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Emergency Service Provision using a Novel Hybrid SOM-Spiral STC Model for Group Decision Support Under Dynamic Uncertainty
Abstract
In emergency scenarios service vehicles must identify potential route(s) and use the best available route. However, route identification requires intelligent decision-support systems which generally use non-traditional approaches with tools characterised by flexible non-hierarchical structures. Conventional models using group decision-support systems have been applied; however, when used in smart urban environments, emergency response services have limitations in their ability to identify unobstructed paths (routes) in dynamic operating environments. In this paper we introduce a novel path planning method for autonomous vehicle control in emergency situations. The proposed model uses self-organizing maps in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC model which uses hedge algebras and \emph{Kansei} evaluation in group decision-support. The proposed model has been designed to quantify qualitative factors using sensor derived data processed with human sensibilities and preferences in emergency decision support. The experimental results show that the proposed model achieves significant improvements in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.
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Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.
Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.
Disaster Management activities often focus on specific tasks (e.g. evacuation, logistic or coordination) and are confined to one specific DM phase (e.g. Preparedness or Response). New awareness about an external change, be it environmental or organisational, typically act as a trigger for such focussed activities. A variety of views or stakeholders are also involved in those activities, and their various concerns get often intertwined. This work advocates the use of a Decision Support System (DSS) that can be deployed as a single access point. Such a system requires a sufficient amount of representative knowledge, and facilities to avail the knowledge to the appropriate stakeholders in an appropriate form. With the multitude of stakeholders and their varying knowledge requirements, the system will need to present the knowledge differently according to the stakeholders needs in their decision making process. Such processes can vary, e.g. whether for policy making or for operational real time responses. This paper presents a hybrid of knowledge elicitation and retrieval mechanisms, some are top down and others are bottom up. The mechanisms make use of the Meta Object Facility (MOF) to structure and present the knowledge appropriately according to different interests and roles. A case study of the recent Mt. Agung volcano eruption in Bali Indonesia is successfully used to demonstrate the efficacy of the mechanisms proposed and the resultant DSS.
Fire disasters are man-made disasters, which cause ecological, social, and economic damage. To minimize these losses, early detection of fire and an autonomous response are important and helpful to disaster management systems. Therefore, in this article, we propose an early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments. To ensure the autonomous response, we propose an adaptive prioritization mechanism for cameras in the surveillance system. Finally, we propose a dynamic channel selection algorithm for cameras based on cognitive radio networks, ensuring reliable data dissemination. Experimental results verify the higher accuracy of our fire detection scheme compared to state-of-the-art methods and validate the applicability of our framework for effective fire disaster management.
Historically, the utilization of context, the range and scope of context-aware systems, and the levels of computational intelligence in such systems have been very limited. While the inherent complexity is a significant factor, a principal reason for these limitations lies in the failure to incorporate the emotional component. Affective computing technologies are designed to implement innate emotional capabilities and the capability to simulate emotions and empathy; thus, intelligent context-aware systems with affective computing provide a basis upon which we may effectively enable the emotional component. Moreover, machine cognition relies upon affective computing technologies to provide a basis upon which the emotional component may be incorporated. This paper poses the question: do we understand the relationship between affective computing, emotion and context-awareness? The conclusion drawn is that while affective computing and the need for the incorporation of the emotional component is generally understood and domain-specific strategies to enable implementation have been proposed, there remain important challenges and open research questions in relation to the cognitive modelling and the effective incorporation of affective computing and the emotional component in intelligent context-aware systems.
In real decision making problems, it is always more natural for decision makers to use linguistic terms to express their preferences/opinions in a qualitative way among alternatives than to provide quantitative values. Additionally, many of these decision making problems are under uncertain environments with vague and imprecise information involved. Following the idea of Computing with Words (CWW) methodology, we propose in this paper a linguistic valued qualitative aggregation and reasoning framework for multi-criteria decision making problems, where a linguistic valued algebraic structure is constructed for modelling the linguistic information involved in multi-criteria decision making problems, and a linguistic valued logic based approximate reasoning method is developed to infer the final decision making result. This method takes the advantage of handling the linguistic information, no matter totally ordered or partially ordered, directly without numerical approximation, and having a non-classical logic as its formal foundation for decision making process.
