Terrain visualization information integration in agent-based military industrial logistics simulation

Simulation cloning is an efficient way to analyze multiple configurations in a parameter exploration task. This paper presents a generic approach to perform incremental agent-based simulation cloning and discusses its implementation on GPU. Compared with the incremental cloning of parallel and distributed simulation (PADS), cloning agent-based simulation (ABS) has new challenges due to the unique way how ABS is executed. In this paper, to support incremental cloning, mechanisms for both actively and passively cloning agents are proposed. A scheme to maintain the correct context of each cloned ABS instance is developed. In addition, a strategy to restrain the propagation of passive cloning in order to maximize computation sharing amongst cloned ABS instances is also investigated. The implementation of our proposed approach on GPU supports concurrent execution of agents within each simulation instance as well as concurrent execution of multiple simulation instances. Performance of the proposed approach is evaluated and analyzed using a case study of an agent-based evacuation simulation on a NVIDIA Quadro 2000 GPU. Our experiment results demonstrate that cloning can significantly speed up the overall parameter exploration task. The proposed approach achieves 2.4 to 5.1 times speedup for parameter exploration tasks containing 8 to 125 simulation instances that evaluate different parameter configurations.

The complexity of systems and their multi-faceted relationships mean that the combined application of simulation methods, or hybrid simulation, will enable synergies across techniques and will provide greater insights to problem solving. In this paper we present a hybrid simulation case study in which two models have been developed; an Agent-Based Simulation (ABS) and a Discrete-Event Simulation (DES). The models simulate the operations of offshore wind turbines, with the ABS modelling turbine failure (through, among other things, simulating a degradation model of turbine components) and the DES modelling Maintenance, Repair and Operations (MRO) strategies. The models have been implemented in NetLogo and Simul8 respectively. In this paper we review strategies for the interoperation of our hybrid ABS-DES model and discuss the advantages of using the IEEE 1516 HLA standard for distributed simulation for dynamic information exchange and synchronized model execution.

This thesis investigates the use of modelling and simulation techniques in urban areas of smart cities, also exploring how big data can be used to feed these models. These modelling techniques have been applied to two different fields that have been gaining prominence during the last years but where research is still limited: urban logistics and urban resilience. Through this thesis, the author has expanded the research knowledge in these fields by exploring different methods such as meta-heuristics, transport modelling, and agent-based simulation in order to define new methodologies to be applied to urban areas. Regarding logistics, the author has shown through the use of meta-heuristics that when traffic congestion is considered as a dynamic attribute to optimize delivery routes in urban areas, time can be reduced by 11%, which is crucial for logistics companies in a market that is fiercer every day. This is true not only for urban areas, but this research has also demonstrated that optimizing routes with dynamic congestion attributes is also beneficial at a strategic level for routes between cities. To consider congestion costs in real time, a new approach has been developed in which data from Google is downloaded to feed these meta-heuristic models, although other sources of big data could be also used. In this thesis, a methodology is also presented that has been used to model logistics routes in urban areas considering real-time data and with the flexibility to add different network attributes (gradient, traffic bans, CO2, etc.) to simulate different scenarios. This can be useful for logistics companies to optimize their deliveries (choosing between van or tricycles, selecting the time of the day to deliver, etc.) but also for public authorities to get guidance on different transport and urban policies (pedestrianization of some streets, traffic bans, etc.).As for city resilience, the thesis focuses on evacuation planning. A new methodology has been created in which agent-based simulation is used through interconnected sub-models to model a large-scenario evacuation scenario (flooding event as a consequence of a dam collapse). This research defines the data needed to create these models that can be of great help to improve city resilience, and also analyzes how traffic congestion can affect the evacuation procedures. Through the different research articles that compose this thesis, the author brings light to these fields by developing new methodologies and using real case-studies that can help urban planners, companies, and policy makers to create more efficient, sustainable, and resilient smart cities.

By analyzing the difficulty in information integration and sharing of ocean, a hierarchical model, including distributed resource layer, data integration layer, services layer, service integration layer and application layer, was proposed to integrate information. Exchange data format based on ontology was defined to transform data between different modules. In addition, service integrator was introduced to the layer model to achieve service collaboration. Finally, an Ocean Disaster Query System was developed to testify the validity and feasibility of the model.

Logistics system is a complex socio-economic system, and its core is the whole enterprise logistics integration, which includes the integration of the basic processes and objects, information integration of the supply chain, and the business integration of logistics business. In this paper the supply chain management is regarded as a breakthrough point, and on the basis of the research on current supply chain simulation, agent-based supply chain simulation is introduced. In this paper, the agent-based supply chain simulation modeling and realizing method is systematically researched. And the experimental results show that the simulation method is effective and advanced, and it is very effective to solve many problems of supply chain and achieve supply chain optimization, which can generate huge economic benefits for the businesses and the community.

