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System state model based multi-period robust generation, transmission, and demand side resource co-optimisation planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第3期13卷 345-355页

作者： Dai, Chenxi Wu, Lei Zeng, Bo Liu, Cong Clarkson Univ Elect & Comp Engn Dept Potsdam NY 13699 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA ERCOT Taylor TX 76574 USA

This study discusses a multi-period co-optimised generation and transmission expansion planning (GTEP) problem while considering a proliferation of demand side resources (DSR). Uncertain renewable energy variations and load fluctuations in the long-term planning horizon are addressed, and a system state model derived via k-means clustering algorithm is adopted to capture temporal operation features. The problem is formulated as a two-stage robust optimisation model with mixed-integer recourse, in which annual investment decisions of generation, transmission, and DSR assets are determined in the first stage and short-term operation decisions of individual system states are made in the second stage. In recognising that considering DSR deployment and the system state model brings significant computational complexity, an extended column-and-constraint-generation algorithm is adopted to effectively solve the proposed planning problem. Numerical studies show that integrating DSRs into multi-period GTEP could effectively postpone or even avoid expensive generation/transmission investment in the planning stage, and improve economic efficiency in the operation stage.

关键词： power generation economics investment power generation planning power transmission economics integer programming power transmission planning pattern clustering computational complexity numerical analysis planning stage system state model multiperiod robust generation uncertain renewable energy variations long-term planning horizon temporal operation features two-stage robust optimisation model short-term operation decisions DSR deployment demand side resource cooptimisation planning generation-transmission investment multiperiod robust transmission multiperiod GTEP problem k-means clustering algorithm multiperiod cooptimised generation and transmission expansion planning problem mixed-integer recourse investment decision computational complexity extended column-and-constraint-generation algorithm

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Design and Implementation of Data Management and Visualisation Module in Financial Digital Management

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JOURNAL OF INFORMATION & kNOWLEDGE MANAGEMENT 2024年第0期

作者： Ren, Junying Henan Inst Econ & Trade Coll Intelligent Finance Zhengzhou 450000 Peoples R China

Enterprise financial data is the key indicator of enterprise development, which provides the important basis for management to analyse and make decisions. Therefore, the provision of reliable and effective information services to enterprises through visualisation technology has become an urgent problem to be solved in the construction of enterprise informatisation. At present, the common data statistics and visualisation tools in the market are difficult to meet the needs of specialised financial enterprises for data analysis. Additionally, the current financial management system has several issues, including an abundance of data and lack of observation suitability. Aiming at the deficiency of data management function in the system, this paper studies the improvement design of data management and visualisation module in financial digital management. First, k-means clustering algorithm and C4.5 decision tree algorithm are selected to improve the financial data management system. Then, through the existing hierarchical data visualisation scheme, the node link method, space filling method and Sankey chart are proposed to display the changes of financial data. Finally, the data management and visualisation module and the corresponding algorithm flow are designed. The experiment indicates a contour coefficient of 0.53 for the performance evaluation model based on the k-means algorithm, indicating a satisfactory clustering result. The employee violation prediction model, based on the C4.5 decision tree algorithm, exhibits a high prediction accuracy of 92.35% for the training dataset, demonstrating its effectiveness in predicting employee violations. The data rendering accuracy of the visualised tool is 98.46%, significantly surpassing that of traditional visualisation tools. At the same time, its visual effect and operation are better than traditional tools. Compared with the traditional data visualisation system, this research method improves the efficiency of enterprise

关键词： Digitalisation financial management data visualisation k-means clustering algorithm C4.5 decision tree algorithm

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Evaluation of black tea appearance quality using a segmentation-based feature extraction method

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FOOD BIOSCIENCE 2024年 58卷

作者： Song, Feihu Lu, Xiaolong Lin, Yiqing Zhou, Qiaoyi Li, Zhenfeng Ling, Caijin Song, Chunfang Jiangnan Univ Sch Mech Engn Jiangsu Key Lab Adv Food Mfg Equipment & Technol Wuxi 214122 Peoples R China Guangdong Acad Agr Sci Tea Res Inst Guangdong Prov Key Lab Tea Plant Resources Innovat Guangzhou 510640 Peoples R China

Compared with highly subjective manual sensory quality evaluation, the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea quality evaluation system. In this study, Yinghong No. 9 black tea was taken as the research object, and the gold pekoe, color and strips were adopted as the appearance evaluation characteristics for black tea. An image segmentation method based on the improved k-means clustering algorithm was proposed to realize the segmentation of the dark background area, tea area and golden pekoe area. The CIELAB color model was used to extract color features of the tea area. The texture features extracted by GLRLM were applied to evaluate the strips. The RF, SVR and BPNN were selected to construct prediction models for evaluating tea appearance quality. The prediction accuracy and generalization ability of the RF model are superior to those of the SVR model and BP model, with R2p, RMSEP and RPD values of 0.898, 1.548 and 3.207, respectively. The proposed feature extraction method based on regional segmentation intuitively described the key evaluation characteristics of black tea appearance, and the predicted results were highly consistent with the manual sensory evaluation.

关键词： Black tea k-means clustering algorithm Image segmentation Gray-level run-length matrix Sensory evaluation

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Increasing flexibility of self-piercing riveting by reducing tool-geometry combinations using cluster analysis in the application of multi-material design

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS 2022年第6期236卷 1310-1320页

作者： kappe, Fabian Schadow, Luca Bobbert, Mathias Meschut, Gerson Paderborn Univ Lab Mat & Joining Technol LWF D-33098 Paderborn Germany

A frequently used mechanical joining process that enables the joining of dissimilar materials is self-piercing riveting. Nevertheless, the increasing number of materials as well as material-thickness combinations leads to the need for a large number of rivet-die combinations as the rigid tool systems are not able to react to changing boundary conditions. Therefore, tool changes or system conversions are needed, resulting in longer process times and inflexibility of the joining processes. In this investigation, the flexibility of the self-piercing riveting process by reducing the required tool-geometry combinations is examined. For this purpose, various joints consisting of similar as well as dissimilar materials with different material thickness are sampled and analysed. Subsequently, a cluster algorithm is used to reduce the number of rivet-die combinations required. Finally, the effect of the changed tool geometries on both the joint formation and the joint load-bearing capacity is investigated. The investigation showed that a reduction by 55% of the required rivet-die combinations was possible. In particular, the rivet length influences the joint formation and the joint load-bearing capacity. An exclusive change of the die (e.g. die depth or die diameter) did not show a significant influence on these parameters.

关键词： Self-piercing riveting flexibility cluster analysis k-means clustering algorithm

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MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint kashin-Beck disease

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JOURNAL OF SUPERCOMPUTING 2021年第8期77卷 9088-9101页

作者： Dang, Chenpo Yi, Guirong Zhu, Zhaomin Zhou, Peng Shao, Hongbin Yao, Yanbin Zhao, Maosheng Li, Lintao Li, Shensong Chinese Peoples Liberat Army Hosp Joint Logist Support Force 90 Dept Orthoped Sport Med Lanzhou 730050 Peoples R China Lanzhou Univ Dept Gastroenterol Affiliated Hosp 2 Lanzhou 730050 Peoples R China Gen Hosp Tibet Mil Reg Dept Orthoped Lasa 850007 Peoples R China Gansu Univ Chinese Med Clinical Med Coll Lanzhou 730050 Peoples R China Nanjing Univ Jinling Hosp Sch Med Dept Orthoped Nanjing 210093 Peoples R China

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of kashin-Beck Disease (kBD) of the knee joint. Based on the shortcomings of the traditional k-means clustering algorithm (kCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare kCA of MapReduce (MR-kCA) and Improved MR-kCA (IMR-kCA). With the same data, the sum of squared errors of MR-kCA and IMR-kCA decreased with the increase in the number of center points. Compared with MR-kCA, the quality of IMR-kCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-kCA and IMR-kCA increased with the increase in the number of center points. Compared to MR-kCA, the total execution time of IMR-kCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-kCA could reduce the total execution time by 50%. Through experiments, IMR-kCA was proved to better present the diagnosis and treatment data of patients with knee joint kBD. The scalability rates of MR-kCA and IMR-kCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-kCA, IMR-kCA had significantly higher scalability. The IMR-kCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of kBD diagnosis and treatment.

关键词： MapReduce distributed parallel computing framework knee joint kashin-Beck disease k-means clustering algorithm Data mining Center point

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Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with clustering Analysis

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SENSORS 2024年第11期24卷 3538页

作者： Borowska, Marta Lipowicz, Pawel Daunoraviciene, kristina Turek, Bernard Jasinski, Tomasz Pauk, Jolanta Domino, Malgorzata Bialystok Tech Univ Inst Biomed Engn Fac Mech Engn PL-15351 Bialystok Poland Vilnius Gediminas Tech Univ Dept Biomech Engn LT-03224 Vilnius Lithuania Warsaw Univ Life Sci Inst Vet Med Dept Large Anim Dis & Clin PL-02787 Warsaw Poland

The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2-25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the k-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.