Understanding how people interpret risks and choose actions based on their interpretations is vital to any strategy for disaster reduction. We review relevant literature with the aim of developing a conceptual framework to guide future research in this area. We stress that risks in the context of natural hazards always involve interactions between natural (physical) and human (behavioural) factors. Decision-making under conditions of uncertainty is inadequately described by traditional models of 'rational choice'. Instead, attention needs to be paid to how people's interpretations of risks are shaped by their own experience, personal feelings and values, cultural beliefs and interpersonal and societal dynamics. Furthermore, access to information and capacity for self-protection are typically distributed unevenly within populations. Hence trust is a critical moderator of the effectiveness of any policy for risk communication and public engagement.
Pressures on the availability of healthcare spaces, the high costs of institutional care, and the desires of those being cared for, cause a current move toward care either at home or within low-supervision environments. This brings about an important question: how can smart care spaces be created that intelligently link the home care environment to the needs of the cared-for? To a significant degree this involves development of sensored spaces connected to intelligent computer-systems. However, that intelligence requires an understanding of how sensors can provide more than just environmental variables, instead making systems aware of symptoms, comfort and potential needs for intervention. Therefore, this paper discusses the current need for development of smart care spaces, provides an introduction to some of the cost-effective sensors available, and reviews links between sensor data and medical conditions. It will conclude that there is a growing need for smart care spaces that allow effective monitoring of home care, where the cared-for can benefit from that monitoring in a familiar and dignity-preserving environment.
This review examines the policies and practices that address the evolv-ing conditions of risk, security, and disaster management in U.S. society. Although each condition presents particular challenges to public agencies and the communities they serve, all represent varying states of uncertainty and require different approaches for informed action. This analysis reframes the issue of managing risk by focusing on the distinction between policies and practices developed in reference to natural and technological hazards and those developed to enhance security from hostile acts. The author concludes that building networks of organizations committed to a process of continual inquiry, informed action, and adaptive learning is a more flexible, robust strategy than the standard practice of establishing greater control over possible threats through administrative structures. Supported by methods of network analysis, compu-tational simulation, information infrastructure, and long-term policy goals, networked strategies offer an important alternative to hierarchical structures that prove vulnerable in uncertain environments.
This article argues that cognition is central to performance in emergency management. Cognition is defined as the capacity to recognize the degree of emerging risk to which a community is exposed and to act on that information. Using the case of Hurricane Katrina to illustrate the collapse of the standard model of emergency management without a clear focus on the role of cognition, the author reframes the concept of intergovernmental crisis management as a complex, adaptive system. That is, the system needs to adjust and adapt its performance to fit the demands of an ever-changing physical, engineered, and social environment. The terms of cognition, communication, coordination, and control are redefined in ways that fit the reality of practice in extreme events. A reframed intergovernmental crisis management system may be conceived as a dynamic interorganizational system that is characterized by four primary decision points: (1) detection of risk, (2) recognition and interpretation of risk for the immediate context, (3) communication of risk to multiple organizations in a wider region, and (4) self-organization and mobilization of a collective, community response system to reduce risk and respond to danger.
The transition from a rural to urban population represents a demographic, economic, cultural, and environmental tipping point.
In 1800, about 3% of the world’s human population lived in urban areas. By 1900, this proportion rose to approximately 14%
and now exceeds 50% in 2008. Nearly every week 1.3 million additional people arrive in the world’s cities (about 70 million
a year), with increases due to migration being largest in developing countries (Brand 2006, Chan 2007). People in developing
countries have relocated from the countryside to towns and cities of every size during the past 50 years. The urban population
on a global basis is projected by the UN to climb to 61% by 2030 and eventually reach a dynamic equilibrium of approximately
80% urban to 20% rural dwellers that will persist for the foreseeable future (Brand 2006, Johnson 2006). This change from
3% urban population to the projected 80% urban is a massive change in the social–ecological dynamics of the planet.
We propose that Information and Communication Technology should deliver a new experience to the user. We call this experience Kansei Experience. To deliver it we advocate the reliance on a new computing paradigm called cultural computing. Within this paradigm, we develop the concept of Kansei Media and how it could be implemented via Kansei Mediated Interaction. Kansei Media is about sharing implicit knowledge such as feelings, emotions and moods. We aim for a Kansei Experience, rendered by Kansei Media, which relates to reality, and enhances it to enlighten the user. To do so we investigate the aesthetics of the experience we wish to produce. Finally, to develop the concept of Kansei Mediated Experience we refer two cultures (Western and Eastern) and use famous stories from both as means of delivering guidelines for the implementation of Kansei Experience. [Article copies are available for purchase from InfoSci-on-Demand.com]
This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.
Disaster debris management plays a critical role in expediting the disaster recovery process. This study aims to present a framework for effective disaster debris management for a resilient community. The framework consists of a Geographical Information System (GIS) and system dynamics to assess debris removal performance.
GIS was used to conduct a land suitability analysis for a temporary debris management site and system dynamics were applied to evaluate the debris removal performance in different scenarios.
This systemic approach and quantitative analysis enable a decision maker to gain insights into the inter-relationship between critical infrastructure and resources, the effectiveness of temporary debris management sites, and the debris removal performance. Also, a debris management team would have benefits from the framework by being able to (1) understand dynamic behaviors of debris removal operation, (2) evaluate the existing debris management plan, and (3) set up multiple strategies for optimal debris removal operations under different disaster-impact scenarios.
Rescue robots have been used in at least 28 disasters in six countries since the first deployment to the 9/11 World Trade Center collapse. All types of robots have been used (land, sea, and aerial) and for all phases of a disaster (prevention, response, and recovery). This chapter will cover the basic characteristics of disasters and their impact on robotic design, and describe the robots actually used in disasters to date, with a special focus on Fukushima Daiichi, which is providing a rich proving ground for robotics. The chapter covers promising robot designs (e. g., snakes, legged locomotion) and concepts (e. g., robot teams or swarms, sensor networks), as well as progress and open issues in autonomy. The methods of evaluation in benchmarks for rescue robotics are discussed and the chapter concludes with a discussion of the fundamental problems and open issues facing rescue robotics, and their evolution from an interesting idea to widespread adoption.
The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.
This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.
Computing with words and fuzzy linguistic rule based systems play important roles as they can find various significant applications based on simulating human capability. In fuzzy set approaches, words are mapped to fuzzy sets, on which work operations of the developed methodologies. The interpretability of the methodologies depends on how well word semantics is represented by fuzzy sets, which in practice are designed based on human-user‟s intuition. In these approaches there is no formal linkage of fuzzy sets with the inherent semantics of words to ensure the interpretability of fuzzy sets and, hence, fuzzy rules. Hedge algebras, as models of linguistic domains of variables, provide a formalism to generate triangular fuzzy sets of terms from their own semantics. This permits for the first time to design genetically terms along with their integrated triangular fuzzy sets and to construct effective fuzzy rule based classifiers. To answer the question if trapezoidal fuzzy sets can be used instead of triangular fuzzy sets in the above design method, in this study we introduce and develop the so-called enlarged hedge algebras, in which the concept of semantics core of words can be modeled. We show that these algebras provide a formal mechanism to design optimal words integrated with their trapezoidal fuzzy sets as well as fuzzy linguistic rule based classifiers to solve classification problems. Two case studies are examined to show the usefulness of the proposed algebras.
