Fusing data-mining and knowledge elicitation for the supervisory control of an aluminium plate mill

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

Diurnal and seasonal cycles of cloud occurrences, types and radiative impact over West Africa

This study focuses on the occurrence and type of clouds observed in West Africa, a subject which has neither been much documented nor quantified. It takes advantage of data collected above Niamey in 2006 with the ARM mobile facility. A survey of cloud characteristics inferred from ground measurements is presented with a focus on their seasonal evolution and diurnal cycle. Four types of clouds are distinguished: high-level clouds, deep convective clouds, shallow convective clouds and mid-level clouds. A frequent occurrence of the latter clouds located at the top of the Saharan Air Layer is highlighted. High-level clouds are ubiquitous throughout the period whereas shallow convective clouds are mainly noticeable during the core of the monsoon. The diurnal cycle of each cloud category and its seasonal evolution is investigated. CloudSat and CALIPSO data are used in order to demonstrate that these four cloud types (in addition to stratocumulus clouds over the ocean) are not a particularity of the Niamey region and that mid-level clouds are present over the Sahara during most of the Monsoon season. Moreover, using complementary data sets, the radiative impact of each type of clouds at the surface level has been quantified in the shortwave and longwave domain. Mid-level clouds and anvil clouds have the largest impact respectively in longwave (about 15 W m−2) and the shortwave (about 150 W m−2). Furthermore, mid-level clouds exert a strong radiative forcing in Spring at a time when the other cloud types are less numerous.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases

The occurrence of wind storms in Central Europe is investigated with respect to large-scale atmospheric flow and local wind speeds in the investigation area. Two different methods of storm identification are applied for Central Europe as the target region: one based on characteristics of large-scale flow (circulation weather types, CWT) and the other on the occurrence of extreme wind speeds. The identified events are examined with respect to the NAO phases and CWTs under which they occur. Pressure patterns, wind speeds and cyclone tracks are investigated for storms assigned to different CWTs. Investigations are based on ERA40 reanalysis data. It is shown that about 80% of the storm days in Central Europe are connected with westerly flow and that Central European storm events primarily occur during a moderately positive NAO phase, while strongly positive NAO phases (6.4% of all days) account for more than 20% of the storms. A storm occurs over Central Europe during about 10% of the days with a strong positive NAO index. The most frequent pathway of cyclone systems associated with storms over Central Europe leads from the North Atlantic over the British Isles, North Sea and southern Scandinavia into the Baltic Sea. The mean intensity of the systems typically reaches its maximum near the British Isles. Differences between the characteristics for storms identified from the CWT identification procedure (gale days, based on MSLP fields) and those from extreme winds at Central European grid points are small, even though only 70% of the storm days agree. While most storms occur during westerly flow situations, specific characteristics of storms during the other CWTs are also considered. Copyright © 2009 Royal Meteorological Society

Monitoring present day changes in water vapour and the radiative energy balance using satellite data, reanalyses and models

A combination of satellite data, reanalysis products and climate models are combined to monitor changes in water vapour, clear-sky radiative cooling of the atmosphere and precipitation over the period 1979-2006. Climate models are able to simulate observed increases in column integrated water vapour (CWV) with surface temperature (Ts) over the ocean. Changes in the observing system lead to spurious variability in water vapour and clear-sky longwave radiation in reanalysis products. Nevertheless all products considered exhibit a robust increase in clear-sky longwave radiative cooling from the atmosphere to the surface; clear-sky longwave radiative cooling of the atmosphere is found to increase with Ts at the rate of ~4 Wm-2 K-1 over tropical ocean regions of mean descending vertical motion. Precipitation (P) is tightly coupled to atmospheric radiative cooling rates and this implies an increase in P with warming at a slower rate than the observed increases in CWV. Since convective precipitation depends on moisture convergence, the above implies enhanced precipitation over convective regions and reduced precipitation over convectively suppressed regimes. To quantify this response, observed and simulated changes in precipitation rate are analysed separately over regions of mean ascending and descending vertical motion over the tropics. The observed response is found to be substantially larger than the model simulations and climate change projections. It is currently not clear whether this is due to deficiencies in model parametrizations or errors in satellite retrievals.