关键词： horse 3D segmentation volume surface area k-means clustering algorithm

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Partition harvesting of a column-comb litchi harvester based on 3D clustering

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 197卷 1页

作者： Li, Can Lin, Jiaquan Li, Boyang Zhang, Sheng Li, Jun South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Guangdong Ruobo Intelligent Co Ltd Foshan 528000 Peoples R China

Accurate recognition of litchi fruits in orchard environments and acquisition of their coordinate position information are key for realizing successful harvesting using litchi harvesters. However, the existing detection methods are often aimed at large and relatively sparse fruits and thus are inappropriate for small and densely distributed litchi fruits. Therefore, at present, litchi fruit are typically manually harvested, resulting in a low harvesting efficiency that cannot meet the needs of growers. To improve the efficiency of litchi harvesting, this study proposes a column-comb litchi harvesting method based on k-means 3D clustering partitioning, which includes four main steps: (1) Litchi image acquisition and labeling methods are developed. (2) An improved version of the present YOLOv3-tiny network model structure is developed named the YOLOv3-tiny-Litchi network model, and the litchi fruit detection results of five kinds of neural networks, namely, YOLOv3-tiny, YOLOv3-tiny-Litchi, YOLOv4, YOLOv5x and Faster R-CNN, are compared. (3) A depth camera is used to obtain the 3D coordinates of litchi fruits, and the k-means clustering algorithm is used to divide the litchi harvesting area to obtain the optimal partitioning results. (4) Field experiments on litchi harvesting are reported. The experimental results show that the improved YOLOv3-tiny-Litchi model can recognize litchi fruits more accurately;the recall rate is 78.99%, the precision rate is 87.43%, and the F1 score is 0.83. The results of 3D clustering partitioning show that when k is equal to 6, the optimal harvesting rate is 90.03%, which satisfies the theoretical requirements of litchi harvesters. The field experiments show that when the theoretical number of partitions is 6, the average harvesting rate of the litchi harvester is 91.15% and the recognition rate is 88.39%, and the harvesting efficiency of mechanical partition harvesting is 1.4 times that of mechanical harvesting without partitioning and 2

关键词： Litchi k-means clustering algorithm Object detection Column-comb Partition harvest

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Application of an unsupervised artificial neural network technique to multivariant surface water quality data

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ECOLOGICAL RESEARCH 2009年第1期24卷 163-173页

作者： Cinar, Oezer Merdun, Hasan Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Environm Engn TR-46060 Kahramanmaras Turkey Kahramanmaras Sutcu Imam Univ Fac Agr Dept Agr Engn TR-46060 Kahramanmaras Turkey

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies-Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, k, Cl, NH4-N, NO2-N, o-PO4, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.

关键词： kohonen self-organising feature maps clustering k-means clustering algorithm Surface water quality Variable dependencies

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Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 461-470页

作者： de Paula, Arthur N. de Oliveira, Edimar J. de Oliveira, Leonardo W. Honorio, Leonardo M. Univ Fed Juiz de Fora Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz De Fora MG Brazil

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

关键词： Transmission expansion planning Transmission active losses Benders decomposition N-1 security criterion Wind power uncertainty k-means clustering algorithm

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MULTIMODAL OPTIMIZATION OF JOB-SHOP SCHEDULING PROBLEMS USING A clustering-GENETIC algorithm BASED APPROACH

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第5期26卷 651-662页

作者： Zou, Pan Rajora, Manik Liang, Steven Y. Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Donghua Univ Coll Mech Engn Shanghai Peoples R China

Though ample work has been performed on the optimization of job-shop scheduling problems (JSSPs), very few techniques can satisfy the requirement of modern-day workshops, i.e., providing multiple schedules that achieve the desired goal. These techniques also do not provide a good balance between converging to the optimal solution while finding multiple optimal solutions. To overcome this obstacle, a new algorithm is proposed by combining the k-means clustering algorithm and genetic algorithm (GA) for multimodal optimization of JSSPs. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters based on some machine sequence-related features under the assumption that different global optima will have different features. Next, the adapted genetic operators are applied to the individuals belonging within the same cluster with the aim of independently searching for global optima within each cluster. The performance of the proposed algorithm is measured by its application to the multimodal optimization of benchmark JSSPs and comparing its performance against other multimodal optimization algorithms. The results of the case studies show that the algorithm has a better average optimal value and is also capable of finding multiple optimal solutions.

关键词： multimodal optimization job-shop scheduling problem (JSSP) k-means clustering algorithm genetic algorithm

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