Search results for: predictive insights

Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Ella Tyuryumina, Alexey Neznanov

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This paper is devoted to mathematical modelling of the progression and stages of breast cancer. We propose Consolidated mathematical growth model of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases (CoM-III) as a new research tool. We are interested in: 1) modelling the whole natural history of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases; 2) developing adequate and precise CoM-III which reflects relations between primary tumor and secondary distant metastases; 3) analyzing the CoM-III scope of application; 4) implementing the model as a software tool. Firstly, the CoM-III includes exponential tumor growth model as a system of determinate nonlinear and linear equations. Secondly, mathematical model corresponds to TNM classification. It allows to calculate different growth periods of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases: 1) ‘non-visible period’ for primary tumor; 2) ‘non-visible period’ for secondary distant metastases growth in patients with lymph nodes metastases; 3) ‘visible period’ for secondary distant metastases growth in patients with lymph nodes metastases. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. Thus, the CoM-III model and predictive software: a) detect different growth periods of primary tumor and secondary distant metastases growth in patients with lymph nodes metastases; b) make forecast of the period of the distant metastases appearance in patients with lymph nodes metastases; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoM-III: the number of doublings for ‘non-visible’ and ‘visible’ growth period of secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of secondary distant metastases. The CoM-III enables, for the first time, to predict the whole natural history of primary tumor and secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on primary tumor sizes. Summarizing: a) CoM-III describes correctly primary tumor and secondary distant metastases growth of IA, IIA, IIB, IIIB (T1-4N1-3M0) stages in patients with lymph nodes metastases (N1-3); b) facilitates the understanding of the appearance period and inception of secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, primary tumor, secondary metastases, survival

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Authors: Gaurav Bisht, Rahul Rathnakumar, Ravikumar Duggirala

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Predictive simulations are one of the key focus areas in safety-critical industries such as aerospace and high-performance automotive engineering. The stone-chipping study is one such effort taken up by the industry to predict and evaluate the damage caused due to gravel impact on vehicles. This paper describes a finite elements based method that can simulate the ejection of gravel chips from a vehicle tire. The FE simulations were used to obtain the initial ejection velocity of the stones for various driving conditions using a computational contact mechanics approach. To verify the accuracy of the tire model, several parametric studies were conducted. The FE simulations resulted in stone loft velocities ranging from 0–8 m/s, regardless of tire speed. The stress on the tire at the instant of initial contact with the stone increased linearly with vehicle speed. Mesh convergence studies indicated that a highly resolved tire mesh tends to result in better momentum transfer between the tire and the stone. A fine tire mesh also showed a linearly increasing relationship between the tire forward speed and stone lofting speed, which was not observed in coarser meshes. However, it also highlighted a potential challenge, in that the ejection velocity vector of the stone seemed to be sensitive to the mesh, owing to the FE-based contact mechanical formulation of the problem.

Keywords: abaqus, contact mechanics, foreign object debris, stone chipping

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Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Zhiyuan Du, Baisravan Hom Chaudhuri, Pierluigi Pisu

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In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.

Keywords: connected vehicles, automated vehicles, intersection coordination systems, multiple interconnected intersections, model predictive control

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Numtana Ladplee, Cherif Haberih

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The value of service evaluation depends critically on guests’ understanding of the evaluation objectives and their roles. The present research presents a three-phase investigation of the impact of evaluating participants’ theories about their roles: (a) identifying the theories, (b) testing the process consequences of participants’ role theories, and (c) gaining insights into the impact of participants’ role theories by testing key moderator/s. The findings of this study will hopefully indicate that (a) when forewarned of an upcoming evaluation task, consumers tend to believe that the evaluation objective is to identify aspects that need improvement, (b) this expectation produces a conscious attempt to identify negative aspects, although the encoding of attribute information is not affected, and (c) cognitive load during the evaluation experience greatly decreases the negativity of expected evaluations. The present study can be applied to other market research techniques and thereby improve our understanding of consumer inputs derived from market research. Such insights can help diminish biases produced by participants’ correct or incorrect theories regarding their roles.

