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Reversible data hiding method for multi-histogram point selection based on improved crisscross optimization algorithm

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INFORMATION SCIENCES 2021年 549卷 13-33页

作者： Weng, Shaowei Tan, Wenlong Ou, Bo Pan, Jeng-Shyang Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Fujian Univ Technol Sch Elect Elect Engn & Phys Fuzhou 350118 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Traditional prediction error expansion (PEE)-based reversible data hiding (RDH) schemes focus on generating a sharp prediction error histogram (PEH) in order to increase embedding performance, but they neglect the influence of local complexity on embedding performance. When the PEH is partitioned into multiple sub-histograms by means of the local complexity, the embedding performance can be increased by excluding the embedding of data in the sub-histograms located in the texture regions. However, multiple sub-histograms also cause a problem about how to identify the optimal embedding points that can achieve the best visual quality for a given payload. Violent iteration is often used in RDH schemes to traverse all possible values of the embedding points in order to identify the optimal points. Therefore, we conclude that violent iteration is a very time-consuming method and that it leads to unacceptable computational complexity. The traditional multiple sub-histogram methods usually reduce the solution space (i.e., all possible combinations of the embedding points) to decrease the computational complexity. However, the solution that is obtained from the aforementioned methods may significantly deviate from the global optimal solution. Instead of shrinking the solution space by abandoning most solutions, we improve the crisscross optimization algorithm in order to search for the optimal solution in the global solution space. In this paper, the k-means clustering algorithm is used to classify all prediction errors into multiple categories according to the local complexity. Each category would generate a PEH. Subsequently, the problem of selecting the embedding points of multiple sub-histograms is transformed into a typical and multi-choice knapsack problem. The improved crisscross optimization algorithm is used to determine the approximate optimal solution. The experimental results showed that our scheme provided effective performance. (C) 2020 Elsevier Inc. All rights

关键词： Reversible data hiding Multiple prediction error histograms k-means clustering algorithm Multi-choice knapsack problem Improved crisscross optimization algorithm

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Sustainable Development of Port Economy Based on Intelligent System Dynamics

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IEEE ACCESS 2021年 9卷 14070-14077页

作者： Xiu, Guoyi Zhao, Zexu Harbin Univ Sci & Technol Sch Econ & Management Harbin 150000 Peoples R China Hainan Trop Ocean Univ Sch Business Sanya 572000 Peoples R China

With the development of economic globalization, international trade competition is increasingly intensified. As an important hub of waterway transportation, the sustainable economic development of ports has an important impact on the economic development of the entire country. Therefore, the research on the sustainable development of port economy has been paid more and more attention by everyone, but the current results are not ideal. This paper studies the sustainable development of port economies based on networked smart sensor technologies and system dynamics. First, according to the construction principles of the sustainable development index system, combined with the actual situation of the port economy, the evaluation index system layer of the sustainable development of the port economy is divided into four sub-systems: population, environment, resources and economy. A system dynamics model for the sustainable development of the port economy will be constructed and its sensitivity will be analyzed. Then, the above system dynamics model is combined with the optimized k-means algorithm, and the k-means algorithm is used to quickly calculate the required data in the system dynamics model to form an intelligent system dynamics model for the sustainable development of port economy. Finally, this article takes a certain port economy sustainable development giant system as an example, and implements a sustainable development strategy based on the idea of the coordinated development of population, environment, resources and economy. Run the intelligent system dynamics model for the sustainable development of port economy constructed in this research to simulate the dynamics of each subsystem. Experimental results show that in the next 20 years, the total population, number of employed population and employment ratio of the port city will continue to increase. After 2025, the emissions of the Exhaust have been declining year by year, the total amount of resources has b

关键词： System dynamics Sustainable development Analytical models Heuristic algorithms Mathematical model Intelligent systems Economics System dynamics k-means clustering algorithm port economy sustainable development subsystem

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Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

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OPEN GEOSCIENCES 2021年第1期13卷 705-716页

作者： Chen, Qiuji Wang, Xin Hang, Mengru Li, Jiye Xian Univ Sci & Technol Coll Geomat Dept Geog Xian 710054 Peoples R China Xian Inst Geotechn Investigat & Surveying Mapping Xian 710054 Peoples R China

The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and k-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for k-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

关键词： single tree segmentation LiDAR technology mining area forest DBSCAN k-means clustering algorithm

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Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 35-35页

作者： Abdel-Basset, Mohamed Mohamed, Reda Hezam, Ibrahim M. Sallam, karam M. Foul, Abdelaziz Hameed, Ibrahim A. Zagazig Univ Zagazig 44519 Sharqiyah Egypt Univ Sharjah Coll Comp & Informat Comp Sci Dept Sharjah U Arab Emirates Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra Australia King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci Alesund Norway

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs us

关键词： Multi-UAVs Mobile edge computing Trajectory planning Non-dominated solutions Pareto optimality k-means clustering algorithm

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Study of Methods for Constructing Intelligent Learning Models Supported by Artificial Intelligence

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第2期11卷 1-12页

作者： Pan, Lijun Zhengzhou Normal Univ Coll Elementary Educ Zhengzhou 450044 Henan Peoples R China

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial ***: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation ***: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements;then, the intelligent learning model is constructed through a k-means clustering algorithm and deep compression sparse self-encoder;finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment ***: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient ***: The results show that the proposed method improves the model's accuracy.

