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Designing an Optimal GNSS Receiver Network for Space Weather Studies Using Unsupervised Machine Learning algorithm

RADIO SCIENCE 2022年第12期57卷 1-20页

作者： Dashora, N. Harsha, P. Babu Sree Natl Atmospher Res Lab Gadanki India

Ionospheric maps from an optimal GNSS (global navigation satellite system) receiver network may lead to better understanding and prediction of the equatorial ionospheric gradients. An unsupervised machine learning framework is proposed using the k-means clustering approach to optimize the available number of GNSS receivers. Beginning with k = 15 receivers, the simulations are performed to obtain distribution of the ionospheric pierce points (IPPs) in different grid resolutions up to k = 1,000. The algorithm uses a reference grid and subsequently optimizes the locations according to the available IPPs in a cluster. The IPP cluster centers are iteratively optimized based on minimizing the "within cluster sum of squares" error. The IPPs from the optimal receiver locations are used to generate a 2-D vertical total electron content (VTEC) map using NeQuick-G model. A one-sigma confidence limit derived from hourly histogram of the IPPs from the k locations is used as decisive criteria for useable grid cells. The number of empty bins thus obtained in a given scenarios provides a percentage normalized empty grid ratio (NEG(ratio)). A NEG(ratio) below 10% is used as a threshold to make a decision over eligibility of a scenario to produce a desired VTEC map. Several scenarios are thus tested for varying temporal and spatial grid resolutions, geographical boundaries, GNSS and NavIC satellite configurations, considering the diurnal and annual variation in year 2019. The framework is found to be successful in designing a GNSS network for any geographical region to obtain high resolution 2-D VTEC maps for space weather applications.

关键词： space weather total electron content unsupervised machine learning clustering algorithm GNSS NavIC ionospheric maps optimization

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Product Family Formation for Reconfigurable Manufacturing Systems 2nd

Product Family Formation for Reconfigurable Manufacturing Sy...

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3rd International Conference on Digital Technologies and Applications

作者： Bahtat, Chaymae El Barkany, Abdellah Jabri, Ahdelouahhah Sidi Mohammed Ben Abdellah Univ Fac Sci & Technol Mech Engn Lab BP 2202 Route Imouzzer Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

Reconfigurable manufacturing systems, or RMS, are a new concept in production systems. Their goal is to assure the feasibility of numerous generations of products by making quick adjustments to existing systems in order to meet the increasing evolution of markets while maintaining a high quality of products at a low cost. An RMS is designed around a family of products with enough flexibility to manufacture them all. These products are grouped into families based on certain common characteristics, such as modularity, components, sequence of manufacturing operations, etc. These characteristics differ depending on the production system studied and the products that need to be integrated in order to have an optimal formation of product families. These characteristics differ according to the production system studied and the products that must be integrated in order to have an optimal formation of product families. With this objective, this paper presents a structured and updated literature review on product family formation for RMS, highlighting key application areas and tools. Through this literature review, we were able to construct a general clustering algorithm that can be applied to any production system.

关键词： Reconfigurable Manufacturing Systems product family formation clustering algorithm

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RETRACTED: Hybrid cluster made content forwarding and light weight sign encryption IoT based Named Data Network using African Buffalo optimization algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2891-2905页

作者： Karthik, S. V. Vijaya Selvi, J. Arputha Vijaya Kings Coll Engn Dept ECE Pudukkottai Tamil Nadu India

Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%).

关键词： Named data network clustering algorithm content poisoning african buffalo optimization hyperelliptic curve cryptography

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Power output optimization of electric vehicles smart charging hubs using deep reinforcement learning

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷

作者： Bertolini, Andrea Martins, Miguel S. E. Vieira, Susana M. Sousa, Joao M. C. Univ Lisbon IDMEC Inst Super Tecn 1 Ave Rovisco Pais P-1049001 Lisbon Portugal

Since most branches of the distribution grid may already be close to their maximum capacity, smart management when charging electric vehicles (EVs) is becoming more and more crucial. In fact, office buildings might not be able to handle several transactions at the same time, especially considering the next generation of fast chargers which are very power expensive. Thus, an efficient charging policy needs to be found. This paper proposes the scheduling of real-time EVs charging through deep reinforcement learning (DRL) techniques. DRL has been chosen because it can adaptively learn from interacting with the surrounding environment. The focus of the optimization is to ensure the completion of the charging transactions in a timely manner, while shifting the load from the times of peak demand. The novelty of the proposed approach lies in its innovative framework: pools of electric vehicles with different characteristics are categorized using a clustering algorithm, a tree-based classifier has been developed to sort new instances of EVs, and a multilayer perceptron artificial deep neural network has been trained to predict the expected duration of each charging session. These features are used as inputs to the DRL agent, and are mapped into actions that adjust the maximum power associated to each charging station. The model has been compared to a traditional charging algorithm and increasingly challenging scenarios have been considered. Results have shown that the developed algorithm fails less than the baseline, with a reduction of the load due to EVs charging of 80% during peak times.