Keywords: fulfillment, hotel guests, service, Thailand

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Jack Nash, Chris Simpson, Holly Hurn, Ronel Terblanche, Alan Mistlin

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There is an increasing incidence of mild traumatic brain injury (mTBI) cases throughout sport and with this, a growing interest from governing bodies to ensure these are managed appropriately and player welfare is prioritised. The Berlin consensus statement on concussion in sport recommends a multidisciplinary approach when managing those patients who do not have full resolution of mTBI symptoms. There are as of yet no standardised guideline to follow in the treatment of complex cases mTBI in athletes. The aim of this project was to analyse the outcomes, both clinical and vocational, of all patients admitted to the mild Traumatic Brain Injury (mTBI) service at the UK’s Defence Military Rehabilitation Centre Headley Court between 1st June 2008 and 1st February 2017, as a result of a sport induced injury, and evaluate potential predictive indicators of outcome. Patients were identified from a database maintained by the mTBI service. Clinical and occupational outcomes were ascertained from medical and occupational employment records, recorded prospectively, at time of discharge from the mTBI service. Outcomes were graded based on the vocational independence scale (VIS) and clinical documentation at discharge. Predictive indicators including referral time, age at time of injury, previous mental health diagnosis and a financial claim in place at time of entry to service were assessed using logistic regression. 45 Patients were treated for sport-related mTBI during this time frame. Clinically 96% of patients had full resolution of their mTBI symptoms after input from the mTBI service. 51% of patients returned to work at their previous vocational level, 4% had ongoing mTBI symptoms, 22% had ongoing physical rehabilitation needs, 11% required mental health input and 11% required further vestibular rehabilitation. Neither age, time to referral, pre-existing mental health condition nor compensation seeking had a significant impact on either vocational or clinical outcome in this population. The vast majority of patients reviewed in the mTBI clinic had persistent symptoms which could not be managed in primary care. A consultant-led, multidisciplinary approach to the diagnosis and management of mTBI has resulted in excellent clinical outcomes in these complex cases. High levels of symptom resolution suggest that this referral and treatment pathway is successful and is a model which could be replicated in other organisations with consultant led input. Further understanding of both predictive and individual factors would allow clinicians to focus treatments on those who are most likely to develop long-term complications following mTBI. A consultant-led, multidisciplinary service ensures a large number of patients will have complete resolution of mTBI symptoms after sport-related mTBI. Further research is now required to ascertain the key predictive indicators of outcome following sport-related mTBI.

Keywords: brain injury, concussion, neurology, rehabilitation, sports injury

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Authors: Olaf Visker, Arnout De Vries, Lambert Schomaker

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Predictive policing refers to the usage of analytical techniques to identify potential criminal activity. It has been widely implemented by various police departments. Being a relatively new area of research, there are, to the author’s knowledge, no absolute tried, and true methods and they still exhibit a variety of potential problems. One of those problems is closely related to the lack of understanding of how acting on these prediction influence crime itself. The goal of law enforcement is ultimately crime reduction. As such, a policy needs to be established that best facilitates this goal. This research aims to find such a policy by using adversarial agent-based modeling in combination with modern reinforcement learning techniques. It is presented here that a baseline model for both law enforcement and criminal agents and compare their performance to their respective reinforcement models. The experiments show that our smart law enforcement model is capable of reducing crime by making more deliberate choices regarding the locations of potential criminal activity. Furthermore, it is shown that the smart criminal model presents behavior consistent with popular crime theories and outperforms the baseline model in terms of crimes committed and time to capture. It does, however, still suffer from the difficulties of capturing long term rewards and learning how to handle multiple opposing goals.

Keywords: adversarial, agent based modelling, predictive policing, reinforcement learning

