Retention Prediction and Policy Optimization for United States Air Force Personnel Management

Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets over time as they depart. To improve retention predictions, we develop and test traditional random forest models and feedforward neural networks as well as partially autoregressive forms of both, outperforming the benchmark on a test dataset by 62.8% and 34.8% for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted personnel, then extend this formulation to include the Air Force Reserve and Air National Guard. We develop and test an adaptation of the Concave Adaptive Value Estimation (CAVE) algorithm and a parameterized Deep Q-Network on the active duty problem instance with 7050 dimensions, finding that CAVE reduces costs from the benchmark policy by 29.76% and 17.38% for the two cost functions tested. We test CAVE across a range of hyperparameters for the larger intercomponent problem instance with 21,240 dimensions, reducing costs by 23.06% from the benchmark, then develop the Stochastic Use of Perturbations to Enhance Robustness of CAVE (SUPERCAVE) algorithm, reducing costs by another 0.67%. Resulting algorithms and methods are directly applicable to contemporary USAF personnel business practices and enable more accurate, less time-intensive, cogent, and data-informed policy targets for current processes

Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.Comment: Under review for IEEE Robotics and Automation Letter

Machine learned Force-Fields for an ab-initio Quality Description of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a reliable description of many of their properties accurate computationally highly efficient methods, like force-field potentials (FFPs), are required. With the advent of machine learning approaches, it is now possible to generate such potentials with relatively little human effort. Here, we present a recipe to parametrize two fundamentally different types of exceptionally accurate and computationally highly efficient machine learned potentials, which belong to the moment-tensor and kernel-based potential families. They are parametrized relying on reference configurations generated in the course of molecular dynamics based, active learning runs and their performance is benchmarked for a representative selection of commonly studied MOFs. For both potentials, comparison to a random set of validation structures reveals close to DFT precision in predicted forces and structural parameters of all MOFs. Essentially the same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a high degree of computational efficiency, with the obtained machine learned potentials being only moderately slower than the extremely simple UFF4MOF or Dreiding force fields. The exceptional accuracy of the presented FFPs combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments-The Wastewater Treatment Plant Control Case

Altres ajuts: Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu (2020 FI_B2 000)The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements-when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively

Automated anomaly recognition in real time data streams for oil and gas industry.

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and other engineering industries. To reduce the reliance on field experts' knowledge for identification of these anomalies, this thesis proposes a deep-learning anomaly-detection framework that can help to create an effective real-time condition-monitoring framework. The aim of this research is to develop a real-time and re-trainable generic anomaly-detection framework, which is capable of predicting and identifying anomalies with a high level of accuracy - even when a specific anomalous event has no precedent. Machine-based condition monitoring is preferable in many practical situations where fast data analysis is required, and where there are harsh climates or otherwise life-threatening environments. For example, automated conditional monitoring systems are ideal in deep sea exploration studies, offshore installations and space exploration. This thesis firstly reviews studies about anomaly detection using machine learning. It then adopts the best practices from those studies in order to propose a multi-tiered framework for anomaly detection with heterogeneous input sources, which can deal with unseen anomalies in a real-time dynamic problem environment. The thesis then applies the developed generic multi-tiered framework to two fields of engineering: data analysis and malicious cyber attack detection. Finally, the framework is further refined based on the outcomes of those case studies and is used to develop a secure cross-platform API, capable of re-training and data classification on a real-time data feed

A comparison framework for distribution system outage and fault location methods

Finding the location of faults in distribution networks has been a long standing problem for utility operators, and an interesting subject for researchers as well. In recent years, significant research efforts have been devoted to the development of methods for identification of the faulted area to assist utility operators in expediting service restoration, and consequently reducing outage time and relevant costs. Considering today's wide variety of distribution systems, a solution preferred for a specific system might be impractical for another one. This paper provides a comparison framework which classifies and reviews a relatively large number of different fault location and outage area location methods to serve as a guide to power system engineers and researchers to choose the best option based on their existing system and requirements. It also supports investigations on the challenging and unsolved problems to realize the fields of future studies and improvements. For each class of methods, a short description of the main idea and methodology is presented. Then, all the methods are discussed in detail presenting the key points, advantages, limitations, and requirements

Machine learning supported forecasting of baseline energy consumption for industrial processes

Abstract. The purpose of the thesis was to study and evaluate machine learning supported methods in order to forecast a baseline energy consumption from time-series data of energy-intensive industry. In addition, time-series anomaly detection methods were studied and the anomaly detection accuracy of them was evaluated with hourly and daily average energy consumption data. In the experimental part of the thesis a simulation scenario was established for hourly average data of two factories. The energy baseline was identified dynamically with week-ahead time-series forecasting by utilizing previous 52 weeks of data in the model training. In addition, model adaptation was considered in the simulation scenario. Predictor variables of the models were designed to imitate natural calendar effect. The energy baseline data of factory A was used to evaluate five linear and non-linear model structures. An average ensemble model structure appeared to outperform other model structures resulting in mean absolute percentage error of 9.3% for validation data of Factory A. The generalization ability of the model structure was evaluated with the data of factory B. For factory B the average ensemble model resulted in mean absolute percentage error of 9.9% for validation data. Overall, the results seemed promising especially as the set of input variables remained relatively simple as more precise subject matter expertise was not available during variable design and selection phase.Koneoppimiseen perustuva ominaisenergian kulutuksen ennustaminen teollisissa prosesseissa . Tiivistelmä. Diplomityön tavoitteena oli tutkia ja evaluoida koneoppimiseen pohjautuvia menetelmiä energiaintensiivisen teollisuuden aikasarjamuotoisen energiankulutusdatan käsittelyssä energiankulutuksen perusuran ennustamiseksi. Lisäksi työssä tutkittiin aikasarjadatan anomaliantunnistusmenetelmiä ja evaluoitiin niiden kykyä tunnistaa poikkeamia tuntija päiväkeskiarvoresoluutioisessa energiankulutusdatassa. Työn kokeellisessa osiossa muodostettiin simulaatioskenaario kahden eri tehtaan vuosien 2020 sekä 2021 tuntikeskiarvoisten energiankulutusaineistojen mallinnukselle. Perusura muodostettiin dynaamisesti kerrallaan viikoksi eteenpäin aikasarjaennusteena edellisen 52 viikon aineistoa mallin opetuksessa hyödyntäen. Mallinnusskenaariossa huomioitiin lisäksi mallin suorituskyvylle olennainen adaptaatioproseduuri. Mallien selittävinä muuttujina käytettiin eksploratiivisen data-analyysin pohjalta luotuja luonnollista kalenterivaikutusta imitoivia muuttujia. Tehtaan A aineistolla evaluoitiin viittä eri lineaarista ja epälineaarista mallirakennetta. Parhaimmaksi mallirakenteeksi osoittautui keskiarvoyhdistelmämalli, jolle ennusteen keskimääräinen suhteellinen virhe oli 9,3 % validointiaineistolla. Mallirakenteen yleistyvyyttä testattiin toisen tehtaan (B) vastaavan ajanjakson aineistolla. Tehtaan B aineistolle keskiarvoyhdistelmämallin ennusteen keskimääräinen suhteellinen virhe oli 9,9 % validointiaineistolla. Tuloksia voidaan yleisesti ottaen pitää lupaavina etenkin, kun mallien tulomuuttujajoukko jäi verrattain yksinkertaiseksi, sillä tarkempaa aiheasiantuntemusta ei ollut saatavilla

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic