Multi-label Classification via Adaptive Resonance Theory-based Clustering

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning

A novel dynamic maximum demand reduction controller of battery energy storage system for educational buildings in Malaysia

Maximum Demand (MD) management is essential to help businesses and electricity companies saves on electricity bills and operation cost. Among different MD reduction techniques, demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for various markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize complex prediction models and rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. Hence, this research work proposes a novel incremental DB-SOINN-R prediction model and a novel dynamic two-stage MD reduction controller. The incremental learning capability of the novel DB-SOINN-R allows the model to be deployed as soon as possible and improves its prediction accuracy as time progresses. The proposed DB-SOINN-R is compared with five models: feedforward neural network, deep neural network with long-short-term memory, support vector regression, ESOINN, and k-nearest neighbour (kNN) regression. They are tested on day-ahead and one-hour-ahead load predictions using two different datasets. The proposed DB-SOINN-R has the highest prediction accuracy among all models with incremental learning in both datasets. The novel dynamic two-stage maximum demand reduction controller of BESS incorporates one-hour-ahead load profiles to refine the threshold found based on day-ahead load profiles for preventing peak reduction failure, if necessary, with no rigid parameters required. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets. The real-world performance of the proposed two-stage MD reduction controller that includes the proposed DB-SOINN-R models is validated in a scaled-down experiment setup. Results show negligible differences of 0.5% in daily PDRP and MAPE between experimental and simulation results. Therefore, it fulfilled the aim of this research work, which is to develop a controller that is easy to implement, requires minimal historical data to begin operation and has a reliable MD reduction performance

A novel dynamic maximum demand reduction controller of battery energy storage system for educational buildings in Malaysia

Maximum Demand (MD) management is essential to help businesses and electricity companies saves on electricity bills and operation cost. Among different MD reduction techniques, demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for various markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize complex prediction models and rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. Hence, this research work proposes a novel incremental DB-SOINN-R prediction model and a novel dynamic two-stage MD reduction controller. The incremental learning capability of the novel DB-SOINN-R allows the model to be deployed as soon as possible and improves its prediction accuracy as time progresses. The proposed DB-SOINN-R is compared with five models: feedforward neural network, deep neural network with long-short-term memory, support vector regression, ESOINN, and k-nearest neighbour (kNN) regression. They are tested on day-ahead and one-hour-ahead load predictions using two different datasets. The proposed DB-SOINN-R has the highest prediction accuracy among all models with incremental learning in both datasets. The novel dynamic two-stage maximum demand reduction controller of BESS incorporates one-hour-ahead load profiles to refine the threshold found based on day-ahead load profiles for preventing peak reduction failure, if necessary, with no rigid parameters required. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets. The real-world performance of the proposed two-stage MD reduction controller that includes the proposed DB-SOINN-R models is validated in a scaled-down experiment setup. Results show negligible differences of 0.5% in daily PDRP and MAPE between experimental and simulation results. Therefore, it fulfilled the aim of this research work, which is to develop a controller that is easy to implement, requires minimal historical data to begin operation and has a reliable MD reduction performance

Class-incremental lifelong object learning for domestic robots

Traditionally, robots have been confined to settings where they operate in isolation and in highly controlled and structured environments to execute well-defined non-varying tasks. As a result, they usually operate without the need to perceive their surroundings or to adapt to changing stimuli. However, as robots start to move towards human-centred environments and share the physical space with people, there is an urgent need to endow them with the flexibility to learn and adapt given the changing nature of the stimuli they receive and the evolving requirements of their users. Standard machine learning is not suitable for these types of applications because it operates under the assumption that data samples are independent and identically distributed, and requires access to all the data in advance. If any of these assumptions is broken, the model fails catastrophically, i.e., either it does not learn or it forgets all that was previously learned. Therefore, different strategies are required to address this problem. The focus of this thesis is on lifelong object learning, whereby a model is able to learn from data that becomes available over time. In particular we address the problem of classincremental learning with an emphasis on algorithms that can enable interactive learning with a user. In class-incremental learning, models learn from sequential data batches where each batch can contain samples coming from ideally a single class. The emphasis on interactive learning capabilities poses additional requirements in terms of the speed with which model updates are performed as well as how the interaction is handled. The work presented in this thesis can be divided into two main lines of work. First, we propose two versions of a lifelong learning algorithm composed of a feature extractor based on pre-trained residual networks, an array of growing self-organising networks and a classifier. Self-organising networks are able to adapt their structure based on the input data distribution, and learn representative prototypes of the data. These prototypes can then be used to train a classifier. The proposed approaches are evaluated on various benchmarks under several conditions and the results show that they outperform competing approaches in each case. Second, we propose a robot architecture to address lifelong object learning through interactions with a human partner using natural language. The architecture consists of an object segmentation, tracking and preprocessing pipeline, a dialogue system, and a learning module based on the algorithm developed in the first part of the thesis. Finally, the thesis also includes an exploration into the contributions that different preprocessing operations have on performance when learning from both RGB and Depth images.James Watt Scholarshi