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: S. B. R. Slagmulder

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Monitoring inspiratory effort and dynamic lung stress in patients on pressure support ventilation in the ICU is important for protecting against self inflicted lung injury (P-SILI) and diaphragm dysfunction. Strategies to address the detrimental effects of respiratory drive and effort can lead to improved patient outcomes. Two non-invasive estimation methods, occlusion pressure (Pocc) and P0.1, have been proposed for achieving lung and diaphragm protective ventilation. However, their relationship and interpretation in neuro ICU patients is not well understood. P0.1 is the airway pressure measured during a 100-millisecond occlusion of the inspiratory port. It reflects the neural drive from the respiratory centers to the diaphragm and respiratory muscles, indicating the patient's respiratory drive during the initiation of each breath. Occlusion pressure, measured during a brief inspiratory pause against a closed airway, provides information about the inspiratory muscles' strength and the system's total resistance and compliance. Research Objective: Understanding the relationship between Pocc and P0.1 in brain-injured patients can provide insights into the interpretation of these values in pressure support ventilation. This knowledge can contribute to determining extubation readiness and optimizing ventilation strategies to improve patient outcomes. The central goal is to asses a study protocol for determining the relationship between Pocc and P0.1 in brain-injured patients on pressure support ventilation and their ability to predict successful extubation. Additionally, comparing these values between brain-damaged and non-brain-damaged patients may provide valuable insights. Key Areas of Inquiry: 1. How do Pocc and P0.1 values correlate within brain injury patients undergoing pressure support ventilation? 2. To what extent can Pocc and P0.1 values serve as predictive indicators for successful extubation in patients with brain injuries? 3. What differentiates the Pocc and P0.1 values between patients with brain injuries and those without? Methodology: P0.1 and occlusion pressures are standard measurements for pressure support ventilation patients, taken by attending doctors as per protocol. We utilize electronic patient records for existing data. Unpaired T-test will be conducted to compare P0.1 and Pocc values between both study groups. Associations between P0.1 and Pocc and other study variables, such as extubation, will be explored with simple regression and correlation analysis. Depending on how the data evolve, subgroup analysis will be performed for patients with and without extubation failure. Results: While it is anticipated that neuro patients may exhibit high respiratory drive, the linkage between such elevation, quantified by P0.1, and successful extubation remains unknown The analysis will focus on determining the ability of these values to predict successful extubation and their potential impact on ventilation strategies. Conclusion: Further research is pending to fully understand the potential of these indices and their impact on mechanical ventilation in different patient populations and clinical scenarios. Understanding these relationships can aid in determining extubation readiness and tailoring ventilation strategies to improve patient outcomes in this specific patient population. Additionally, it is vital to account for the influence of sedatives, neurological scores, and BMI on respiratory drive and occlusion pressures to ensure a comprehensive analysis.

Keywords: brain damage, diaphragm dysfunction, occlusion pressure, p0.1, respiratory drive

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Authors: Nastaran Hemmati, Farhad Nikkhahi, Amir Javadi, Sahar Eskandarion, Seyed Mahmuod Amin Marashi

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Neisseria meningitidis, Escherichia coli K, Streptococcus agalactiae, and Streptococcus pneumoniae cause 90% of bacterial meningitis. Almost all infected people die or have irreversible neurological complications. Therefore, it is essential to have a diagnostic kit with the ability to quickly detect these fatal infections. The project involved 212 patients from whom cerebrospinal fluid samples were obtained. After total genome extraction and performing multiplex quantitative polymerase chain reaction (qPCR), the presence or absence of each infectious factor was determined by comparing with standard strains. The specificity, sensitivity, positive predictive value, and negative predictive value calculated were 100%, 92.9%, 50%, and 100%, respectively. So, due to the high specificity and sensitivity of the designed primers, they can be used instead of bacterial culture that takes at least 24 to 48 hours. The remarkable benefit of this method is associated with the speed (up to 3 hours) at which the procedure could be completed. It is also worth noting that this method can reduce the personnel unintentional errors which may occur in the laboratory. On the other hand, as this method simultaneously identifies four common factors that cause bacterial meningitis, it could be used as an auxiliary method diagnostic technique in laboratories particularly in cases of emergency medicine.

Keywords: cerebrospinal fluid, meningitis, quantitative polymerase chain reaction, simultaneous detection, diagnosis testing

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Authors: Pranav Gunhal., Krish Jhurani

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This study tackles the significant computational challenge of predicting excited state dynamics in organic photovoltaic (OPV) materials—a pivotal factor in the performance of solar energy solutions. Time-dependent density functional theory (TDDFT), though effective, is computationally prohibitive for larger and more complex molecules. As a solution, the research explores the application of transformer neural networks, a type of artificial intelligence (AI) model known for its superior performance in natural language processing, to predict excited state dynamics in OPV materials. The methodology involves a two-fold process. First, the transformer model is trained on an extensive dataset comprising over 10,000 TDDFT calculations of excited state dynamics from a diverse set of OPV materials. Each training example includes a molecular structure and the corresponding TDDFT-calculated excited state lifetimes and key electronic transitions. Second, the trained model is tested on a separate set of molecules, and its predictions are rigorously compared to independent TDDFT calculations. The results indicate a remarkable degree of predictive accuracy. Specifically, for a test set of 1,000 OPV materials, the transformer model predicted excited state lifetimes with a mean absolute error of 0.15 picoseconds, a negligible deviation from TDDFT-calculated values. The model also correctly identified key electronic transitions contributing to the excited state dynamics in 92% of the test cases, signifying a substantial concordance with the results obtained via conventional quantum chemistry calculations. The practical integration of the transformer model with existing quantum chemistry software was also realized, demonstrating its potential as a powerful tool in the arsenal of materials scientists and chemists. The implementation of this AI model is estimated to reduce the computational cost of predicting excited state dynamics by two orders of magnitude compared to conventional TDDFT calculations. The successful utilization of transformer neural networks to accurately predict excited state dynamics provides an efficient computational pathway for the accelerated discovery and design of new OPV materials, potentially catalyzing advancements in the realm of sustainable energy solutions.

