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Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation

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IET IMAGE PROCESSING 2020年第12期14卷 2901-2910页

作者： Mano, Abhisha Anand, Swaminathan Rajas Inst Technol Elect & Commun Engn Nagercoil India Mepco Schlenk Engn Coll Elect & Commun Engn Sivakasi India

Multi-region segmentation plays a major role in numerous medical diagnostics especially brain tumour identification and classification in Magnetic Resonance Imaging (MRI). Brain tumour segmentation is used in medical field for early diagnostics and detection of tumour. The main goal of this work is to improve the performance of detection by using grid based techniques with Weighted Bee Swarm Intelligence and K-means clustering. This technique is more effective due to hybrid combination of segmentation and optimisation as it seems to possess specific tasks of image information and detection to obtain a detailed and accurate image analysis. grid based segmentation balance overall computation time and reduces complexity. Weighted Bee Swarm Optimisation is used to optimise segmentation parameters to get maximum performance. The various informative regions such as cerebrospinal fluid, grey matter, white matter are segmented by using proposed algorithm which will be most useful to study and characterise the tumour. The experimental outcomes show that the proposed strategy enhances performance measures in terms of sensitivity and specificity analysis. The performance of this technique is also improved by a factor of 1.5%.

关键词： medical image processing image segmentation biomedical MRI brain tumours optimisation swarm intelligence pattern clustering sensitivity analysis multiregion tumour segmentation grid-based segmentation weighted bee swarm optimisation medical diagnostics brain tumour identification brain tumour classification magnetic resonance imaging computerised digital image processing techniques decision-making time grid-based technique image information image analysis segmentation parameters informative regions tumour region brain MRI image segmentation weighted bee swarm intelligence brain tumour segmentation K-means clustering cerebrospinal fluid grey matter white matter sensitivity analysis specificity analysis

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grid-based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-28页

作者： Gong, Yiguang Chen, Kai Niu, Tianyu Liu, Yunping Nanjing Univ Informat Sci & Technol Sch Automat Nanjing Peoples R China

In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.

关键词： Unmanned aerial vehicle Coverage path planning Ant colony algorithm Floyd algorithm No-fly zone grid-based technique

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A Survey of Clustering techniques and Algorithms 2

A Survey of Clustering Techniques and Algorithms

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Nisha Kaur, Puneet Jai Panjab Univ UIET Chandigarh India

ISBN: (纸本)9789380544168

Clustering is used data mining technique in which a group of similar objects is combined together to form clusters, these clusters are different from the objects in another clusters. This paper describes some clusterization techniques like, partitional technique, hierarchical technique, grid-based technique, density-based technique and their algorithms. Partitional method divides the data set into objects based on some similarity criterion, hierarchical method creates a hierarchy between clusters by combining the data objects into clusters, and then these clusters are further combined together to form large clusters and so on, grid-based method forms clusters by combining the data objects into grids or cells, density based method are used to separate the high dense clusters from low dense clusters.

关键词： Clustering data mining density-based technique grid-based technique hierarchical technique partitional technique

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Ray tracing simulation of wing mirrors for ultrasonic sensor based blind spot monitoring system 14

Ray tracing simulation of wing mirrors for ultrasonic sensor...

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14th IEEE International Conference on Application of Information and Communication Technologies (AICT)

作者： Kholkhujaev, Jasurkhuja Abdurakhmonov, Nodirbek Ruzimov, Sanjarbek Abdukarimov, Nurmukhammad Inoyatkhodjaev, Jamshid Saidov, Abdulla Turin Polytech Univ Tashkent Dept Mech & Aerosp Engn Tashkent Uzbekistan

ISBN: (纸本)9781728173863

in this paper, we propose a method for 3D virtual estimation of blind spot zone for passenger vehicle. Blind spot zone (BSZ) is the problem of inconspicuous area that accounts for majority part of lane change related accidents. To estimate BSZ, the wing mirror reconstruction with the driver's eyes as a focal point in the vehicle has been performed. Thus far, the reconstruction of mirrors and driver in the vehicle has been a problem since they are often based on approximations or inaccurate data. Our aim in this paper was to confirm that ray tracing simulation of mirrors can be performed in order to identify the BSZ. The virtual model of wing mirror and contiguous vehicle parts are created in Solidworks and TracePro to make ray tracing simulation. The research was limited to the range of 3600mm. Physical experiment set up is grid-based technique used to model BSZ and make comparative analysis with virtual mirror ray tracing simulation. Matlab is used to compare grid-based technique to identify BSZ and the simulated BSZ angle. Additionally, the paper discusses possibilities of using different sensor technique for blind spot monitoring systems (BSMs) for passenger vehicles. BSMs are often applied in driver assistant systems for autonomous vehicles with level one, while the paper discusses possibility of installing BSMs onto passenger cars with level 0 autonomy. In conclusion, ray tracing simulation of virtual wing mirror can be used to detect 3D BSZ and suggest a BSMs based on the data obtained from simulation. BSMs installment can be done on passenger cars with much cost effective way using the BSZ virtual analysis.

关键词： Autonomous vehicle blind spot zone grid-based technique passenger cars sensors

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Continual learning fault diagnosis method based on grid-based artificial immune system

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第11期33卷 115004-115004页

作者： Sun, Xin Wang, Haotian Liu, Shulin Xiao, Haihua Wang, Lu Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

In recent years, continual learning for class increments has attracted a great deal of attention. The ontinual-learning classification method (CLCM based on an artificial immune system (AIS) can identify unknown faults during testing. However, the CLCM still has the problem of excessive runtime consumption. Therefore, it is crucial to improve the efficiency of the immune algorithm and take advantage of its continual learning mechanism in the field of fault diagnosis. In this paper, a continual learning fault diagnosis method based on sparse grid and the AIS, which called sparse grid classification method (SGCM), is proposed, which is inspired by grid-based techniques and the CLCM based on an AIS. Firstly, a new cell generation strategy is proposed to reduce the time complexity and improve the diagnosis efficiency;therefore, the problem of dimension explosion is avoided. In addition, the memory cell coding capabilities of the SGCM increases the utilization rate of cells so as to simplify the calculation of affinity. At the same time, the conceived cell backtracking strategy enhances the continual learning ability of the algorithm so that new fault types can be quickly identified through the existing learning results. Ultimately, the model adaptive adjustment method inspired by a single-layer feed-forward neural network improves the generalization power and the accuracy of classification. We conduct experiments on well-known datasets from the UCI repository to assess the performance of the SGCM. To evaluate the fault diagnosis performance of the SGCM, experiments on a reciprocating compressor experimental dataset and the XJTU-SY rolling element bearing dataset were performed. The results show that theSGCM is a fast fault diagnosis method with low time complexity and continual learning ability.

关键词： artificial immune system intelligent fast fault diagnosis reciprocating compressor grid-based technique classification method weighted Manhattan distance

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PPS: Energy-Aware grid-based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

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SENSORS 2020年第13期20卷 3742页

作者： Ghaddar, Alia Merei, Ahmad Natalizio, Enrico Int Univ Beirut Dept Comp Sci POB 14-6404 Beirut Lebanon Univ Lorraine CNRS LORIA F-54000 Nancy France

Area monitoring and surveillance are some of the main applications for Unmanned Aerial Vehicle (UAV) networks. The scientific problem that arises from this application concerns the way the area must be covered to fulfill the mission requirements. One of the main challenges is to determine the paths for the UAVs that optimize the usage of resources while minimizing the mission time. Different approaches rely on area partitioning strategies. Depending on the size and complexity of the area to monitor, it is possible to decompose it exactly or approximately. This paper proposes a partitioning method called Parallel Partitioning along a Side (PPS). In the proposed method, grid-mapping and grid-subdivision of the area, as well as area partitioning are performed to plan the UAVs path. An extra challenge, also tackled in this work, is the presence of non-flying zones (NFZs). These zones are areas that UAVs must not cover or pass over it. The proposal is extensively evaluated, in comparison with existing approaches, to show that it enables UAVs to plan paths with minimum energy consumption, number of turns and completion time while at the same time increases the quality of coverage.

关键词： Unmanned Aerial Vehicles coverage path planning energy-aware trajectories remote sensing grid-based technique cellular decomposition area partitioning non-flying zones

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