FuzzyART: An R Package for ART-based Clustering

Adaptive Resonance Theory (ART) was introduced by Steven Grossberg as a theory of human cognitive information processing (Grossberg 1976, 1980). Extending the capabilities of the ART 1 model, which can learn to categorize patterns in binary data, fuzzy ART as described in (Carpenter, Grossberg, and Rosen 1991) has become one of the most commenly used Adaptive Resonance Theory models (Brito da Silva, Elnabarawy, and Wunsch 2019). By incorporating fuzzy set theroy operators, fuzzy ART is capable of learning from binaray and bounded real valued data. Its advantage over other unsupervised learning algorithms lies in the flexibility of the learning rule. If a given input feature does not resemble a known category satisfactorily, as determined by the vigilance test, a new category is initialized. Hence, the total number of categories (or clusters) is not determined a-priori, like k-means, but chosen in accordance with the data and the context of already learnt representations. This vignette explores the use of the fuzzy ART implementation as provided by the FuzzyART R package

Joint Control of Manufacturing and Onsite Microgrid System Via Novel Neural-Network Integrated Reinforcement Learning Algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method

Testing The Limits: A Robustness Analysis Of Logistic Growth Models For Life Cycle Estimation During The COVID-19 Pandemic

The semiconductor industry operates in a dynamic environment characterized by rapid technological advancements, extensive research and development investments, long planning horizons, and cyclical market behavior. Consequently, staying vigilant to technological disruptions and shifting trends is crucial. This is especially challenging when external shocks seriously affect supply chain processes and demand patterns. Particularly, recent events, such as the COVID-19 pandemic, the ongoing Russian invasion of Ukraine, and high consumer price inflation impacting the semiconductor cycle emphasize the need to account for these influences. In this context, we analyze growth patterns and life cycles of various technologies within the semiconductor industry by estimating logistic growth models. The logistic growth model was originally formulated to describe population dynamics. However, many processes outside the discipline of ecology share the fundamental characteristics of natural growth: self-proportionality and a self-regulating mechanism. Out of the different applications, two are of particular interest in the context of strategic business decisions: (1) modeling innovation diffusion and technological change to predict the mid- to long-term growth of a market, and (2) modeling of product life cycles. To obtain market growth and life cycle predictions, we apply the logistic growth model to forecast cumulative revenues by technology over time. This model treats the analyzed technology as a closed system. However, in practice, external shocks are the norm. To analyze the robustness to such external shocks, we compare technology life cycle estimates derived from logistic growth models before and after the effects of COVID-19 became evident for a wide array of semiconductor technologies. We find that the impact of COVID-19 on these life cycle estimates is mixed, but the median change is low. Our findings have implications for the application of logistic growth models in strategic decision-making, helping stakeholders navigate the complexities of technological innovation, diffusion, and market growth

Less is more: Beating the market with recurrent reinforcement learning

Multiple recurrent reinforcement learners were implemented to make trading decisions based on real and freely available macro-economic data. The learning algorithm and different reinforcement functions (the Differential Sharpe Ratio, Differential Downside Deviation Ratio and Returns) were revised and the performances were compared while transaction costs were taken into account. (This is important for practical implementations even though many publications ignore this consideration.) It was assumed that the traders make long-short decisions in the S&P500 with complementary 3-month treasury bill investments. Leveraged positions in the S&P500 were disallowed. Notably, the Differential Sharpe Ratio and the Differential Downside Deviation Ratio are risk adjusted and are therefore expected to yield more stable and less risky strategies. Interestingly, the Return-traders performed the most consistently. Explanations for these findings were explored. The strong performance of the return-based traders - even based on few and readily available macro-economic time series - showed the power and practical relevance of the simpler algorithm --Abstract, page iv

Joint Control of Manufacturing and Onsite Microgrid System via Novel Neural-Network Integrated Reinforcement Learning Algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method

Handling missing data for unsupervised learning with an application on a FITBIR Traumatic Brain Injury (TBI) Dataset

The problem of missing data and imputation have been widely discussed amongst specialists. However, many data scientists and applied statisticians fail to appropriately consider this issue. Often, it seems intuitive to discard observations containing missing data or simply to substitute means. This can lead to disastrous consequences, particularly in an era of exponentially increasing data volumes. In the following, we show how inappropriate handling of missing data and an insufficient analysis of the censoring mechanism can lead to a bias, overconfidence in the estimation of parameters, could challenge the reproducibility of obtained results, and may distort the structure of the dataset. --Background

An Explainable and Statistically Validated Ensemble Clustering Model Applied to the Identification of Traumatic Brain Injury Subgroups

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of analysis to enhance the quality and the explainability of results. This methodology is applicable to other clinical data sets that exhibit significant heterogeneity as well as other diverse data science applications in biomedicine and elsewhere

Heterogeneity in Blood Biomarker Trajectories after Mild TBI Revealed by Unsupervised Learning

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI