Executive control systems in the engineering design environment

An executive control system (ECS) is a software structure for unifying various applications codes into a comprehensive system. It provides a library of applications, a uniform access method through a cental user interface, and a data management facility. A survey of twenty-four executive control systems designed to unify various CAD/CAE applications for use in diverse engineering design environments within government and industry was conducted. The goals of this research were to establish system requirements to survey state-of-the-art architectural design approaches, and to provide an overview of the historical evolution of these systems. Foundations for design are presented and include environmental settings, system requirements, major architectural components, and a system classification scheme based on knowledge of the supported engineering domain(s). An overview of the design approaches used in developing the major architectural components of an ECS is presented with examples taken from the surveyed systems. Attention is drawn to four major areas of ECS development: interdisciplinary usage; standardization; knowledge utilization; and computer science technology transfer

Efcacy of sodium bicarbonate ingestion strategies for protecting blinding

Sodium bicarbonate (NaHCO3) is a widely researched ergogenic aid, but the optimal blinding strategy during randomised placebo-controlled trials is unknown. In this multi-study project, we aimed to determine the most efficacious ingestion strategy for blinding NaHCO3 research. During study one, 16 physically active adults tasted 0.3 g·kg-1 body mass NaHCO3 or 0.03 g·kg-1 body mass sodium chloride placebo treatments given in different flavour (orange, blackcurrant) and temperature (chilled, room temperature) solutions. They were required to guess which treatment they had received. During study two, 12 recreational athletes performed time-to-exhaustion (TTE) cycling trials (familiarisation, four experimental). Using a randomised, double-blind design, participants consumed 0.3 g·kg-1 body mass NaHCO3 or a placebo in 5 mL·kg-1 body mass chilled orange squash/water solutions or capsules and indicated what they believed they had received immediately after consumption, pre-TTE and post-TTE. In study one, NaHCO3 prepared in chilled orange squash resulted in the most unsure ratings (44%). In study two, NaHCO3 administered in capsules resulted in more unsure ratings (% here) than NaHCO3 (% here) given in solution, with differences in treatment assignment after consumption, epre-TTE, and post-TTE (all p<0.05). Administering NaHCO3 in capsules was the most efficacious blinding strategy which provides important implications for researchers conducting randomised placebo-controlled trials

A Cyber-Support System for Distributed Infrastructures

The Internet is now heavily relied upon by the Critical Infrastructures (CI). This has led to different security threats facing interconnected security systems. By understanding the complexity of critical infrastructure interdependency, and how to take advantage of it in order to minimize the cascading problem, enables the prediction of potential problems before they happen. Our proposed system, detailed in this paper, is able to detect cyber-attacks and share the knowledge with interconnected partners to create an immune system network. In order to demonstrate our approach, a realistic simulation is used to construct data and evaluate the system put forward. This paper provides a summary of the work to-date, on the development of a system titled Critical Infrastructure Auto-Immune Response System (CIAIRS). It provides a view of the main CIAIRS segments, which comprise the framework and illustrates the functioning of the system

Long range correlations in DNA : scaling properties and charge transfer efficiency

We address the relation between long range correlations and charge transfer efficiency in aperiodic artificial or genomic DNA sequences. Coherent charge transfer through the HOMO states of the guanine nucleotide is studied using the transmission approach, and focus is made on how the sequence-dependent backscattering profile can be inferred from correlations between base pairs.Comment: Submitted to Phys. Rev. Let

Low complexity lossless compression of underwater sound recordings

Author Posting. © Acoustical Society of America, 2013. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 133 (2013): 1387-1398, doi:10.1121/1.4776206.Autonomous listening devices are increasingly used to study vocal aquatic animals, and there is a constant need to record longer or with greater bandwidth, requiring efficient use of memory and battery power. Real-time compression of sound has the potential to extend recording durations and bandwidths at the expense of increased processing operations and therefore power consumption. Whereas lossy methods such as MP3 introduce undesirable artifacts, lossless compression algorithms (e.g., flac) guarantee exact data recovery. But these algorithms are relatively complex due to the wide variety of signals they are designed to compress. A simpler lossless algorithm is shown here to provide compression factors of three or more for underwater sound recordings over a range of noise environments. The compressor was evaluated using samples from drifting and animal-borne sound recorders with sampling rates of 16–240 kHz. It achieves >87% of the compression of more-complex methods but requires about 1/10 of the processing operations resulting in less than 1 mW power consumption at a sampling rate of 192 kHz on a low-power microprocessor. The potential to triple recording duration with a minor increase in power consumption and no loss in sound quality may be especially valuable for battery-limited tags and robotic vehicles.Algorithm development was supported by SERDP, ONR, US Navy (N45) and NOPP. M.J. was supported by the Marine Alliance for Science and Technology Scotland (MASTS)

Profiling Users in the Smart Grid

The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier

A Smart Health Monitoring Technology

With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing

Smart Monitoring: An Intelligent System to Facilitate Health Care across an Ageing Population

In the UK, the number of people living with self-limiting conditions, such as Dementia, Parkinson’s disease and depression, is increasing. The resulting strain on national healthcare resources means that providing 24-hour monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade. Our research directly proposes an alternative and cost effective method for supporting independent living that offers enhancements for Early Intervention Practices (EIP). In the UK, a national roll out of smart meters is underway, which enable detailed around-the-clock monitoring of energy usage. This granular data captures detailed habits and routines through the users’ interactions with electrical devices. Our approach utilises this valuable data to provide an innovative remote patient monitoring system. The system interfaces directly with a patient’s smart meter, enabling it to distinguish reliably between subtle changes in energy usage in real-time. The data collected can be used to identify any behavioural anomalies in a patient’s habit or routine, using a machine learning approach. Our system utilises trained models, which are deployed as web services using cloud infrastructures, to provide a comprehensive monitoring service. The research outlined in this paper demonstrates that it is possible to classify successfully both normal and abnormal behaviours using the Bayes Point Machine binary classifier

Identifying Behavioural Changes for Health Monitoring Applications using the Advanced Metering Infrastructure

The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions and legislation. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient’s condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. Where such technologies are used, they are considered too intrusive and often incapable of being personalised to the individual needs of patients. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of emerging applications that are unachievable using the traditional energy grid. Between now and 2020, UK energy suppliers will install and configure of 50 million smart meters therefore providing access to a highly accurate sensing network. Each smart meter records accurately the electrical load for a given property at 30 minute intervals, 24 hours a day. This granular data captures detailed habits and routines through the occupant’s interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders, such as Dementia

Anomalous jumping in a double-well potential

Noise induced jumping between meta-stable states in a potential depends on the structure of the noise. For an $\alpha$-stable noise, jumping triggered by single extreme events contributes to the transition probability. This is also called Levy flights and might be of importance in triggering sudden changes in geophysical flow and perhaps even climatic changes. The steady state statistics is also influenced by the noise structure leading to a non-Gibbs distribution for an $\alpha$-stable noise.Comment: 11 pages, 7 figure