关键词： Reinforcement learning Electric vehicles Real-time charging scheduling Neural network clustering algorithm

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Significant full reference image segmentation evaluation: a survey in remote sensing field

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第13期81卷 17959-17987页

作者： Pugazhenthi, A. Sebastian, Sruthy Rohith, G. Kumar, Lakshmi Sutha Natl Inst Technol Puducherry Dept Elect & Commun Engn Pondicherry India

Image segmentation is a crucial step in remote sensing application, as it breaks down a larger image into smaller chunks, which contains useful information. Although there are several image segmentation algorithms available, evaluation of the algorithms is challenging. Furthermore, the evaluation of image segmentation can elucidate the best image segmentation algorithm for a single image or a group of image or a whole class of image. This paper explores and evaluates the benefits and the drawbacks of various qualitative and quantitative image segmentation evaluation metrics used in remote sensing applications. For all the metrics, a quantitative set of values for good and bad segmentation is provided. In addition, some image segmentation algorithms such as Multi Otsu Threshold, K Means clustering, Fuzzy C Means clustering, Improved K Means clustering (IFCM), Improved Fuzzy C Means clustering, Naive Bayes classifier, K Nearest Neighbor, Decision Tree (DT) and Random Forest classifier are used in the experimental comparison of metrics. The qualitative and quantitative satellite image segmentation evaluation using the mentioned algorithms is measured. The results are analyzed to strengthen the impact of different metrics on the segmentation algorithms. In both qualitative and quantitative analysis, the IFCM outperformed the other unsupervised machine learning algorithms and the DT outperformed the other supervised machine learning algorithms. The effectiveness of the provided metrics in the remote sensing field is validated.

关键词： Image segmentation INSAT-3D Evaluation metrics clustering algorithm Qualitative evolution Quantitative evolution Machine learning algorithm

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Hybrid access and adaptive duty cycle clustering protocol for ultra-low power wireless sensor networks

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IET COMMUNICATIONS 2021年第9期15卷 1158-1173页

作者： Al-Sulaifanie, Adnan Ismail Al-Sulaifanie, Bayez Khorsheed Univ Duhok Coll Engn Elect & Comp Engn Dept Dahuk Iraq

This paper investigates many drawbacks and limitations of current clustering algorithms designed for wireless sensor networks. These limitations include high resource requirements, significant overhead, tight synchronization requirements, and TDMA limitations. A new clustering algorithm called hybrid access and an adaptive duty cycle clustering (HADC) protocol is introduced, aiming to address the previous limitations. HADC protocol is based on several concepts, including static clusters, adaptive duty cycling, hybrid scheme channel access, minimum node's functionality, and pseudo synchronization. The resulting network's important features are energy efficiency, robustness, low latency, scalability, and self-healing. The functionality of HADC is validated by both simulation and laboratory experiments. Simulation results show significant improvement of HADC over the well-known LEACH protocol in terms of energy, latency, and packet delivery ratio. The experiment results show that the node's lifetime with two 3000 mAh batteries and transmitting four packets per minute is about 16 years. The lifetime of the cluster head supported by the same energy sources ranges from 110 to 472 days. HADC is the first clustering algorithm designed for sporadic and low rate WSN applications to the best of authors' knowledge.

关键词： TDMA limitations adaptive duty cycle clustering protocol minimum node time 110.0 d to 472.0 d Protocols high resource requirements previous limitations cluster head laboratory experiments static clusters time division multiple access current clustering algorithms time 16.0 year Wireless sensor networks pseudosynchronization Multiple access communication pattern clustering LEACH protocol wireless sensor networks protocols adaptive duty cycling HADC protocol clustering algorithm Radio links and equipment hybrid scheme channel access resulting network tight synchronization requirements hybrid access

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Cluster-based gradient method for stochastic optimal control problems with elliptic partial differential equation constraint

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NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS 2022年第6期38卷 1861-1879页