An observing system simulation experiment for climate monitoring with GNSS radio occulation data: setup and testbed study

The long-term stability, high accuracy, all-weather capability, high vertical resolution, and global coverage of Global Navigation Satellite System (GNSS) radio occultation (RO) suggests it as a promising tool for global monitoring of atmospheric temperature change. With the aim to investigate and quantify how well a GNSS RO observing system is able to detect climate trends, we are currently performing an (climate) observing system simulation experiment over the 25-year period 2001 to 2025, which involves quasi-realistic modeling of the neutral atmosphere and the ionosphere. We carried out two climate simulations with the general circulation model MAECHAM5 (Middle Atmosphere European Centre/Hamburg Model Version 5) of the MPI-M Hamburg, covering the period 2001–2025: One control run with natural variability only and one run also including anthropogenic forcings due to greenhouse gases, sulfate aerosols, and tropospheric ozone. On the basis of this, we perform quasi-realistic simulations of RO observables for a small GNSS receiver constellation (six satellites), state-of-the-art data processing for atmospheric profiles retrieval, and a statistical analysis of temperature trends in both the “observed” climatology and the “true” climatology. Here we describe the setup of the experiment and results from a test bed study conducted to obtain a basic set of realistic estimates of observational errors (instrument- and retrieval processing-related errors) and sampling errors (due to spatial-temporal undersampling). The test bed results, obtained for a typical summer season and compared to the climatic 2001–2025 trends from the MAECHAM5 simulation including anthropogenic forcing, were found encouraging for performing the full 25-year experiment. They indicated that observational and sampling errors (both contributing about 0.2 K) are consistent with recent estimates of these errors from real RO data and that they should be sufficiently small for monitoring expected temperature trends in the global atmosphere over the next 10 to 20 years in most regions of the upper troposphere and lower stratosphere (UTLS). Inspection of the MAECHAM5 trends in different RO-accessible atmospheric parameters (microwave refractivity and pressure/geopotential height in addition to temperature) indicates complementary climate change sensitivity in different regions of the UTLS so that optimized climate monitoring shall combine information from all climatic key variables retrievable from GNSS RO data.

Ensemble-based data assimilation and the localisation problem

The “butterfly effect” is a popularly known paradigm; commonly it is said that when a butterfly flaps its wings in Brazil, it may cause a tornado in Texas. This essentially describes how weather forecasts can be extremely senstive to small changes in the given atmospheric data, or initial conditions, used in computer model simulations. In 1961 Edward Lorenz found, when running a weather model, that small changes in the initial conditions given to the model can, over time, lead to entriely different forecasts (Lorenz, 1963). This discovery highlights one of the major challenges in modern weather forecasting; that is to provide the computer model with the most accurately specified initial conditions possible. A process known as data assimilation seeks to minimize the errors in the given initial conditions and was, in 1911, described by Bjerkness as “the ultimate problem in meteorology” (Bjerkness, 1911).

Soil disturbed using a strip tillage implement on a range of soil types and the effects on sugar beet establishment

The area of soil disturbed using a single tine is well documented. However, modern strip tillage implements using a tine and disc design have not been assessed in the UK or in mainland Europe. Using a strip tillage implement has potential benefits for European agriculture where economic returns and sustainability are key issues. Using a strip tillage system a narrow zone is cultivated leaving most of the straw residue on the soil surface. Small field plot experiments were undertaken on three soil types and the operating parameters of forward speed, tine depth and tine design were investigated together with measurements of seedbed tilth and crop emergence. The type of tine used was found to be the primary factor in achieving the required volume of disturbance within a narrow zone whilst maintaining an area of undisturbed soil with straw residue on the surface. The winged tine produced greater disturbance at a given depth compared with the knife tine. Increasing forward speed did not consistently increase the volume of disturbance. In a sandy clay loam the tilth created and emergence of sugar beet by strip tillage and ploughing were similar but on a sandy loam the strip tillage treatments generally gave a finer tilth but poorer emergence particularly at greater working depth.

The use of wireless data communication and body sensing devices to evaluate occupants' comfort in buildings

Physiological parameters measured by an embedded body sensor system were demonstrated to respond to changes of the air temperature in an office environment. The thermal parameters were monitored with the use of a wireless sensor system that made possible to turn any existing room into a field laboratory. Two human subjects were monitored over daily activities and at various steady-state thermal conditions when the air temperature of the room was altered from 22-23°C to 25-28°C. The subjects indicated their thermal feeling on questionnaires. The measured skin temperature was distributed close to the calculated mean skin temperature corresponding to the given activity level. The variation of Galvanic Skin Response (GSR) reflected the evaporative heat loss through the body surfaces and indicated whether sweating occurred on the subjects. Further investigations are needed to fully evaluate the influence of thermal and other factors on the output given by the investigated body sensor system.

A new algorithm for OFDM joint data detection and phase noise cancellation

This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the problem of "overfitting" such that the iterative approach may converge to a trivial solution. Although it is essential for this joint approach, the overfitting problem was relatively less studied in existing algorithms. In this paper, specifically, we apply a hard decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the phase noise, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.