniques or negative emissions technologies (NETs) will be necessary to achieve this goal. However, the high implementation costs of advanced NETs lead to expensive carbon credits, hindering their broad acceptance and use. One potential solution involves governmental support through subsidies, aiming to boost the availability of NET-derived carbon credits. This research uses a graphical technique based on an extension of pinch analysis to identify the ideal subsidy level for carbon dioxide removal, taking into account factors such as carbon pricing, supply, and demand. The proposed approach modifies the limiting composite curve (LCC) methodology to accurately determine the optimal subsidy and establish the baseline amount of subsidized carbon dioxide removal needed. The approach enables the convenient and efficient construction of the LCC using a composite table algorithm. To illustrate the proposed methodology, two case studies composed of different NETs and demand sectors are investigated. The results show the most advantageous subsidy levels for these technologies, providing valuable insights to guide policymakers and investors in their decarbonization efforts. This work contributes to the development of effective governance and investment strategies by optimizing NET subsidy allocation. Such optimization is crucial for facilitating the widespread implementation of these technologies, which are in-line with the global efforts to mitigate climate change.

Performance Analysis and Parallel Scalability of Numerical Methods for Fractional-in-Space Diffusion Problems with Adaptive Time Stepping

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian (−Δ)α, 0<α<1, following the integral definition. Fractional diffusion is non-local, and the finite element method (FEM) discretization in space leads to a dense stiffness matrix. It is well known that numerically solving such non-local boundary value problems is expensive. Difficulties increase significantly when the problem is time-dependent. The aim of the article is to develop computationally efficient methods and algorithms. There are two main features of our approach. Hierarchical semi-separable (HSS) compression is applied for an approximate solution of the arising linear systems. For time discretization, we use an adaptive forward–backward Euler scheme. The properties of the composite algorithm thus obtained are investigated. In particular, the block representation of HSS compression allowed us to upgrade the HSS solver to efficiently handle varying diagonally perturbed transition matrices corresponding to changing time steps. The contribution of the paper is threefold. The methods are completely constructive, which allows for a clearly structured description of the algorithms. A theoretical estimate of the computational complexity is presented. It shows the advantages of the adaptive time stepping in combination with the HSS solver. Theoretical results are supported by representative numerical experiments. Both sequential and parallel scalability and efficiency are analyzed. The presented results provide convincing proof of the concept of the proposed methods and algorithms.

A pattern design strategy for microwave-absorbing coding metamaterials with tortuosity and connectivity

Microwave absorption presents a challenge for the design of metamaterials, which is critical for the stealth and electromagnetic compatibility. To address this, a novel design strategy for patterns is proposed to enhance the wave absorption, which is tortuosity and connectivity. Utilizing the carbon ink composite and genetic algorithm, multi-layer coding metamaterials (MCMs) are designed to satisfy diverse engineering specifications, with reflectivity tests confirming their efficacy. Temperature alternation experiments simulate frequent environmental changes, and the absorptivity of MCMs is compared to evaluate their resilience. This approach ensures the designed MCMs maintain performance and stability under variable thermal conditions, offering a robust solution for advanced applications.

Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis

Code deobfuscation, which attempts to simplify code that has been intentionally obfuscated to prevent understanding, is a critical technique for downstream security analysis tasks like malware detection. While there has been significant prior work on code deobfuscation, most techniques either do not handle control flow obfuscations that modify control flow or they target specific classes of control flow obfuscations, making them unsuitable for handling new types of obfuscations or combinations of existing ones. In this paper, we study a new deobfuscation technique that is based on program synthesis and that can handle a broad class of control flow obfuscations. Given an obfuscated program P, our approach aims to synthesize a smallest program that is a control-flow reduction of P and that is semantically equivalent. Since our method does not assume knowledge about the types of obfuscations that have been applied to the original program, the underlying synthesis problem ends up being very challenging. To address this challenge, we propose a novel trace-informed compositional synthesis algorithm that leverages hints present in dynamic traces of the obfuscated program to decompose the synthesis problem into a set of simpler subproblems. In particular, we show how dynamic traces can be useful for inferring a suitable control-flow skeleton of the deobfuscated program and performing independent synthesis of each basic block. We have implemented this approach in a tool called Chisel and evaluate it on 546 benchmarks that have been obfuscated using combinations of six different obfuscation techniques. Our evaluation shows that our approach is effective and that it produces code that is almost identical (modulo variable renaming) to the original (non-obfuscated) program in 86% of cases. Our evaluation also shows that Chisel significantly outperforms existing techniques.

