Improving the health forecasting alert system for cold weather and heat-waves in England: a proof-of-concept using temperature-mortality relationships

Objectives In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. Method The prototype health forecasting alert system introduces an “impact vs likelihood matrix” for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. Conclusions The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.

TCA Evaluation : Lab Measurements, Modelling and System Simulations

The study reported here is part of a large project for evaluation of the Thermo-Chemical Accumulator (TCA), a technology under development by the Swedish company ClimateWell AB. The studies concentrate on the use of the technology for comfort cooling. This report concentrates on measurements in the laboratory, modelling and system simulation. The TCA is a three-phase absorption heat pump that stores energy in the form of crystallised salt, in this case Lithium Chloride (LiCl) with water being the other substance. The process requires vacuum conditions as with standard absorption chillers using LiBr/water. Measurements were carried out in the laboratories at the Solar Energy Research Center SERC, at Högskolan Dalarna as well as at ClimateWell AB. The measurements at SERC were performed on a prototype version 7:1 and showed that this prototype had several problems resulting in poor and unreliable performance. The main results were that: there was significant corrosion leading to non-condensable gases that in turn caused very poor performance; unwanted crystallisation caused blockages as well as inconsistent behaviour; poor wetting of the heat exchangers resulted in relatively high temperature drops there. A measured thermal COP for cooling of 0.46 was found, which is significantly lower than the theoretical value. These findings resulted in a thorough redesign for the new prototype, called ClimateWell 10 (CW10), which was tested briefly by the authors at ClimateWell. The data collected here was not large, but enough to show that the machine worked consistently with no noticeable vacuum problems. It was also sufficient for identifying the main parameters in a simulation model developed for the TRNSYS simulation environment, but not enough to verify the model properly. This model was shown to be able to simulate the dynamic as well as static performance of the CW10, and was then used in a series of system simulations. A single system model was developed as the basis of the system simulations, consisting of a CW10 machine, 30 m2 flat plate solar collectors with backup boiler and an office with a design cooling load in Stockholm of 50 W/m2, resulting in a 7.5 kW design load for the 150 m2 floor area. Two base cases were defined based on this: one for Stockholm using a dry cooler with design cooling rate of 30 kW; one for Madrid with a cooling tower with design cooling rate of 34 kW. A number of parametric studies were performed based on these two base cases. These showed that the temperature lift is a limiting factor for cooling for higher ambient temperatures and for charging with fixed temperature source such as district heating. The simulated evacuated tube collector performs only marginally better than a good flat plate collector if considering the gross area, the margin being greater for larger solar fractions. For 30 m2 collector a solar faction of 49% and 67% were achieved for the Stockholm and Madrid base cases respectively. The average annual efficiency of the collector in Stockholm (12%) was much lower than that in Madrid (19%). The thermal COP was simulated to be approximately 0.70, but has not been possible to verify with measured data. The annual electrical COP was shown to be very dependent on the cooling load as a large proportion of electrical use is for components that are permanently on. For the cooling loads studied, the annual electrical COP ranged from 2.2 for a 2000 kWh cooling load to 18.0 for a 21000 kWh cooling load. There is however a potential to reduce the electricity consumption in the machine, which would improve these figures significantly. It was shown that a cooling tower is necessary for the Madrid climate, whereas a dry cooler is sufficient for Stockholm although a cooling tower does improve performance. The simulation study was very shallow and has shown a number of areas that are important to study in more depth. One such area is advanced control strategy, which is necessary to mitigate the weakness of the technology (low temperature lift for cooling) and to optimally use its strength (storage).

Monitoring results of combined pellet and solar heating system

In this study the monitoring results of prototype installation of a recently developed solar combisystem have been evaluated. The system, that uses a water jacketed pellet stove as auxiliary heater, was installed in a single family house in Borlänge/Sweden. In order to allow an evaluation under realistic conditions the system has been monitored for a time period of one year. From the measurements of the system it could be seen that it is important that the pellet stove has a sufficient buffer store volume to minimize cycling. The measurements showed also that the stove gives a lower share of the produced heat to the water loop than measured under stationary conditions. The solar system works as expected and covers the heat demand during the summer and a part of the heat demand during spring and autumn. Potential for optimization exists for the parasitic electricity demand. The system consumes 680 kWh per year for pumps, valves and controllers which is more than 4% of the total primary heating energy demand.

Prototype Generation from Ontology Charts

Semantic Analysis is a business analysis method designed to capture system requirements. While these requirements may be represented as text, the method also advocates the use of Ontology Charts to formally denote the system's required roles, relationships and forms of communication. Following model driven engineering techniques, Ontology Charts can be transformed to temporal Database schemas, class diagrams and component diagrams, which can then be used to produce software systems. A nice property of these transformations is that resulting system design models lend themselves to complicated extensions that do not require changes to the design models. For example, resulting databases can be extended with new types of data without the need to modify the database schema of the legacy system. Semantic Analysis is not widely used in software engineering, so there is a lack of experts in the field and no design patterns are available. This make it difficult for the analysts to pass organizational knowledge to the engineers. This study describes an implementation that is readily usable by engineers, which includes an automated technique that can produce a prototype from an Ontology Chart. The use of such tools should enable developers to make use of Semantic Analysis with minimal expertise of ontologies and MDA.

