The Study on Personalized Guidance in Intelligent Library Based on Multi-Agent

2010 ◽  
Vol 143-144 ◽  
pp. 18-22
Author(s):  
Zhi Gang Ji
Keyword(s):  
Environmental Factors ◽  
Mobile Phone ◽  
Mobile Agents ◽  
Large Scale ◽  
Complete Analysis ◽  
Personal Favorite ◽  
Multi Agent ◽  
Real Time Information ◽  
Moving Path ◽  
Time Information

The system integrated RFID technique, wireless and mobile phone which apply in the intelligent guidance and book management of large scale of library, and to collate the real time information by different functional mobile agents to provide customized information and service, according to the favorite of each reader. Meanwhile, to display forgoing information by multimedia video of book introduction for readers to replace traditional looking for some particular book provide the customized knowledge search style and effective book introduction. In addition, our article set forth System Environment Aware and Recommending Mechanism which bring up considerations for customized information, besides to analyzing personal favorite, should think about the environmental factors in the library, such as, moving path of readers, service and so on such synthetic factors to have a complete analysis and consideration, so as to know what the most appropriate guided service and information is for readers and to make it as a customized intellectual guided service.

Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

2017 ◽  
Vol 8 (4) ◽  
pp. 34-53 ◽  
Author(s):  
Stefan Bosse
Keyword(s):  
Mobile Agents ◽  
Large Scale ◽  
Learning Algorithm ◽  
Seismic Station ◽  
Geodetic Network ◽  
Distributed Learning ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.

Incremental Distributed Learning With JavaScript Agents for Earthquake and Disaster Monitoring

2019 ◽  
pp. 813-833
Author(s):  
Stefan Bosse
Keyword(s):  
Mobile Agents ◽  
Large Scale ◽  
Learning Algorithm ◽  
Seismic Station ◽  
Geodetic Network ◽  
Distributed Learning ◽  
The Internet ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.

scholarly journals Distributed Optimal Control for Distribution Systems with Microgrids

2021 ◽  
Author(s):  
Melissa Ospina-Quiroga ◽  
Eduardo Mojica-Nava
Keyword(s):  
Information Exchange ◽  
Distribution Systems ◽  
Large Scale ◽  
New Technologies ◽  
Distributed Optimal Control ◽  
Progressive Development ◽  
Real Time Information ◽  
And Control ◽  
Time Information ◽  
Optimal Distributed Control

The growing consumption of electricity as well as the progressive development of new technologies implies that the power system is increasingly automated with the purpose of having a more efficient and economical operation. This development drives the system to a Smart Grid, a large-scale cyber-physical network covering different energy generation technologies, storage and communications enabling real-time information exchange and control. In this work, we present an optimal distributed control based on the consensus+innovation technique, where each agent of the network obtains information from its neighbours. Simulations on a microgrid system based on an IEEE 14-Bus reference system demonstrate the effectiveness of the approach. Convergence is observed in the microgrid system under different scenarios in the physical and communications network.

scholarly journals Tsada-Mobiminder: A Location Based Alarm Mobile Reminder

2015 ◽  
Vol 3 (4) ◽  
pp. 43
Author(s):  
Marylene Saldon Eder
Keyword(s):  
Mobile Phone ◽  
Mobile Application ◽  
Smart Phones ◽  
User Needs ◽  
Application Service ◽  
Scenic Spot ◽  
Current Location ◽  
Real Time Information ◽  
The City ◽  
Time Information

Existing location based alarm applications has inability to give information to user’s particular direction to a specified place of destination and doesn’t display a particular scenic spot from its current location going to the destination. With this problem, a location based alarm mobile reminder was developed. The application is implemented on Android based smart phones to provide services like providing routing information, helping find nearby hotels, restaurants and scenic spots and offer many advantages to the mobile users to retrieve the information about their current location and process that data to get more useful information near to their location. It reminds the user about the location when the user enters some predefined location. All the user needs to have is the mobile phone with android platform with version 4.0 and above, and then the user can select the destination and find the destination on the application. The main objective of the project is to develop a location based application that provides tourists with real time information for scenic spots and provides alarm to a specified place of destination. This mobile application service will act as assistance for the frequent travelers to visit new places around the City.

scholarly journals Performance evaluation and resource optimization of cloud based parallel Hadoop clusters with an intelligent scheduler.

2018 ◽  
Vol 7 (4.20) ◽  
pp. 4220
Author(s):  
Manishankar S. ◽  
S. Sathayanarayana
Keyword(s):  
Large Scale ◽  
Performance Monitoring ◽  
Processing System ◽  
Monitoring And Evaluation ◽  
Monitoring Tool ◽  
Super Node ◽  
Hadoop Clusters ◽  
Hadoop Cluster ◽  
Real Time Information ◽  
Time Information

Data generated from real time information systems are always incremental in nature. Processing of such a huge incremental data in large scale requires a parallel processing system like Hadoop based cluster. Major challenge that arises in all cluster-based system is how efficiently the resources of the system can be used. The research carried out proposes a model architecture for Hadoop cluster with additional components integrated such as super node who manages the clusters computations and a mediation manager who does the performance monitoring and evaluation. Super node in the system is equipped with intelligent or adaptive scheduler that does the scheduling of the job with optimal resources. The scheduler is termed intelligent as it automatically decides which resource to be taken for which computation, with the help of a cross mapping of resource and job with a genetic algorithm which finds the best matching resource. The mediation node deploys ganglia a standard monitoring tool for Hadoop cluster to collect and record the performance parameters of the Hadoop cluster. The system over all does the scheduling of different jobs with optimal usage of resources thus achieving better efficiency compared to the native capacity scheduler in Hadoop. The system is deployed on top of OpenNebula Cloud environment for scalability.     

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