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Indirect solution modeling of melting behavior of SiO2 based on the image processing technology

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2024年第2期19卷 e3028-e3028页

作者： Lu, Cunhao Zhang, Yi Zhang, Jiayi Sun, Weixiang Xia, Anying Zhang, Mingli Chen, Jian Yangzhou Univ Sch Mech Engn Huayang Weststr 196 Yangzhou 225127 Jiangsu Peoples R China Shandong Jinyu Elect Technol Co LTD Innovat Workshop Jinan Shandong Peoples R China McGill Univ Montreal Neurol Inst McGill Ctr Integrat Neurosci Montreal PQ Canada Shandong Technol & Business Univ Sch Comp Sci Yantai Shandong Peoples R China

The utilization of tempered blast-furnace slag through the direct fiber forming process to produce high-value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast-furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO2 in a high-temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO2. Given that SiO2 is proportionate to crucible pixels, the area of the SiO2 is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast-furnace slag.

关键词： image processing indirect solution method k-means clustering algorithm traversal algorithm

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A Framework for Adaptive Façade Optimization Design Based on Building Envelope Performance Characteristics

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BUILDINGS 2024年第9期14卷 2646页

作者： Chen, Ping Tang, Hao Shandong Jianzhu Univ Sch Architecture & Urban Planning SAU Jinan 250101 Peoples R China

The adaptive fa & ccedil;ades serve as the interface between the indoor and outdoor energy of the building. Adaptive fa & ccedil;ade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive fa & ccedil;ade optimization design. This paper aims to reveal the potential functional relationship between building fa & ccedil;ade performance characteristics and adaptive fa & ccedil;ade design. It proposes an adaptive fa & ccedil;ade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a k-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive fa & ccedil;ade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson's correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive fa & ccedil;ades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive fa & ccedil;ade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 kWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive fa & ccedil;ade optimization design, which helps to achieve multiple performance objectives for buildings.

关键词： adaptive fa & ccedil ades building envelope performance daylighting performance quality of view solar energy utilization k-means clustering algorithm multi-objective optimization

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The Improved YOLOV5 algorithm and Its Application in Small Target Detection 15th

The Improved YOLOV5 Algorithm and Its Application in Small T...

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15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Zhao, Yu Shi, Yuanbo Wang, Zelong Liaoning Petrochem Univ Fushun 113000 Peoples R China

ISBN: (纸本)9783031138416;9783031138409

According to the characteristics of YOLOV5, a method based on YOLOV5 is proposed. First, this method is used for the identification of small objects. Secondly, this paper conducts experiments on the improved YOLOV5 algorithm and conducts experiments on it, and compares the performance of the two methods. In modern detection technology, the most commonly used is the target detection technology. In recent years, small object detection has been an important research topic in industrial inspection. The small target has a small number of pixels, and its features are blurred. Larger targets have lower detection rates and higher false detection rates. In order to improve the accuracy and accuracy of target detection, this paper proposes a method based on depthwise convolution and k-means clustering, which realizes the clustering of the dimension and aspect ratio of the target image. On the basis of CCPD, the YOLOv5 algorithm is optimized and compared with the original YOLOv5 algorithm. Experiments show that the improved YOLOv5 can better detect smaller targets, so that the recall rate and average precision of small targets are significantly improved.

关键词： Machine vision Small object detection YOLOv5 CCPD dataset k-means clustering algorithm Mosaic

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Multifactor diagnostic model of converter energy consumption based on k-means algorithm and its application

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JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL 2025年

作者： Dai, Fei-xiang Chen, Guang Bao, Xiang-jun Liu, Gong-guo Zhang, Lu Yang, Xiao-jing Anhui Univ Technol Sch Energy & Environm Maanshan 243002 Anhui Peoples R China Pangang Grp Res Inst Co Ltd Panzhihua 617000 Sichuan Peoples R China

To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant, the focus is placed on the critical components of material and heat balance. Through a thorough analysis of the interactions between various components and energy consumptions, six pivotal factors have been identified-raw material composition, steel type, steel temperature, slag temperature, recycling practices, and operational parameters. Utilizing a framework based on an equivalent energy consumption model, an integrated intelligent diagnostic model has been developed that encapsulates these factors, providing a comprehensive assessment tool for converter energy consumption. Employing the k-means clustering algorithm, historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates. Building upon this data-driven foundation, an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented, enhancing the precision and efficiency of energy management. Upon implementation with energy consumption data at a steel plant in 2023, the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units. The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade, with contributions of -0.550 and 0.379.

关键词： Equivalent energy consumption model Intelligent diagnostic model k-means clustering algorithm Online system Energy management

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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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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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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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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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