Kanban devs modelling, simulation and verification

Kanban Control Systems (KCS) have become a widely accepted form of inventory and production control. The creation of realistic Discrete Events Simulation (DES) models of KCS require specification of both information and material flow. There are several commercially available simulation packages that are able to model these systems although the use of an application specific modelling language provides means for rapid model development. A new Kanban specific simulation language as well as a high-speed execution engine is verified in this paper through the simulation of a single stage single part type production line. A single stage single part KCS is modelled with exhaustive enumeration of the decision variables of container sizes and number of Kanbans. Several performance measures were used; 95% Confidence Interval (CI) of container Flow Time (FT), mean line throughput as well as the Coefficient of Variance (CV) of FT and Cycle Time were used to determine the robustness of the control system.

Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

System of systems approach to threat detection and integration of heterogeneous independently operable systems

This paper presents a system of systems approach to threat detection through integration of heterogeneous independently operable systems. The approach is presented on a realistic situation where a human-controlled base robot, swarm robot(s), and sensors work together to obtain a decision about a possible threat in the environment. The base robot is remotely operated by a human using a haptic control system. The swarm robot(s) are autonomous and can accept directives from the base robot. Finally, sensors directly communicate with (report to) the base robot. In this scenario, heterogeneous systems and human interact in a system of systems architecture. With the inclusion of human expert and sensor verification of swarm robots, the system can successfully perform the threat detection and reduce the false alarms. Finally, a system of systems simulation framework including a base robot, a swarm robot, and two sensors is presented in addition to an experimental evaluation of the proposed SoS architecture

Detecting and tracing DDoS attacks by intelligent decision prototype

Over the last couple of months a large number of distributed denial of service (DDoS) attacks have occurred across the world, especially targeting those who provide Web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called intelligent decision prototype (IDP), is proposed. IDP can be used on both probabilistic packet marking (PPM) and deterministic packet marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as data mining, forensic analysis, intrusion detection systems (IDS) and DDoS defense systems.

Applications and prototype for systems of systems swarm robotics

In order to develop a robotic system of systems the robotic platforms must be designed and built. For this to happen, the type of application involved should be clear. Swarm robots need to be self contained and powered. They must also be self governing. Here the authors examine various applications and a prototype robot that may be useful in these scenarios.

Haptically enabled interactive virtual reality prototype for general assembly

Desktop computers based virtual training systems are attracting paramount attention from manufacturing industries due to their potential advantages over the conventional training practices. Significant cost savings can be realized due to the shorter training-scenarios development times and reuse of existing engineering models. In addition, by using computer based virtual reality (VR) training systems, the time span from the product design to commercial production can be shortened due to non-reliance on hardware parts. Within the aforementioned conceptual framework, a haptically enabled interactive and immersive virtual reality (HIIVR) system is presented. Unlike existing VR systems, the presented idea tries to imitate real physical training scenarios by providing comprehensive user interaction, constrained within the physical limitations of the real world imposed by the haptics devices within the virtual environment. As a result, in contrast to the existing VR systems, capable of providing knowledge generally about assembly sequences only, the proposed system helps in procedural learning and procedural skill development as well, due to its high physically interactive nature.

Development of a building integrated photovoltaic/thermal solar energy cogeneration system

Using renewable energy sources for onsite cogeneration from structural building elements is a relatively new concept and is gaining considerable interest. In this study the design, development, manufacturing and testing of a novel building integrated photovoltaic/thermal (BIPVT) solar energy cogeneration system is discussed.

Adhesives (ADH), resistance seam welding (RSW) and autoclaving (ATC) were identified as the most appropriate for fabricating BIPVT roofing panels. Of these manufacturing methods ADH was found to be most suitable for low volume production systems due to its low capital cost.

A prototype panel, fabricated using ADH methods, exhibited good thermal performance. It was also shown that BIPVT performance could be theoretically predicted using a one dimensional heat transfer model and showed excellent agreement with experimental data. The model was used to suggest further design improvements. Finally, a transient simulation of the BIPVT was performed in TRNSYS and is used to illustrate the benefits of the system.

Intelligent assistant to re-configure parameter-driven systems

Parameter-Driven Systems (PDS) are widely used in commerce for large-scale applications. Reusability is achieved with a PDS design by relocating implicit control structures in the software and the storage of explicit data in database files. This approach can accommodate various user requirements without tedious modification of the software. In order to specify appropriate parameters in a system, knowledge of both business activities and system behaviour are required. For large, complex software packages, this task becomes time consuming and requires specialist knowledge, yet the consistency and correctness still cannot be guaranteed. My research studied the types of knowledge required and agents involved in the PDS customisation. The work also identified the associated problems and constraints. A solution is proposed and implemented as an Intelligent Assistant prototype than a manual approach. Three areas of achievement have been highlighted: 1. The characteristics and problems of maintaining parameter instances in a PDS are defined. It is found that the verification is not complete with the technical/structural knowledge alone, but a context is necessary to provide semantic information and related business activities (thus the implemented parameters) so that mainline functions can relate with each other. 2. A knowledge-based modelling approach has been proposed and demonstrated via a practical implementation. A Specification Language was designed which can model various types of knowledge in a PDS and encapsulate relationships. The Knowledge-Based System (KBS) developed verifies parameters based on the interpreted model of a given context. 3. The performance of the Intelligent Assistant prototype was well received by the domain specialist from the participating organisation. The modelling and KBS approach developed in my research offers considerable promise in solving practical problems in the software industry.