Keywords: transformer neural networks, organic photovoltaic materials, excited state dynamics, time-dependent density functional theory, predictive modeling

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Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

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The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

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Authors: Raul G. Angeles, Rowena R. De Guzman

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The future workforce for health in a globalized context highlights better health human resource planning. Health sciences students are challenged to develop skills needed for global migration. Advancing health sciences education is crucial in preparing them to overcome border challenges. The purpose of this mixed-method, two-part study was to determine the extent by which the current instructional planning and implementation (IPI) framework is reframed with teaching approaches that foster students' 21st-century skills development and to examine participants’ over-all insights on learner-centered teaching and learning (LCTL) particularly in health sciences classrooms. Participants were groups of teachers and students drawn from a national sample through the Philippine higher education institutions (HEIs). To the participants, the use of technology, practices driven by students’ interests and enriching learning experiences through project-based learning are the approaches that must be incorporated with great extent in IPI to encourage student engagement, active learning and collaboration. Participants were asked to detail their insights of learner-centered teaching and learning and using thematic content analysis parallel insights between the groups of participants lead to three emerging themes: opportunities, challenges and prospects. More contemporary understanding of LTCL in today’s health sciences classrooms were demonstrated by the participants. Armed with true understanding, educational leaders can provide interventions appropriate to the students’ level of need, teachers’ preparation and school’s readiness in terms of resources. Health sciences classrooms are innovated to meet the needs of the current and future students.

Keywords: globalization, health workforce, role of education, student-centered teaching and learning, technology in education

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Fahad Raj Khan, Suleman Khan

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There is a lack of consensus about the predictive value of raised blood glucose levels in terms of major adverse cardiac events (MACEs) in non-diabetic patients admitted for acute decompensated heart failure. The purpose of this research was to examine the long-term prognosis of acute decompensated heart failure (ADHF) in non-diabetic persons who had increased blood glucose levels, i.e., stress hyperglycemia, at the time of their ADHF hospitalization. The research involved 650 non-diabetic patients. Based on their admission stress hyperglycemia, they were divided into two groups.ie with and without (SHGL). The two groups' one-year outcomes for major adverse cardiac events (MACEs) were compared, and key predictors of MACEs were discovered. For statistical analysis, the two-tailed Mann-Whitney U test, Fisher's exact test, and binary logistic regression analysis were utilized. SHGL was found in 353 (54.3%) individuals. It was more frequent in men than in women. About 27% of patients with SHGL had previously been admitted for ADHF. Almost 62% were hypertensive, whereas 14 % had CKD. MACEs were significantly predicted by SHGL, HTN, prior hospitalization for ADHF, CKD, and cardiogenic shock upon admission. SHGL at the time of ADHF admission, independent of DM status, may be a predictive indication of MACEs.

Keywords: stress hyperglycemia, acute heart failure, major adverse cardiac events, MACEs

Procedia PDF Downloads 74

Authors: Scott Ashby, Emily Granger, Mark Connellan

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Background: In-hospital management of Out-of-Hospital Cardiac Arrest (OHCA) is complex as the aetiologies are varied. Acute coronary angiography has been shown to improve outcomes for patients with coronary occlusion as the cause; however, these patients are difficult to identify. ECG results may help identify these patients, but the accuracy of this diagnostic test is under debate, and requires further investigation. Methods: Arrest and hospital management information was collated retrospectively for OHCA patients who presented to a single clinical site between 2009 and 2013. Angiography results were then collected and checked for significance with survival to discharge. The presence of a severe lesion (>70%) was then compared to categorised ECG findings, and the accuracy of the test was calculated. Results: 104 patients were included in this study, 44 survived to discharge, 52 died and 8 were transferred to other clinical sites. Angiography appears to significantly correlate with survival to discharge. ECG showed 54.8% sensitivity for detecting the presence of a severe lesion within the group that received angiography. A combined criterion including any ECG pathology showed 100% sensitivity and negative predictive value, however, a low specificity and positive predictive value. Conclusion: In the cohort investigated, ST elevation on ECG is not a sensitive enough screening test to be used to determine whether OHCA patients have coronary stenosis as the likely cause of their arrest, and more investigation into whether screening with a combined ECG criterion, or whether all patients should receive angiography routinely following OHCA is needed.