Autonomous vehicles (AVs) may have a transformative influence on transportation systems. AV use is expected to reduce crash, transport costs, and land use. However, the net effects of the autonomous vehicle are unknown because there are no fully-autonomous vehicles in reality. The simulation systems are indispensable to be built up not only for cost but also for obtaining massive data. An agent-based simulation is capable of simulating the entities in a complex system, such as the AV system. Considering the potential of agent-based models to complement existing models and the difficulty to understand, to duplicate and to compare different agent-based models, this paper review existing agent-based modeling of autonomous vehicles with the help of ODD protocol, which has been applied in the review of agent-based land-use change models and agricultural policy evaluation. The significant variables that researchers have taken into consideration in sensitivity analysis and in the different scenarios are explored in the paper. Fleet size is the primary variable researchers considered, and we also discussed the probable causes of different AV replacement rates. We have assessed the quality of reviewed articles to gain more acceptance from the agent-based modeling community. Most of the reviewed papers follow the ODD protocol of agent decision making. However, in order to improve the level of modeling transparency, the executable and related data shall be available, at least the data source and the programming language/simulation platform should be provided. Several recommendations for further research are also presented.

Lifelong learning has become an essential component in a professional’s career path. With the wide use of mobile devices in working environments, professionals can acquire knowledge anytime and anywhere. For healthcare providers, their clinical knowledge acquisition in a timely manner can significantly improve patient treatment. In this paper, we present a real-time professional content recommendation system for healthcare providers’ knowledge acquisition. The system includes five layers: Data Layer (healthcare provider profile, learning behavior, social network and etc.), Algorithm Layer (clinical/medical knowledge classification, medical expertise similarity measurement, professional-knowledge matching algorithms and etc.), Service Layer (click/browsing behavior monitor, healthcare provider feedback collection and etc.), Application Layer (medical content recommendation, retrieval optimization and etc.) and Management Layer (clinical scenario configuration, medical terminology management and etc.). The system has been applied in a knowledge service applications targeting clinicians in mobile Health (mHealth) scenario.

This paper designs a frame system of Digital Ocean including the supporting layer, base layer, data layer, service layer, application layer and assurance layer. The framework system realizes the effective integration, exchanging and sharing of the marine information, the basic geographical information and all kinds of closely related economic and social information by using the open architecture and standardized service mode, and it provides the structure template for the construction of the Digital Ocean.

China is a country with vast ocean area, long coastline and abundant marine resources. In recent years, the State Council has put forward a series of strategic plans for building a powerful maritime country, which requires to strengthen the capacity of building the omni-directional stereoscopic ocean monitoring system. On September 12, 2017, the State Oceanic Administration issued the fax about notice on carrying out the research on aerospace science and technology serving in marine development. It is worth mentioning that the rapid development of UAV technology has brought new opportunities to the marine application. The UAV system can be used as an important supplement method compared with satellites, ships and helicopters, which could provide a good guarantee for reconnaissance, surveillance, intelligence collection, target positioning and emergency response missions with its high reliability, high efficiency and low cost and low risk. Construction and application of ocean big data platform based on UAV system is a hierarchical architecture including application layer, service layer, access layer and resource layer from top to bottom. Every layer has its responsibility and they cooperate with each other to provide the big data service and application throughout. The platform is obviously superior to others with its diversified, comprehensive, personalized and intelligent characteristics, which can efficiently provide real-time or quasi real-time marine environment data, continuously execute missions in all-weather condition day and night, flexibly approach the accident site and immediately take emergency measures. As what is shown in picture 1, the resource layer provides a wide range of hardware, software and data resources. The access layer provides a comprehensive access means with different kinds of UAV systems, which is the core component of the whole platform. The cruise altitude of CH-AUV, CH-804C, CH-3, CH-4, CH-5 and CH-T4 is respectively 1500m in the sea, 100m~3000m,80m~6400m,200m~7200m,500m~8000m,1000m~25000m, which could fully cover of the space base, air base ,land base and sea base. The max endurance of those above is10h,4h,12h,30h,40h,30days respectively, which makes it possible to execute the missions in all-weather condition. The max payload capacity ranges from 3kg to 1050kg, so the optical, microwave and laser payloads can be carried at the same time. The service layer is a data center providing big data modeling, retrieval, association, mining and integration service where users can get these diversified services and accurate data easily at any time anywhere. The service layer provides data security for the application layer and the application layer gives feedback to the service layer according to the application requirements. These two layers supplement each other to improve the quality and efficiency of the whole platform. The application layer consists of two parts: the basic application and the integrated application. The basic application meet the basic requirements of marine applications including ocean environmental protection, disaster prevention and mitigation, maritime law enforcement, ocean and islands monitoring and emergency response. The integrated application including environmental forecast, disaster warning and situation analysis shows the high intelligence of the platform, which just describes the real smart ocean system. Above all, construction and application of ocean big data platform based on UAV system can not only provide a variety of data accessing methods, but also provide intelligent services and applications. It will bring new chapters to intelligent monitoring, dynamic prediction, systematic analysis, emergency response and other marine applications with its capacity of all directions service, all time accessing and all business coverage.

