Online pattern classification with multiple neural network systems: an experimental study

In this paper, an empirical study of the development and application of a committee of neural networks on online pattern classification tasks is presented. A multiple classifier framework is designed by adopting an Adaptive Resonance Theory-based (ART) autonomously learning neural network as the building block. A number of algorithms for combining outputs from multiple neural classifiers are considered, and two benchmark data sets have been used to evaluate the applicability of the proposed system. Different learning strategies coupling offline and online learning approaches, as well as different input pattern representation schemes, including the "ensemble" and "modular" methods, have been examined experimentally. Benefits and shortcomings of each approach are systematically analyzed and discussed. The results are comparable, and in some cases superior, with those from other classification algorithms. The experiments demonstrate the potentials of the proposed multiple neural network systems in offering an alternative to handle online pattern classification tasks in possibly nonstationary environments.

Uplink power control via adaptive Hidden-Markov-Model-Based pathloss estimation

Dynamic variations in channel behavior is considered in transmission power control design for cellular radio systems. It is well known that power control increases system capacity, improves Quality of Service (QoS), and reduces multiuser interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss dynamics of the fading channel is presented. Formulating power control decisions based on the measured received power levels allows modeling the fading channel pathloss dynamics in terms of a Hidden Markov Model (HMM). Applying the online HMM identification algorithm enables accurate estimation of the real pathloss ensuring efficient performance of the suggested power control scheme.

Paradigm shift to enhanced water supply planning through augmented grids, scarcity pricing and adaptive factory water : a system dynamics approach

This paper details a system dynamics model developed to simulate proposed changes to water governance through the integration of supply, demand and asset management processes. To effectively accomplish this, interconnected feedback loops in tariff structures, demand levels and financing capacity are included in the model design, representing the first comprehensive life-cycle modelling of potable water systems. A number of scenarios were applied to Australia's populated South-east Queensland region, demonstrating that introducing temporary drought pricing (i.e. progressive water prices set inverse with availability), in conjunction with supply augmentation through rain-independent sources, is capable of efficiently providing water security in the future. Modelling demonstrated that this alternative tariff structure reduced demand in scarcity periods thereby preserving supply, whilst revenues are maintained to build new water supply infrastructure. In addition to exploring alternative tariffs, the potential benefits of using adaptive pressure-retarded osmosis desalination plants for both potable water and power generation was explored. This operation of these plants for power production, when they would otherwise be idle, shows promise in reducing their net energy and carbon footprints. Stakeholders in industry, government and academia were engaged in model development and validation. The constructed model displays how water resource systems can be reorganised to cope with systemic change and uncertainty.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

A sliding-mode-control-based adaptive cruise controller

This paper presents an alternative solution to the conventional cruise controller of a hybrid electric vehicle based on the sliding mode control approach. The mathematical model of a hybrid electric vehicle cruise control system is developed. Then, the sliding mode control approach is applied as the controller. The sliding mode control stability is investigated and demonstrated. Thereafter, the system is simulated and the results are presented. © 2014 IEEE.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

Adaptive translational cueing motion algorithm using fuzzy based tilt coordination

Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simulator’s physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.

Adaptive washout algorithm based fuzzy tuning for improving human perception

The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the simulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adaptive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of highpass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cueing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance, improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Adaptive robust control of 6-DOF autonomous aerial vehicles

The thesis focused on development of an auto-pilot system for UAV’s and small fixed wing aircraft for use in hazardous flight conditions, such as severe weather. This led to development of a mathematical algorithm that unbinds the flight systems from coupling effects which can adaptively changed to the environment.

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

A nonlinear adaptive backstepping approach for coordinated excitation and steam-valving control of synchronous generators

Steam-valving and excitation systems play an important role to maintain the transient stability of power systems with synchronous generators when power systems are subjected to large disturbances and sudden load changes. This paper presents a nonlinear adaptive backstepping approach for controlling excitation and steam-valving systems of synchronous generators. In this paper, the proposed excitation and steam-valving controllers are designed in a coordinated manner so that they can work under several and most severe operating conditions. Both excitation and steam-valving controllers are designed by considering some critical parameters as unknown. The effectiveness of the proposed coordinated control scheme is evaluated on a single machine infinite bus system under different operating conditions such as load changes and three-phase short circuit faults at the generator terminal. Finally, performance of the proposed scheme is compared to that of a similar nonlinear adaptive backstepping excitation controller without any coordination and simulation results demonstrate the superiority of the proposed one.

Robust adaptive backstepping excitation controller design for simple power system models with external disturbances

This paper presents a nonlinear robust adaptive excitation controller design for a simple power system model where a synchronous generator is connected to an infinite bus. The proposed controller is designed to obtain the adaption laws for estimating critical parameters of synchronous generators which are considered as unknown while providing the robustness against the bounded external disturbances. The convergence of different physical quantities of a single machine infinite bus (SMIB) system, with the proposed control scheme, is ensured through the negative definiteness of the derivative of Lyapunov functions. The effects of external disturbances are considered during formulation of Lyapunov function and thus, the proposed excitation controller can ensure the stability of the SMIB system under the variation of critical parameters as well as external disturbances including noises. Finally, the performance of the proposed scheme is investigated with the inclusion of external disturbances in the SMIB system and its superiority is demonstrated through the comparison with an existing robust adaptive excitation controller. Simulation results show that the proposed scheme provides faster responses of physical quantities than the existing controller.

Design of distributed adaptive neural traffic signal timing controller by cuckoo search optimization

This paper focuses on designing an adaptive controller for controlling traffic signal timing. Urban traffic is an inevitable part in modern cities and traffic signal controllers are effective tools to control it. In this regard, this paper proposes a distributed neural network (NN) controller for traffic signal timing. This controller applies cuckoo search (CS) optimization methods to find the optimal parameters in design of an adaptive traffic signal timing control system. The evaluation of the performance of the designed controller is done in a multi-intersection traffic network. The developed controller shows a promising improvement in reducing travel delay time compared to traditional fixed-time control systems.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.