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Sustainable Development of Port Economy Based on Intelligent System Dynamics

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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A batch-based run-to-run process control scheme for semiconductor manufacturing

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IIE TRANSACTIONS 2013年第6期45卷 658-669页

作者： Wang, kaibo Han, kai Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

Run-to-Run (R2R) control has been widely used in semiconductor manufacturing to compensate for process disturbance and to improve quality. The traditional R2R controller only takes the process output in a previous run as its input and generates an optimal recipe for the next run. In a multistage semiconductor manufacturing process, variations in upstream stations are propagated to downstream stations. However, the information from upstream stations is not considered by existing controllers. In addition, most R2R processes have a limited capacity;the products must be processed in batches. Therefore, if the incoming materials could be grouped with small within-batch variations and large batch-to-batch variations and the recipes are customized for each batch to drive all batch averages toward the same target value, the output variation could be reduced and quality improved. A batch Exponentially Weighted Moving Average (EWMA) controller is proposed in this article. It employs a modified k-means algorithm to group the incoming materials into batches with a fixed and equal size while minimizing the within-batch variation. The controller then generates the control settings by taking both the batch information and the feedback quality information into account. Simulation studies show that the proposed controller could significantly reduce output variation and improve quality.

关键词： Batch-to-batch variation k-means clustering algorithm run-to-run process control semiconductor manufacturing within-batch variation

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MR functional cardiac imaging: Segmentation, measurement and WWW based visualisation of 4D data

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 1999年第2期15卷 185-193页

作者： Delibasis, kk Mouravliansky, N Matsopoulos, Gk Nikita, kS Marsh, A Natl Tech Univ Athens Inst Commun & Comp Syst GR-15773 Athens Greece

This paper considers the problem of ventricular segmentation and visualisation from dynamic (4D) MR cardiac data covering an entire patient cardiac cycle, in a format that is compatible with the web. Four different methods are evaluated for the process of segmentation of the objects of interest: The k-means clustering algorithm, the fuzzy k-means (FkM) algorithm, self-organizing maps (SOMs) and seeded region growing algorithm. The technique of active surface is then subsequently applied to refine the segmentation results, employing a deformable generalised cylinder as geometric primitive. The final ventricular models are presented in VRML 2.0 format. The same process is repeated for all the 3D volumes of the cardiac cycle. The radial displacement between end systole and end diastole is calculated for each point of the active surface and is encoded in colour on the VRML vertex, using the RGB colour model. Using the VRML 2.0 specifications, morphing is performed showing all cardiac phases in real time. The expert has the ability to view the objects and interact with them using a simple internet browser. Preliminary results of normal and abnormal cases indicate that very important pathological situations (such as infarction) can be visualised and thus easily diagnosed and localised with the assistance of the proposed technique. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： 4D MR cardiac images k-means clustering algorithm fuzzy k-means (FkM) algorithm self-organizing maps (SOMs) seeded region growing algorithm sctive surface surface triangulation VRML 2.0 format

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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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A k-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第7期21卷 2258-2269页

作者： Tien-Loc Le Tuan-Tu Huynh Lin, Lo-Yi Lin, Chih-Min Chao, Fei Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan Yuan Ze Univ Taoyuan Taiwan Lac Hong Univ Dept Elect Elect & Mech Engn Bien Hoa Vietnam Taipei Vet Gen Hosp Dept Radiol Taipei Taiwan Xiamen Univ Dept Cognit Sci Xiamen Fujian Peoples R China

This paper proposes a new medical diagnosis algorithm that uses a k-means interval type-2 fuzzy neural network (kIT2FNN). This kIT2FNN classifier uses a k-means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the k-means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient approach. The Lyapunov theory is also used to verify the convergence and stability of the proposed method. The performance of the system is evaluated using several medical datasets from the University of California at Irvine (UCI). All of the experimental and comparison results are presented to demonstrate the effectiveness of the proposed medical diagnosis algorithm.

关键词： Classification problem Interval type-2 fuzzy neural network k-means clustering algorithm Medical diagnosis

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Machine learning approach in mortality rate prediction for hemodialysis patients

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第1期25卷 111-122页

作者： Radovic, Nevena Prelevic, Vladimir Erceg, Milena Antunovic, Tanja Univ Montenegro Elect Engn Dept Podgorica Montenegro Clin Ctr Montenegro Clin Nephrol Podgorica Montenegro Clin Ctr Montenegro Ctr Lab Diagnost Podgorica Montenegro

kernel support vector machine algorithm and k-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

关键词： Mortality rate prediction support vector machine algorithm k-means clustering algorithm chronic kidney disease

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A parallel strategy for predicting the quality of welded joints in automotive bodies based on machine learning

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 62卷 636-649页

作者： Chen, Geng Sheng, Buyun Luo, Ruipin Jia, Pengzhen Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

Due to the complexity of the resistance spot welding process, it is still a challenge to accurately know the operating status of the welding robot under the current parameter settings and to assess the welding quality of electrode caps under different types of plates in real time with large data sizes. To solve this problem, this paper classifies the overall data set and proposes a parallel strategy method for predicting the quality of weld joints using machine learning for subsets of the data with different distribution patterns. Firstly, the PCA dimensionality reduction model was used to set the number of principal components to reduce the dimensionality of the welding process feature value dataset and reduce the difficulty of classifying the data subgroups, and the elbow method was used to set the number of clustering centers to complete the classification of the sub-datasets by applying the k-means model on the basis of the dimensionality reduction data. Finally, the feature parameters of each sub-dataset are used as input for machine learning, and a parallel prediction strategy for weld joint quality is developed based on the data distribution characteristics of each sub-dataset. The test results show that the model in this paper outperforms the static BP neural network in predicting the quality of all types of welded joints, the machine learning parallel strategy tailored to the characteristics of the data population works well with more complexly distributed welded big data. This paper provides accurate and effective estimation of body resistance welding condition, which can provide some guidance for online inspection of body resistance spot welding quality in automotive production lines.