Design of Neural Network-Based Multi-Fault Tolerant Control System for Unmanned Aerial Vehicles

Actuator failure seriously threatens the flight safety of unmanned helicopters. Considering the problem of multiple faults such as actuator bias and failure in unmanned helicopters, a composite fault-tolerant flight control algorithm is proposed. For actuator bias fault, a nonlinear fault observer is designed to estimate it in real time; for actuator failure fault, a same-dimensional auxiliary system is constructed and processed by neural network technology. The composite fault-tolerant flight controller of the unmanned helicopter is designed by backstepping method, and the Lyapunov stability theory is used to prove that the error signals of the closed-loop system are bounded and convergent. Simulation results show that the proposed control algorithm can improve the fault tolerance of the unmanned helicopter when multiple actuator faults occur, ensuring its safe flight.

Design and Implementation of Gesture Music Composition System based on RealSense and Recurrent Neural Network

Aiming at the problems of slow human-computer interaction and low accuracy of hand gesture recognition (HGR) in music composition systems such as human-computer interaction, gesture recognition, and algorithmic composition, this paper designs an HGR music composition system based on real-sense technology and recurrent neural network. The system uses an Intel RealSense 3D camera to shoot the user's hand and extract the information on the hand joint points with Smart Wearable Biosensors (SWBSs). The joint points are used to construct a three-dimensional model of the hand bone, and the joint points' equivalent distance and joint deflection are calculated by the joint point information to match the corresponding Curwen HGR. The matching music data is input into the recurrent neural network. GRU is combined with the Markov chain algorithm to complete the composition and avoid the phenomenon of gradient disappearance or gradient explosion. The HGR recognition function and composition algorithm are simulated and tested to verify the method's feasibility. Cohort verification investigations and efficacy evaluations of wearable biosensors are essential to support their clinical acceptability. The research results show that the HGR music composition system based on real-sense technology and recurrent neural networks can effectively identify the tester's HGRs and assist the tester in completing the composition using intelligent biosensors. The composition mode is close to the professional composition process.

Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm

Tidal flats, a critical part of coastal wetlands, offer unique ecosystem services and functions. However, in China, these areas are under significant threat from industrialization, urbanization, aquaculture expansion, and coastline reconstruction. There is an urgent need for macroscopic, accurate and periodic tidal flat resource data to support the scientific management and development of coastal resources. At present, the lack of macroscopic, accurate and periodic high-resolution tidal flat maps in China greatly limits the spatio-temporal analysis of the dynamic changes of tidal flats in China, and is insufficient to support practical management efforts. In this study, we used the Google Earth Engine (GEE) platform to construct multi-source intensive time series remote sensing image collection from Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) images, and then automated the execution of improved MSIC-OA (Maximum Spectral Index Composite and Otsu Algorithm) to process the collection, and then extracted and analyzed the tidal flat data of China in 2018 and 2023. The results are as follows: (1) the overall classification accuracy of the tidal flat in 2023 is 95.19%, with an F1 score of 0.92. In 2018, these values are 92.77% and 0.88, respectively. (2) The total tidal flat area in 2018 and 2023 is 8300.34 km2 and 8151.54 km2, respectively, showing a decrease of 148.80 km2. (3) In 2023, estuarine and bay tidal flats account for 54.88% of the total area, with most tidal flats distribute near river inlets and bays. (4) In 2023, the total length of the coastline adjacent to the tidal flat is 10,196.17 km, of which the artificial shoreline accounts for 67.06%. The development degree of the tidal flat is 2.04, indicating that the majority of tidal flats have been developed and utilized. The results can provide a valuable data reference for the protection and scientific planning of tidal flat resources in China.

A 6 month-long real-time time scale steered to NIM-Sr1 optical lattice clock

Abstract NIM-Sr1 optical lattice clock has been operated intermittently for seven consecutive months with an operation uptime around 13.8%, which serves as the reference to steer a hydrogen maser for generating a real-time time scale TS(Sr1). The peak-to-peak time difference of TS(Sr1) compared to UTC is 1.8 ns within 180 days and is less than 0.5 ns within the last month, after correcting the time error induced by a time transfer link abnormality. A dedicated automation system with hardware and software is developed to monitor the operation status of NIM-Sr1, process the measurement data, and periodically predict the hydrogen maser frequency. According to the simulation results, a composite algorithm based on both Kalman filtering (KF) and weighted least squares fitting (WLSF) is utilized for generating TS(Sr1). The KF is utilized when the unavailability of NIM-Sr1 is shorter than one day, and the WLSF with fitting interval T fit=30 d is utilized in other conditions leveraging the long-term predictability of the hydrogen maser. In addition, it is demonstrated by simulation and the post-processing of earlier experimental data that, a time scale with smaller time error can be achieved by shortening the WLSF interval from 30 days to about 5 days under the condition of sufficient measurement data points for reducing the statistical error.