scholarly journals A membrane computing framework for social navigation in robotics

2021 ◽  
Vol 95 ◽  
pp. 107408
Author(s):  
Ignacio Pérez-Hurtado ◽  
David Orellana-Martín ◽  
Miguel Á. Martínez-del-Amor ◽  
Luis Valencia-Cabrera
Keyword(s):  
Membrane Computing ◽  
Social Navigation ◽  
Computing Framework

scholarly journals A Spectral Clustering Algorithm Improved by P Systems

2018 ◽  
Vol 13 (5) ◽  
pp. 759-771 ◽  
Author(s):  
Guangchun Chen ◽  
Juan Hu ◽  
Hong Peng ◽  
Jun Wang ◽  
Xiangnian Huang
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Membrane Computing ◽  
Clustering Algorithms ◽  
P System ◽  
Comparison Results ◽  
Clustering Effect ◽  
Computing Framework ◽  
Spectral Clustering Algorithm ◽  
Selection Of

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm.

scholarly journals A membrane computing framework for self-reconfigurable robots

2018 ◽  
Vol 18 (3) ◽  
pp. 635-646 ◽  
Author(s):  
Dongyang Bie ◽  
Miguel A. Gutiérrez-Naranjo ◽  
Jie Zhao ◽  
Yanhe Zhu
Keyword(s):  
Membrane Computing ◽  
Reconfigurable Robots ◽  
Computing Framework

ANALISIS EFEKTIFITAS FRAMEWOK ERP CLOUD COMPUTING UNTUK USAHA KECIL MENENGAH

2018 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Matheus Alvian Wikanargo ◽  
Novian Adi Prasetyo ◽  
Angelina Pramana Thenata
Keyword(s):  
Cloud Computing ◽  
Human Resource ◽  
Software As A Service ◽  
Infrastructure As A Service ◽  
Computing Framework

AbstrakTeknologi cloud computing pada era sekarang berkembang pesat. Penerapan teknologi cloud computing sudah merambah ke berbagai industri, mulai dari perusahaan besar hingga perusahaan kecil dan menengah. Perambahan cloud computing di perindustrian berupa implementasi ke dalam sistem ERP. Namun, penetrasi teknologi ini dalam lingkup perusahaan kecil dan menengah (UKM) masih belum sekuat perusahaan besar. Penerapan ERP berbasis cloud computing yang masih tergolong baru tentu memiliki keuntungan dan penghambat yang mempengaruhi kinerja perusahaan. Hal tersebut menjadi salah satu pertimbangan UKM masih enggan menggunakan teknologi ini. Penelitian ini akan menganalisis framework yang paling sesuai untuk UKM dalam menerapkan sistem ERP berbasis cloud computing. Framework yang dianalisa yaitu Software as a Service (SaaS), Infrastructure as a Service (IaaS), dan Platform as as Service (PaaS). Ketiga framework ini akan dibandingkan menggunakan metode studi literatur. Tolak ukur yang menjadi acuan untuk perbandingan adalah Compatibility, Cost, Flexibility, Human Resource, Implementation, Maintenance, Security, dan Usability. Faktor-faktor tersebut akan diukur keuntungan dan penghambatnya jika diterapkan dalam SME. Hasil dari penilitian ini adalah Framework SaaS yang paling cocok untuk diterapkan pada perusahaan kecil dan menengah. Kata kunci— Cloud Computing, UKM, SaaS, IaaS, PaaS 

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

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