A Review on Brain-Controlled Home Automation

A "smart home" employs ambient intelligence to keep tabs on things around the house so that the owner may get services tailored to their specific needs and control their home appliances from afar. Home automation for the elderly and handicapped focuses on enabling older persons and those with disabilities to live safely and comfortably at home. Additionally, the integration of this technology with a brain-computer interface (BCI) is perhaps of tremendous usefulness to those who are either old or disabled. These BCI-based brain-controlled home automation (BCHA) systems have emerged as a viable option for people with neuro disorders to remain in their homes rather than move to assisted living facilities. To summarize, BCI-based BCHA for the elderly and handicapped people is transforming people's lives every day. Most individuals prefer a simple approach to save time and effort. Automating the house is the simplest way for individuals to save time and effort. The brain-computer interface, often known as a BCI, is an innovative method of human-computer connection that does not rely on conventional output channels (muscle tissue and peripheral nerve). Over the course of the last three decades, it has attracted the attention of industry experts and developed into a thriving centre for research. Brain-controlled home automation (BCHA), as a typical BCI application, may provide physically challenged people with a new communication route with the outside world. However, the primary challenge that BCHA faces is to rapidly decipher multi-degree-of-freedom control instructions extracted from an electroencephalogram (EEG). The BCHA's research has made significant headway in a short amount of time during the last fifteen years. This study investigates the BCHA from several viewpoints, including the pattern of instructions for the control system, the type of signal acquisition, and the operational mechanism of the control system itself. This paper a concise description of the building blocks of smart homes and how they may be used to construct BCI-controlled home automation to assist disabled individuals. It is a compilation of information pertaining to communication protocols, multimedia devices, sensors, and systems that are often used in the process of putting smart homes into action. A comprehensive strategy for developing a functional and sustainable BCI-controlled home automation system is laid out in this paper as well, which could be useful to researchers in the future

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report

Knowledge-Based Systems. Overview and Selected Examples

The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications. By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China." This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics

A Knowledge-Based Expert Systems Primer and Catalog

For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog)

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Assessing vulnerability and modelling assistance: using demographic indicators of vulnerability and agent-based modelling to explore emergency flooding relief response

Flooding is a significant concern for much of the UK and is recognised as a primary threat by most local councils. Those in society most often deemed vulnerable: the elderly, poor or sick, for example, often see their level of vulnerability increase during hazard events. A greater knowledge of the spatial distribution of vulnerability within communities is key to understanding how a population may be impacted by a hazard event. Vulnerability indices are regularly used – in conjunction with needs assessments and on-the-ground research – to target service provision and justify resource allocation. Past work on measuring and mapping vulnerability has been limited by a focus on income-related indicators, a lack of consideration of accessibility, and the reliance on proprietary data. The Open Source Vulnerability Index (OSVI) encompasses an extensive range of vulnerability indicators supported by the wider literature and expert validation and provides data at a sufficiently fine resolution that can identify vulnerable populations. Findings of the OSVI demonstrate the potential cascading impact of a flood hazard as it impacts an already vulnerable population: exacerbating pre-existing vulnerabilities, limiting capabilities and restricting accessibility and access to key services. The OSVI feeds into an agent-based model (ABM) that explores the capacity of the British Red Cross (BRC) to distribute relief during flood emergencies using strategies based upon the OSVI. A participatory modelling approach was utilised whereby the BRC were included in all aspects of the model development. The major contribution of this work is the novel synthesis of demographics analysis, vulnerability mapping and geospatial simulation. The project contributes to the growing understanding of vulnerability and response management within the NGO sector. It is hoped that the index and model produced will allow responder organisations to run simulations of similar emergency events and adjust strategic response plans accordingly

Project Retrosight. Understanding the returns from cardiovascular and stroke research: Case Studies

Copyright @ 2011 RAND Europe. All rights reserved. The full text article is available via the link below.This project explores the impacts arising from cardiovascular and stroke research funded 15-20 years ago and attempts to draw out aspects of the research, researcher or environment that are associated with high or low impact. The project is a case study-based review of 29 cardiovascular and stroke research grants, funded in Australia, Canada and UK between 1989 and 1993. The case studies focused on the individual grants but considered the development of the investigators and ideas involved in the research projects from initiation to the present day. Grants were selected through a stratified random selection approach that aimed to include both high- and low-impact grants. The key messages are as follows: 1) The cases reveal that a large and diverse range of impacts arose from the 29 grants studied. 2) There are variations between the impacts derived from basic biomedical and clinical research. 3) There is no correlation between knowledge production and wider impacts 4) The majority of economic impacts identified come from a minority of projects. 5) We identified factors that appear to be associated with high and low impact. This report presents the key observations of the study and an overview of the methods involved. It has been written for funders of biomedical and health research and health services, health researchers, and policy makers in those fields. It will also be of interest to those involved in research and impact evaluation.This study was initiated with internal funding from RAND Europe and HERG, with continuing funding from the UK National Institute for Health Research, the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada and the National Heart Foundation of Australia. The UK Stroke Association and the British Heart Foundation provided support in kind through access to their archives

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

On computational models of animal movement behaviour

Finding structures and patterns in animal movement data is essential towards understanding a variety of behavioural phenomena, as well as shedding light into the relationships between animals among conspecifics and across different taxa with respect to their environments. The recent advances in the field of computational intelligence coupled with the proliferation of low-cost telemetry devices have made the gathering and analyses of behavioural data of animals in their natural habitat and in a wide range of context possible with aid of devices such as Global Positioning System (GPS). The sensory input that animals receive from their environment, and the corresponding motor output, as well as the neural basis of this relationship most especially as it affects movement, encode a lot of information regarding the welfare and survival of these animals and other organisms in nature's ecosystem. This has huge implications in the area of biodiversity monitoring, global health and understanding disease progression. Encoding, decoding and quantifying these functional relationships however can be challenging, boring and labour intensive. Artificial intelligence holds promise in solving some of these problems and even stand to benefit as understanding natural intelligence for instance can aid in the advancement of artificial intelligence. In this thesis, I investigate and propose several computational methods leveraging information theoretic metrics and also modern machine learning methods including supervised, unsupervised and a novel combination of both towards understanding, predicting, forecasting and quantifying a variety of animal movement phenomena at different time scales across different taxa and species. Most importantly the models proposed in this thesis tackle important problems bordering on human and animal welfare as well as their intersection. Crucially, I investigate several information theoretic metrics towards mining animal movement data, after which I propose machine learning and statistical techniques for automatically quantifying abnormal movement behaviour in sheep with Batten disease using unsupervised methods. In addition, I propose a predictive model capable of forecasting migration patterns in Turkey vulture as well as their stop-over decisions using bidirectional recurrent neural networks. And finally, I propose a model of sheep movement behaviour in a flock leveraging insights in cognitive neuroscience with modern deep learning models. Overall, the models of animal movement behaviour developed in this thesis are useful to a wide range of scientists in the field of neuroscience, ethology, veterinary science, conservation and public health. Although these models have been designed for understanding and predicting animal movement behaviour, in a lot of cases they scale easily into other domains such as human behaviour modelling with little modifications. I highlight the importance of continuous research in developing computational models of animal movement behaviour towards improving our understanding of nature in relation to the interaction between animals and their environments