Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues

Harnessing brain power at NUI Maynooth

The Department of Electronic Engineering at NUI Maynooth is involved in exciting interdisciplinary work in the biomedical, digital signal processing, control and electronic systems areas. Here Tomas Ward, Seán McLoone and Shirley Coyle highlight three specific projects

Utilising Mobile Phone RSSI Metric for Human Activity Detection

Recent research into urban analysis through the use of mobile device usage statistics has presented a need for the collection of this data independently from mobile network operators. In this paper we propose that cumulative received signal strength indications (RSSI) for overall mobile device transmissions in an area may provide such independent information. A process for the detection of high density areas within the RSSI temporal data set will be demonstrated. Finally, future applications for this collection method are discussed and we highlight its potential to complement traditional metric analysis techniques, for the representation of intensity of urban and local activities and their evolution through time and space

Exploiting A Priori Time Constant Ratio Information in Difference Equation Two-Thermocouple Sensor Characterization

The characterization of thermocouple sensors for temperature measurement in varying-flow enviroments is a challenging problem. Recently, the authors introduced novel different-equation-based algorithms that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. In particular, a linear least squares (LS) formulation of the characterization problem, which yields unbiased estimates when identified using generalized total LS, was introduced. These algorithms assume that time constants do not change during operation and are, therefore, appropriate for temperature measurement in homogenous constant-velocity liquid of gas flows. This paper develops an alternative B formulation of the characterization problem that has the major advantage of allowing exploitation of a priori knowledge of the ratio of the sensor time constants, thereby facilitating the implementation of computationally efficient algorithms that are less sensitive to measurement noise. A number of variants of the B formulation are developed, and appropriate unbiased estimators are identified. Monte Carlo simulation results are used to support the analysis

On Generalisation of Dual-Thermocouple Sensor Characterisation to RTDs

Intrusive temperature sensors such as thermocouples and resistance temperature detectors (RTDs) have become industry standards for simple and cost-effective temperature measurement. However, many situations require the use of physically robust and therefore low bandwidth temperature sensors. Much work has been published on dual-thermocouple thermometry as a means of obtaining increased sensor bandwidth from relatively robust thermocouples, which are assumed to have firstorder response. This contribution seeks to determine if RTDs, which are known to have approximately first-order response [1], can also be characterised using the dual-thermocouple approach. Experimental results show that the response of an RTD cannot be represented by a first-order model with sufficient accuracy to allow successful application of this method. Furthermore, simulation studies demonstrated that if a sensor exhibits even marginally second-order response, highly inaccurate temperature reconstructions follow. It is concluded that a higher-order model that more accurately reflects RTD response would be required for successful dual-RTD characterisation

Difference equation approach to two-thermocouple sensor characterization in constant velocity flow environments

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in certain harsh environments, for example, in combustion systems and engine exhausts, large wire diameters are required, and consequently the measurement bandwidth is reduced. This article discusses a software compensation technique to address the loss of high frequency fluctuations based on measurements from two thermocouples. In particular, a difference equation sDEd approach is proposed and compared with existing methods both in simulation and on experimental test rig data with constant flow velocity. It is found that the DE algorithm, combined with the use of generalized total least squares for parameter identification, provides better performance in terms of time constant estimation without any a priori assumption on the time constant ratios of the thermocouples

Utilising Mobile Phone Billing Records for Travel Mode Discovery

A novel methodology to infer transportation mode taken by mobile device users between regions of interest is introduced. It relies on analysing anonymised billing data, namely call detail records, supplied by mobile network operators as the primary source of user-created data. Coupled with the spatial coverage and distribution of mobile network cells and geographical route map information of major transportation modes, assumed to be partially non-overlapping, user travel paths can be predicted. Journey specific trajectories are constructed and analysed using the concept of virtual cell path for each qualified pre-processed list of activities from each unique user. After classification, kernel density paths for each route were generated both for illustration and validation purposes. Dierentiation between rail and road users travelling between Dublin and Cork in the Republic of Ireland is shown as an example application case stud

In Situ Two-Thermocouple Sensor Characterisation using Cross-Relation Blind Deconvolution with Signal Conditioning for Improved Robustness

Thermocouples are one of the most widely used temperature measurement devices due to their low cost, ease of manufacture and robustness. However, their robustness is obtained at the expense of limited sensor bandwidth. Consequently, in many applications signal compensation techniques are needed to recover the true temperature from the attenuated measurements. This, is turn, necessitates in situ thermocouple characterisation. Recently the authors proposed a novel characterisation technique based on the cross-relation method of blind deconvolution applied to the output of two thermocouples simultaneously measuring the same temperature. This offers a number of advantages over competing methods including low estimation variance and no need for a priori knowledge of the time constant ratio. A weakness of the proposed method is that it yields biased estimates in the presence of measurement noise. In this paper we propose the inclusion of a signal conditioning step in the characterisation algorithm to improve the robustness to noise. The enhanced performance of the resulting algorithm is demonstrated using both simulated and experimental data

Predicting organic acid concentration from UV/vis spectrometry measurements – A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data