Patient management in hospital environments with the provision of effective care pathways represents a significant challenge. Hospital settings are characterized by uncertainty driven by the need to accommodate both elective treatments and emergency cases [which generally arrive without warning, this can place great strain on the healthcare system. This paper considers these issues along with the potential technological developments which may provide a solution to the challenges faced by healthcare professionals (or at least mitigate the impact of the challenging complex and dynamic hospital environment). It is postulated that technology enhanced patient management based on situational awareness in intelligent context-aware systems has the capability to improve patient experience along with improvements in resource utilization in a hospital setting.
Context is inherently complex requiring intelligent context processing to address the broad and diverse range of available data that when processed can be viewed as contextual information useful in intelligent context-aware systems in a broad range of domains and systems. The function of context-aware systems is to target service provision based on an entities context, an entity has been defined as "a person, place, or physical or computational object". Given the diverse range of available data and entities a primary requirement for an intelligent context-aware system is decision support under uncertainty. This paper provides an overview of context and intelligent context processing. The novel 'extended' context matching algorithm is presented with an overview of fuzzy systems design as it relates to context processing and matching. The design of the membership function is discussed and it is shown that context matching using a membership function based on semantic representation provides a basis upon which the granularity of the context matching result can be improved. Anomalies where the context matching result lies close to a decision boundary are discussed with context processing under uncertainty. The paper concludes with a discussion conclusions, and open research questions.
This paper discusses the basic concepts of computer support for situation awareness. The main idea is to share “situations” by computer and human agents. Two cases of situation awareness are discussed. Some of the future research topics are listed.
It is commonsense that people do express, think, reason, and make decision directly using linguistic terms in natural language rather than using numerical quantification. How to establish the formalized approach imitating the common way of human being’s to manipulate directly linguistic terms without numerical approximation to provide the rational decision is still an open research area. Investigations on the algebraic structure of linguistic term set for varied decision making applications (especially in social science) still lack a formalism for development of strict linguistic valued logic system as a theoretical foundation and its approximate reasoning scheme in practice. To attain this goal we characterize and construct a typical structure of commonly used linguistic term sets in natural language by a lattice-ordered algebra structure – lattice implication algebra (LIA), where Łukasiewicz implication algebra, as a special case of LIA, plays a substantial role. By using Łukasiewicz logic’s axiomatizability in terms of Pavelka type fuzzy logic, we propose a new axiomatizable lattice ordered qualitative linguistic truth-valued logic system based on LIA to place an important foundation for further establishing formal linguistic truth-valued logic based approximate reasoning and decision making with applications. This proposed logic system has a distinct feature of handling comparable or incomparable linguistic terms directly without numerical quantification, will be especially beneficial for perception-based decision making processes. It attempts to enhance the quantitative theory of decision science with qualitative, algebraic and logic-oriented approaches to achieve reasoning with words.
Context-aware systems have traditionally employed a limited range of contextual data. While research is addressing an increasingly broad range of contextual data, the level of intelligence generated in context-aware systems is restricted by the failure to effectively implement emotional response. This paper considers emotion as it relates to context and the application of computational intelligence in context-aware systems. Following an introduction, personalization and the computational landscape is considered and context is introduced. Computational intelligence and the relationship to the Semantic Web is discussed with consideration of the nature of knowledge and a brief overview of knowledge engineering. Cognitive conceptual models and semiotics are introduced with a comparative analysis and approaches to implementation. Ongoing research with illustrative ‘next generation’ intelligent context-aware systems incorporating emotional responses are briefly considered. The paper concludes with a discussion where the challenges and opportunities are addressed; there are closing observations, consideration of future directions for research, and identification of open research questions.
