Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models

Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, their preferences recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.Comment: Submitted to a 2023 conference, 20 pages, 13 figure

A unified model for context-based behavioural modelling and classification

A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.South African National Research Foundation (NRF) and the the Advanced Sensors and Electronics Defence (ASED) Centre of KACST through the Council for Scientific and Industrial Research (CSIR).http://www.elsevier.com/locate/eswa2016-11-30hb201

Maritime piracy situation modelling with dynamic Bayesian networks

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.The Advanced Sensors and Electronics Defence (ASED) Centre of KACST through the Council for Scientific and Industrial Research (CSIR) and the South African National Research Foundation (NRF).http://www.elsevier.com/locate/inffushj201

Particle predictive control

This work explores the use of sequential and batch Monte Carlo techniques to solve the nonlinear model predictive control (NMPC) problem with stochastic system dynamics and noisy state observations. This is done by treating the state inference and control optimisation problems jointly as a single artificial inference problem on an augmented state-control space. The methodology is demonstrated on the benchmark car-up-the-hill problem as well as an advanced F-16 aircraft terrain following problem.http://www.elsevier.com/locate/jspiai201

Joint inference of dominant scatterer locations and motion parameters of an extended target in high range-resolution radar

A target of interest measured by a high range resolution radar may be modelled by multiple dominant points of reflections referred to as dominant scatterers. In this paper a non-linear state space setting is used to model the states and measurements of a target moving in the down- and cross-range dimensions. A resample-move particle filter with simulated annealing is successfully used to jointly infer the locations of the dominant scatterers and the motion parameters of the target. A novel technique for the initialization of the particle filter for the given application is presented. The location estimates of scatterers using the particle filter method are compared to those obtained using standard range-Doppler inverse synthetic aperture radar (ISAR) imaging when using the same radar returns for both cases. The particle filter infers the location of scatterers more accurately than range-Doppler ISAR processing, and the processing can be performed online as opposed to ISAR processing, which requires batching. It is relatively straightforward to extend the method to perform localisation and tracking of scatterers in three dimensions, whereas such an extension is challenging in range-Doppler ISAR processing. However, several challenges need be addressed to make this algorithm suitable for practical implementation and these challenges are discussed. This method may be used to obtain very accurate estimates of target state, which may in turn be used for accurate ISAR motion compensation. Given enough computing resources this algorithm may in future become the basis of a new radar target imaging scheme.King Abdulaziz City for Science and Technology (KACST) in the Kingdom of Saudi Arabia and the Council for Scientific and Industrial Research (CSIR) in South Africa.http://digital-library.theiet.org/content/journals/iet-rsnhb201

Consistent haul road condition monitoring by means of vehicle response normalisation with Gaussian processes

Suboptimal haul road management policies such as routine, periodic and urgent maintenance may result in unnecessary cost, both to roads and vehicles. A recent idea is to continually access haul road condition based on measured vehicle response. However the vehicle operating conditions, such as its instantaneous speed, may significantly influence its dynamic response resulting in possibly ambiguous road classifications. This paper proposes vehicle response calibration by means of Gaussian process regression, so that a severity metric which is more robust to fluctuating operating conditions may be obtained.http://www.elsevier.com/locate/engappaiai201

A method for real-time condition monitoring of haul roads based on Bayesian parameter estimation

Current haul road management techniques, such as routine, periodic and urgent maintenance have shortcomings in many complex haul road environments. Real-time road condition monitoring may significantly reduce maintenance costs, both to the road and to the vehicles. A recent idea is that vehicle on-board data collection systems could be used to monitor haul roads on a real-time basis by means of vibration signature analysis. This paper proposes a methodology based on Bayesian regression to isolate the effect of varying vehicle speed on the measured vehicle response metric. A key feature of the proposed methodology is that it avoids the costly need to generate analytical or empirical vehicle models.http://www.sciencedirect.com/science/journal/00224898ai201

Interval Algebra - an effective means of scheduling surveillance radar networks

Interval Algebra provides an effective means to schedule surveillance radar networks, as it is a temporal ordering constraint language. Thus it provides a solution to a part of resource management, which is included in the revised Data Fusion Information Group model of information fusion. In this paper, the use of Interval Algebra to schedule mechanically steered radars to make multistatic measurements for selected targets of importance is shown. Interval Algebra provides a framework for incorporating a richer set of requirements, without requiring modi cations to the underlying algorithms. The performance of Interval Algebra was compared to that of the Greedy Randomised Adaptive Search Procedure and the applicability of Interval Algebra to nimble scheduling was investigated using Monte-Carlo simulations of a binary radar system. The comparison was done in terms of actual performance as well as in terms of computation time required. The performance of the algorithms was quanti ed by keeping track of the number of targets that could be measured simultaneously. It was found that nimble scheduling is important where the targets are moving fast enough to rapidly change the recognised surveillance picture during a scan. Two novel approaches for implementing Interval Algebra for scheduling surveillance radars are presented. It was found that adding targets on the y and improving performance by incrementally growing the network is more e cient than pre-creating the full network. The second approach stemmed from constraint ordering. It was found that for simple constraint sets, the Interval Algebra relationship matrix reduces to a single vector of interval sets. The simulations revealed that an Interval Algebra algorithm that utilises both approaches can perform as well as the Greedy Randomised Adaptive Search Procedure with similar processing time requirements. Finally, it was found that nimble scheduling is not required for surveillance radar networks where ballistic and supersonic targets can be ignored. Nevertheless, Interval Algebra can easily be used to perform nimble scheduling with little modi - cation and may be useful in scheduling the scans of multifunction radars.Council for Scientific and Industrial Research, the University of Cape Town and the University of Pretoria.http://www.elsevier.com/locate/inffushb201

A method of parameterising a feed forward multi-layered perceptron artificial neural network, with reference to South African financial markets

No analytic procedures currently exist for determining optimal artificial neural network structures and parameters for any given application. Traditionally, when artificial neural networks have been applied to financial modelling problems, structure and parameter choices are often made a priori without sufficient consideration of the effect of such choices. A key aim of this study is to develop a general method that could be used to construct artificial neural networks by exploring the model structure and parameter space so that informed decisions could be made relating to the model design. In this study, a formal approach is followed to determine suitable structures and parameters for a Feed Forward Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with a single hidden layer. This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model. The performance of a range of more traditional time series models is compared with that of the artificial neural network models. The results suggest that, on a statistical level, artificial neural networks perform as well as time series models at forecasting the returns for financial markets. Hybrid models, combining artificial neural networks with the time series models, are constructed, trained and tested for the money market and for the rate of inflation. They appear to add value to the time series models when forecasting inflation, but not for the money market.http://www.actuarialsociety.org.za/Professionalresources/SAActuarialJournal.aspxam2017Electrical, Electronic and Computer EngineeringInsurance and Actuarial Scienc

Feedback-assisted automatic target and clutter discrimination using a Bayesian convolutional neural network for improved explainability in SAR applications

DATA AVAILABILITY STATEMENT : The NATO-SET 250 dataset is not publicly available; however, the MSTAR dataset can be found at the following url: https://www.sdms.afrl.af.mil/index.php?collection=mstar (accessed on 5 January 2022).In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.The Radar and Electronic Warfare department at the CSIR.http://www.mdpi.com/journal/remotesensinghj2023Electrical, Electronic and Computer Engineerin