关键词： Intelligent learning model Model element extraction k-means clustering algorithm Deep compression sparse self-encoder

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Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

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WATER 2024年第19期16卷 2786页

作者： Liu, Weitao Han, Mengke Zhao, Jiyuan Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number k = 3 was determined. The collected data were clustered into three categories using the k-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7 similar to 414.9 m and 750 similar to 900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified

关键词： floor failure depth k-means clustering algorithm grey correlation analysis CatBoost algorithm groundwater

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A General 3D Space-Time-Frequency Non-Stationary Model for 6G Channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第1期20卷 535-548页

作者： Huang, Ziwei Cheng, Xiang Peking Univ Sch Elect Engn & Comp Sci Dept Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China

In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., k-means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.

关键词： 6G channel model 3D space-time-frequency non-stationarity correlated cluster based BD process method k-means clustering algorithm

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An improved algorithm for pile damage localization based on complex continuous wavelet transform

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SMART STRUCTURES AND SYSTEMS 2021年第3期27卷 493-506页

作者： Liu, Jing-Liang Lin, Cheng-Xu Ye, Xi-Jun Zheng, Wen-Ting Luo, Yong-Peng Fujian Agr & Forestry Univ Coll Transportat & Civil Engn Fuzhou 350002 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China Fujian Univ Technol Coll Civil Engn Fuzhou 350118 Peoples R China

Since the complex continuous wavelet transform (CCWT) based pile damage detection method is empirical and subjective, an improved algorithm for pile damage localization based on CCWT is proposed by introducing k-means clustering and fast Fourier transform (FFT). In this method, the k-means clustering algorithm is used to accurately calculate the time coordinates of two energy concentrating points caused by the incident and reflected waves, respectively. Meanwhile, FFT is employed to estimate the concerned frequency band of the response signal. Therefore, a specific region in the time frequency plane is defined objectively and it can be used to search the phase angle turning points and localize pile damage. The proposed method is verified by numerical examples of piles with single and multiple damage positions. A parameter analysis is also conducted to investigate how damage depth and damage degree in piles affect the accuracy and effectiveness of the proposed method. The results demonstrate that the proposed method is able to localize a pile with a damage at least 2.5 m away from the pile head when the damage degree is as less as 5%. After that, dynamic tests of an actual square reinforced concrete pile and an actual circular reinforced concrete pile are investigated to verify the application of the proposed method on practical engineering. Although the proposed method is capable of localizing actual piles more accurately than the CCWT method, the problem of interference points needs to be addressed by mutual verification with other pile damage localization methods.

关键词： complex continuous wavelet transform k-means clustering algorithm fast Fourier transform phase angle damage localization

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Surface reinforcement of recycled aggregates (RAs) by geopolymer and quantifying its morphological characteristics by machine learning

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JOURNAL OF BUILDING ENGINEERING 2024年 91卷

作者： Chen, Zhengfa Zhang, Jiahao Cao, Shuang Cindy Song, Yan Chen, Zhaoyan Changzhou Univ Sch Urban Construct Civil Engn Dept Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Sch Mat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Zhaoshe Decorat Co Ltd Wuhan 430070 Hubei Peoples R China

Recycled concrete aggregate (RCA) and recycled brick aggregate (RBA), derived from construction and demolition waste (C&DW), are increasingly used in concrete materials to conserve natural resources. In certain places, RBA, typically derived from C&DW, originally made with minerals that absorb water easily, dating back to the last century, exhibit higher water absorption and poorer mechanical properties compared to RCA. This study investigates the use of fly ashbased geopolymer paste (FGP) as a surface reinforcement coating for pre-treated RCA (PRCA) and pre-treated RBA (PRBA). Image processing and machine learning are employed to quantify the morphological characteristics of RAs. The results demonstrate improved morphology and altered distribution of morphological characteristic indexes for PRCA and PRBA. Physical and mechanical tests reveal the highest improvement in PRCA and PRBA occurs at a SiO2/Na2O molar ratio (Ms) of 1.4. Apparent density increases by 2.3 % and 2.6 %, and crushing value decreases by 21.6 % and 22.2 %, respectively. However, affected by the paste type, coating thickness, and geopolymerization reaction, the water absorption increases by 2.1 % and 5.8 %. XRD, TG-DTG, SEM, and LF-NMR tests indicate the formation of N(C)-A-S-H on RAs' surface by FGP, enhancing their microstructure.

关键词： k-means clustering algorithm C4.5 decision tree algorithm Surface reinforcement Morphological characteristics of RAs LF-NMR

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Optimizing risk budgets in portfolio selection problem: A bi-level model and an efficient gradient-based algorithm

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IISE TRANSACTIONS 2024年第8期56卷 841-854页

作者： Bayat, Maryam Hooshmand, Farnaz MirHassani, Seyed Ali Amirkabir Univ Technol Tehran Polytech Dept Math & Comp Sci Tehran Iran

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time.

关键词： Risk budgeting portfolio bi-level optimization gradient descent method k-means clustering algorithm

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