关键词： Resistance spot welding Quality prediction Principal component analysis (PCA) k-means clustering algorithm Back propagation neural network (BPN) Parallel strategies

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Machine learning for creation of generalized lumped parameter tank models of low temperature geothermal reservoir systems

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GEOTHERMICS 2017年 70卷 62-84页

作者： Li, Y. Juliusson, E. Palsson, H. Stefansson, H. Valfells, A. Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ene Minist Educ 92 Weijin Rd Tianjin 300072 Peoples R China Reykjavik Univ Sch Sci & Engn Menntavegi 1 IS-105 Reykjavik Iceland Landsvirkjun Haaleitisbraut 68 IS-103 Reykjavik Iceland Univ Iceland Fac Ind Engn Mech Engn & Comp Sci Hjardarhagi 2-6 IS-107 Reykjavik Iceland

Lumped parameter tank models have gained renewed interest in recent years as an alternative tool for geothermal reservoir analysis and production planning. The models can be structured in various ways regarding the number of tanks, connections between the tanks and the parameters representing the physical properties of the geothermal system. It usually requires a time consuming and difficult process of trials and errors to manually decide the optimal configuration of a tank model. Inspired by recent development in the use of machine learning methods, we propose a method for automatically generating accurate and computationally feasible generalized tank models for isothermal, single phase, reservoirs. This is an extension of earlier work on complexity reduction of generalized tank models (Li et al., 2016). Here, a recursive "switch-back" method is constructed to maximize prediction accuracy of the model. It is also shown how the k-means clustering algorithm can be used to aggregate production wells in generalized tank models. One synthetic example and one field application from t Reykir geothermal fields in Iceland are used to illustrate the effectiveness of these methods.

关键词： Geothermal reservoir modeling Lumped-parameter models Machine learning Complexity analysis Information criterion Complexity reduction algorithm Switch-back method Model selection k-means clustering algorithm

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Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in ¹⁸F-FDG-PET images

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IET IMAGE PROCESSING 2017年第6期11卷 389-396页

作者： Tafsast, Abdelghani Hadjili, Mohamed Laid Bouakaz, Ayache Benoudjit, Nabil Univ Batna 2 Lab Automat Avancee & Anal Syst Batna Algeria High Sch Comp Sci HEB ESI Ecole Super Informat Brussels Belgium Univ Francois Rabelais Tours UMR Inserm Imagerie & Cerveau U930 Tours France

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (F-18-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in F-18-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

关键词： pattern clustering image segmentation tumours positron emission tomography medical image processing unsupervised cluster-based method laryngeal tumour biological tumour volume segmentation F-18-FDG-PET image 18-fluorodeoxyglucose positron emission tomography radiotherapy biological tumour volume delineation BTV delineation k-means clustering algorithm automatic optimal cluster number estimation intrinsic positron emission tomography image information patient clinical dataset

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UAVs to the Rescue: Prolonging the Lifetime of Wireless Devices Under Disaster Situations

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORkING 2019年第4期3卷 942-954页

作者： Shakhatreh, Hazim khreishah, Abdallah Ji, Bo Yarmouk Univ Dept Telecommun Engn Irbid 21163 Jordan New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider downlink scenarios from an aerial base station to ground users. In this paper, we consider an uplink scenario under disaster situations (such as earthquakes or floods), when cellular networks are down. We formulate the placement problem of UAVs, where the objective is to determine the locations of a set of UAVs that maximize the time duration of uplink transmission until the first wireless device runs out of energy. We prove that this problem is NP-complete. Due to its intractability, we start by restricting the number of UAVs to be one. We show that under this special case the problem can be formulated as a convex optimization problem under a restriction on the coverage angle of the ground users. After that, we propose a gradient projection-based algorithm to find the optimal location of the UAV. Based on this, we then develop an efficient algorithm for the general case of multiple UAVs. The proposed algorithm starts by clustering the wireless devices into several clusters where each cluster being served by one UAV. After it finishes clustering the wireless devices, it applies the gradient projection-based algorithm in each cluster. We also formulate the problem of minimizing the number of UAVs required to serve the ground users such that the time duration of uplink transmission of each wireless device is greater than or equal to a threshold value. We prove that this problem is NP-complete and propose to use two efficient methods to determine the minimum number of UAVs required to serve the wireless devices. We validate the analysis by simulations and demonstrate the effectiveness of the proposed algorithms under different cases.

关键词： Unmanned aerial vehicles lifetime of wireless devices emergency response convex optimization gradient projection algorithm k-means clustering algorithm

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