This chapter examines the psychopathology of everyday things. The human mind is exquisitely tailored to make sense of the world. Well-designed objects are easy to interpret and understand. They contain visible clues to their operation. Poorly designed objects can be difficult and frustrating to use. They provide no clues—or sometimes false clues. They trap the user and thwart the normal process of interpretation and understanding. The result is a world filled with frustration, with objects that cannot be understood, with devices that lead to error. A psychology of causality is also at work as people use everyday things. Something that happens right after an action appears to be caused by that action. Touching a computer terminal just when it fails makes a person believe that he caused the failure, even though the failure and his action were related only by coincidence. Such false causality is the basis for much superstition.
Taking Hollands' previous statement on the transition from intelligent to smart cities as its point of departure (‘Will the real smart city stand up?’ City 12(3), 302–320), this article reflects upon the anxieties currently surrounding such developments. In particular, it considers the suggestion that such developments have more to do with cities meeting the corporate needs of marketing campaigns than the social intelligence required for them to be smart. Focusing on the social intelligence of such developments, this article captures the information-rich and highly communicative qualities of the transition. In particular, it examines the methodological issues that smart communities pose cities and the critically insightful role which the networks of innovation and creative partnerships set up to embed such intelligence play in the learning, knowledge transfer and capacity-building exercises servicing this community-led transition to smart cities. This, the article suggests, is what existing representations of smart cities miss. This article offers a critically insightful account of the transition.
Confronted with increasing risk and uncertainty from disruptive change, public managers seek methods to strengthen the capacity of their interdependent organizations to anticipate risk and demonstrate resilience in response to threat. The problem intensifies for public organizations that interact with private and nonprofit organizations to protect a community at risk from natural or technological disasters. It reflects the constraints placed upon human decision processes in complex environments by limited cognitive capacity, and illustrates the persistent difficulty in achieving coordination among multiple organizations with different responsibilities in different locations in serving the public interest.
This article summarizes current research on the design and development of an interactive, intelligent, spatial information system (IISIS) for decision support in the mitigation of, and response to, risk from hazardous materials for a university community. Appropriate uses of the IISIS prototype are expected to increase both the technical and organizational capacity to manage timely, accurate information exchange within and among organizations, thus increasing coordination in action.
Cloud computing is a new field in Internet computing that provides novel perspectives in internetworking technologies and raises issues in the architecture, design, and implementation of existing networks and data centers. The relevant research has just recently gained momentum, and the space of potential ideas and solutions is still far from being widely explored.
Terrorist attacks at the World Trade Center (WTC) in New York City and the Pentagon on September 11, 2001, not only affected multi-level structures in urban areas but also impacted upon their immediate environment at the street level in ways that considerably reduced the speed of emergency response. In this paper, we examine the potential of using real-time 3D GIS for the development and implementation of GIS-based intelligent emergency response systems (GIERS) that aim at facilitating quick emergency response to terrorist attacks on multi-level structures (e.g. multi-story office buildings). We outline a system architecture and a network data model that integrates the ground transportation system with the internal conduits within multi-level structures into a navigable 3D GIS. We examine important implementation issues of GIERS, especially the need for wireless and mobile deployment. Important decision support functionalities of GIERS are also explored with particular reference to the application of network-based shortest path algorithms. Finally, we present the results of an experimental implementation of an integrated 3D network data model using a GIS database of Franklin County, Ohio (USA). Our study shows that response delay within multi-level structures can be much longer than delays incurred on the ground transportation system, and GIERS have the potential for considerably reducing these delays.
The area of disaster management receives increasing attention from multiple disciplines of research. A key role of computer scientists has been in devising ways to manage and analyze the data produced in disaster management situations.In this paper we make an effort to survey and organize the current knowledge in the management and analysis of data in disaster situations, as well as present the challenges and future research directions. Our findings come as a result of a thorough bibliography survey as well as our hands-on experiences from building a Business Continuity Information Network (BCIN) with the collaboration with the Miami-Dade county emergency management office. We organize our findings across the following Computer Science disciplines: data integration and ingestion, information extraction, information retrieval, information filtering, data mining and decision support. We conclude by presenting specific research directions.
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Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are
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