作者： Xiong, Meixin Chen, Liuhong Ming, Ju Hou, Lisheng Huazhong Univ Sci & Technol Sch Math & Stat Wuhan Peoples R China Iowa State Univ Dept Math Ames IA USA

In this article, we establish a cluster-based gradient method (CGM) by combining K-means clustering algorithm and stochastic gradient descent (SGD). By clustering the sampling solutions, we use the cluster centroids to represent sampling data and give an estimate to the full gradient. It is well known that the full gradient descent (FGD) can provide the steepest descent direction for finding a local minimum of the desired stochastic control problems. However, the huge computational requirements, which is proportional to the product of sample size and the numerical cost for each sample, often makes FGD cost prohibitive for large scale optimization problems. To reduce the formidable cost and the risks of getting stuck in a local minimum, SGD is proposed and can be regarded as a stochastic approximation of FGD. This, however, would result in a slow convergence due to the incorrect approximation of the iteratively update parameters. Our study shows that CGM could provide a good stochastic approximation to the full gradient with small sample size while has a more stable and faster convergence than SGD. To verify our algorithm, a stochastic elliptic control problem is selected and tested. The numerical results validate our method as a reliable gradient descent method with great potential applications in optimization problems.

关键词： clustering algorithm full gradient descent Monte Carlo method optimization under uncertainty PDE-constrained optimization stochastic gradient descent

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clustering-based method for locating critical slip surface using the strength reduction method

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COMPUTERS AND GEOTECHNICS 2023年 155卷

作者： Jia, Jinqing Ju, Shiyuan Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

In slope stability analysis, the strength reduction method (SRM) combined with the finite element method (FEM) can directly obtain the factor of safety (FOS) of complex slopes, but it cannot directly locate the critical slip surface in the plastic zone. Current methods for locating the critical slip surface in SRM are approximations based on visualization techniques, which limits the application of SRM. To solve this problem, a cluster-based method for locating the critical slip surface was proposed in the present study, which can make full use of the various parameters calculated by FEM. In the proposed method, each node in the finite element model is treated as a data point, and its parameters such as stress, strain, and displacement are used as the features of this data point. Then all nodes in the model are classified into two classes using the clustering algorithm, and the boundary between the two classes is the critical slip surface. The effectiveness of the proposed method was demonstrated by applying it to several examples and comparing the results with those of other methods.

关键词： clustering algorithm Strength reduction method The critical slip surface Dimensionality reduction Slope stability

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New perspective of learning objects in e-learning system

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INTERNATIONAL JOURNAL OF INFORMATION AND LEARNING TECHNOLOGY 2023年第3期40卷 269-279页

作者： Amane, Meryem Aissaoui, Karima Berrada, Mohammed Sidi Mohamed Ben Abdellah Univ Fes Morocco Mohammed Premier Univ Oujda Morocco

PurposeTogether, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning ***/methodology/approachThe development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and *** achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering ***/valueFinally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

关键词： Learning objects Web-based mining techniques Fuzzy c-means clustering algorithm Machine learning Latent semantic analysis (LSA)

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Decentralized Multi-Target Tracking for Netted Radar Systems with Non-Overlapping Field of View

Decentralized Multi-Target Tracking for Netted Radar Systems...

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IEEE Radar Conference (RadarConf)

作者： Peng, Cong Mao, Haiyi Liu, Yue Chai, Lei Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665436694

In this paper, a robust and high-accuracy decentralized fusion strategy is proposed for multi-target tracking (MTT) in netted radar systems with non-overlapping field of view (FoV). Each radar in the network runs a local Probability Hypothetical Density (PHD) filter with the decentralized consensus protocol to reduce communication bandwidth and eliminate information inconsistency among nodes. In the above process, the most critical core is an effective fusion strategy. Our proposed method adopts the geometric covariance intersection (GCI) rule to improve fusion accuracy. However, the standard GCI fusion is not suitable for the netted radar systems with non-overlapping FoV because it only focuses on the targets within the intersection of radar FoVs. Consider that, we extend the weights in GCI fusion to be a set of state-dependent weights instead of scalars to perform GCI fusion in a more robust manner. Furthermore, the radar FoVs are always unknown and time-varying in practical scenarios. Towards addressing this case, we combine a clustering algorithm based on highest posterior density to maintain a good fusion performance. The Gaussian mixture implementation of the proposed method is provided. Numerical simulations are designed to verify the effectiveness of the proposed method.

关键词： decentralized multi-target tracking field of view consensus protocol state-dependent generalized covariance intersection fusion clustering algorithm

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