81 research outputs found
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Ontology engineering for simulation component reuse
Commercial-off-the-shelf (COTS) simulation packages (CSPs) are widely used in industry, although they have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organizational use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architectures provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontologies to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community
Opportunities and obligations for physical computing systems
The recent confluence of embedded and real-time systems with wireless, sensor, and networking technologies is creating a nascent infrastructure for a technical, economic, and social revolution. Based on the seamless integration of computing with the physical world via sensors and actuators, this revolution will accrue many benefits. Potentially, its impact could be similar to that of the current Internet. We believe developers must focus on the physical, real-time, and embedded aspects of pervasive computing. We refer to this domain as physical computing systems. For pervasive computing to achieve its promise, developers must create not only high-level system software and application solutions, but also low-level embedded systems solutions. To better understand physical computing\u27s advantages, we consider three application areas: assisted living, emergency response systems for natural or man-made disasters, and protecting critical infrastructures at the national level
SEF-SCC: Software engineering framework for service and cloud computing
© Springer International Publishing AG, part of Springer Nature 2018. Service computing and cloud computing have emerged to address the need for more flexible and cost-efficient computing systems where software is delivered as a service. To make this more resilient and reliable, we need to adopt software engineering (SE) principles and best practices that have existed for the last 40 years or so. Therefore, this chapter proposes a Software Engineering Framework for Service and Cloud Computing (SEF-SCC) to address the need for a systematic approach to design and develop robust, resilient, and reusable services. This chapter presents SEF-SCC methods, techniques, and a systematic engineering process supporting the development of service-oriented software systems and software as a service paradigms. SEF-SCC has been successfully validated for the past 10 years based on a large-scale case study on British Energy Power and Energy Trading (BEPET). Ideas and concepts suggested in this chapter are equally applicable to all distributed computing environments including Fog and Edge Computing paradigms
Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial
Large language models (LLMs) have become phenomenally surging, since
2018--two decades after introducing context-awareness into computing systems.
Through taking into account the situations of ubiquitous devices, users and the
societies, context-aware computing has enabled a wide spectrum of innovative
applications, such as assisted living, location-based social network services
and so on. To recognize contexts and make decisions for actions accordingly,
various artificial intelligence technologies, such as Ontology and OWL, have
been adopted as representations for context modeling and reasoning. Recently,
with the rise of LLMs and their improved natural language understanding and
reasoning capabilities, it has become feasible to model contexts using natural
language and perform context reasoning by interacting with LLMs such as ChatGPT
and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and
autonomous agents (AutoAgents) that enable LLMs to perform context modeling and
reasoning without requiring fine-tuning of the model. We organize and introduce
works in the related field, and name this computing paradigm as the LLM-driven
Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors
reading data, and the command to actuators are supposed to be represented as
texts. Given the text of users' request and sensor data, the AutoAgent models
the context by prompting and sends to the LLM for context reasoning. LLM
generates a plan of actions and responds to the AutoAgent, which later follows
the action plan to foster context-awareness. To prove the concepts, we use two
showcases--(1) operating a mobile z-arm in an apartment for assisted living,
and (2) planning a trip and scheduling the itinerary in a context-aware and
personalized manner.Comment: Under revie
Double-Sided Energy Auction in Microgrid: Equilibrium under Price Anticipation
Citation: Faqiry, M. N., & Das, S. (2016). Double-Sided Energy Auction in Microgrid: Equilibrium under Price Anticipation. Ieee Access, 4, 3794-3805. doi:10.1109/ACCESS.2016.2591912This paper investigates the problem of proportionally fair double-sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A bi-objective optimization problem is identified that takes into account both the agents' social welfare as well as the aggregator's revenue from the surcharge. The results of extensive simulations, which corroborate the theoretical analysis, are reported. © 2013 IEEE
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