Healthcare systems are designed to facilitate the end users in order to maintain the good health and predicting the future trends for safety measures. Most of the systems running right now are assisting the users with the number of services like m-health, e-health. Although, many of the systems are operative, but still a lot of problems are to be addressed. The data related to healthcare industry is extremely sensitive, that could not be altered or edited by any source, and likely many problems of privacy and security are still maintained in the current systems. Though, many systems are still working on the security challenges but they are struggling to resolve the related issues. To secure patients data is the biggest deal to solve. We will try to overcome the problem related to security by proposing a framework, named as Service Oriented Architecture for Health care based on cloud computing (SOHA-CC). This framework contains four layers, specifically, Application layer, Cloud application and Service layer, Network computing layer and, finally Healthcare layer.

Spatial agent-based simulations of infectious disease epidemics require a high-resolution regional population model. However, only aggregated demographic data is available for most geographic regions. Furthermore, the infectious disease application case can require the fusion of multiple data sources (e.g. census and public health statistics), inducing demand for a modular and extensible modeling approach. In this work we provide a novel sequential sample-free approach to generate synthetic baseline populations for agent-based simulations, combining synthetic reconstruction and combinatorial optimization. We applied the approach to generate a population model for the German state of North Rhine-Westphalia (17.5 million inhabitants) which yielded an average accuracy of around 98% per attribute. The resulting population model is publicly available and has been utilized in multiple simulation-based infectious disease case studies. We suggest that our research can pave the way for more geographically granular synthetic populations to be used in model-driven infectious disease epidemics prediction and prevention.

A practical component-based WebGIS named as Geo-Union is presented. Geo-Union consists of four layers: storage layer, service layer, component layer and application layer. Service layer can be partitioned into another two layers: client service layer and server service layer. The architectures and object constitutions of each layer in Geo-Union are discussed in details. The Web application model of Geo-Union is also presented. At last, some future works in WebGIS, such as interoperability, security, distributed computing and intelligent computing, are indicated and simply explored.KeywordsService LayerTool ObjectSpatial EntityStorage LayerGeometry ObjectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Uncertainty management and information integration have been challenging issues in AI and database research. The literature is vast and rich on either of these two issues, however, they have not been studied simultaneously in the same setting. In this work, we make a first attempt and propose a framework for information integration with uncertainty, which uses the information source tracking(IST) model [9] as the underlying certainty model. The IST model is an extension of the relational data model in which every tuple t is annotated with (a set of) fixed length vectors, called agent vectors, representing the (human or sensor) agents which confirmed t or contributed to it. Our framework consists of a dynamic collection of autonomous but cooperating IST databases, called the information sources or sites, in which each relation r is annotated with a site vector, indicating which sites contributed to the definition of r. We extend the relational algebra from the basic IST model accordingly to manipulate agent and site vectors. We also extend the reliability calculation algorithm from the basic model to compute the certainty of each answer tuple as a function of the reliabilities of the contributing agents and sites. We have developed a running prototype of the proposed framework for which we mainly used SQL programming for query rewriting and manipulation of agent and site vectors.KeywordsQuery ProcessingQuery ResultInformation IntegrationRelational AlgebraAgent AttributeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

CoronaBio: Using Crowdsourcing for Biomedical Research on COVID-19 to Manage a Pandemic

In order to solve the problems of the present University information system, this paper presents a campus information portal solution based on Portal technology, which divide the campus portal system into four layers: base layer, data layer, application layer and presentation layer. This architecture to provide basic services of traditional school information systems and campus portal-specific single sign-on, data synchronization, application integration and personalization services and other functions. Base layer includes hardware, system software, network infrastructure and Email, WWW, FTP and other basic services, data layer is formed by the database and LDAP, application layer contains the ESB, unified authentication and the application system, presentation layer consists of the portal and browser. The presentation layer and application layer exchange information in the form of Portlet.