Keywords: out of hospital cardiac arrest, coronary angiography, resuscitation, emergency medicine

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Authors: Yuri Kitahara

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Background: Cognitive reappraisal refers to an emotion regulation strategy in which one changes the interpretation of emotion-eliciting events. Numerous studies show that cognitive reappraisal is associated with mental health and better social functioning. However the examination of the predictive factors of adaptive emotion regulation remains as an issue. The present study examined the factors contributing to the habitual use of cognitive reappraisal, with a focus on emotional awareness and working memory. Methods: Data was collected from 30 junior high school students, using a Japanese version of the Emotion Regulation Questionnaire (ERQ), the Levels of Emotional Awareness Scale for Children (LEAS-C), and N-back task. Results: A positive correlation between emotional awareness and cognitive reappraisal was observed in the high-working-memory group (r = .54, p < .05), whereas no significant relationship was found in the low-working-memory group. In addition, the results of the analysis of variance (ANOVA) showed a significant interaction between emotional awareness and working memory capacity (F(1, 26) = 7.74, p < .05). Subsequent analysis of simple main effects confirmed that high working memory capacity significantly increases the use of cognitive reappraisal for high-emotional-awareness subjects, and significantly decreases the use of cognitive reappraisal for low-emotional-awareness subjects. Discussion: These results indicate that under the condition when one has an adequate ability for simultaneous processing of information, explicit understanding of emotion would contribute to adaptive cognitive emotion regulation. The findings are discussed along with neuroscientific claims.

Keywords: cognitive reappraisal, emotional awareness, emotion regulation, working memory

Procedia PDF Downloads 199

Authors: Ella Tyuryumina, Alexey Neznanov

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Finding algorithms to predict the growth of tumors has piqued the interest of researchers ever since the early days of cancer research. A number of studies were carried out as an attempt to obtain reliable data on the natural history of breast cancer growth. Mathematical modeling can play a very important role in the prognosis of tumor process of breast cancer. However, mathematical models describe primary tumor growth and metastases growth separately. Consequently, we propose a mathematical growth model for primary tumor and primary metastases which may help to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoM-IV and corresponding software. We are interested in: 1) modelling the whole natural history of primary tumor and primary metastases; 2) developing adequate and precise CoM-IV which reflects relations between PT and MTS; 3) analyzing the CoM-IV scope of application; 4) implementing the model as a software tool. The CoM-IV is based on exponential tumor growth model and consists of a system of determinate nonlinear and linear equations; corresponds to TNM classification. It allows to calculate different growth periods of primary tumor and primary metastases: 1) ‘non-visible period’ for primary tumor; 2) ‘non-visible period’ for primary metastases; 3) ‘visible period’ for primary metastases. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. Thus, the CoM-IV model and predictive software: a) detect different growth periods of primary tumor and primary metastases; b) make forecast of the period of primary metastases appearance; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of BC and facilitate optimization of diagnostic tests. The following are calculated by CoM-IV: the number of doublings for ‘nonvisible’ and ‘visible’ growth period of primary metastases; tumor volume doubling time (days) for ‘nonvisible’ and ‘visible’ growth period of primary metastases. The CoM-IV enables, for the first time, to predict the whole natural history of primary tumor and primary metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on primary tumor sizes. Summarizing: a) CoM-IV describes correctly primary tumor and primary distant metastases growth of IV (T1-4N0-3M1) stage with (N1-3) or without regional metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and manifestation of primary metastases.

Keywords: breast cancer, exponential growth model, mathematical modelling, primary metastases, primary tumor, survival

Procedia PDF Downloads 314

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

Procedia PDF Downloads 118

Authors: Dieter Veldsman, Theo Heyns Veldsman

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The knowledge economy demands an organisation that is flexible, adaptable and able to navigate the ever-changing environment. This fast-paced environment has however resulted in an organizational landscape that battles to engage employees, retain top talent and create meaningful work for its members. In the knowledge economy, the concept of organizational identity has become an important consideration as organisations aim to create a compelling and inviting narrative for all stakeholders across the business value chain. Values are often seen as the behavioural framework that informs organisational culture, yet often values are perceived to be inauthentic and misaligned with the true character or identity of the organisation and how it is perceived by different role players. This paper focuses on exploring the relationship between organisational identity and value systems by focusing on a case study within a multi-national organisation within South Africa. The paper evaluates the implementation of mixed methods OD approach that gathered collaborative inputs of more than 4500 employees who participated in crafting the newly established values system post a retrenchment process. The paper will evaluate the relationship between the newly crafted value system and the identity of the organisation as described by various internal and external stakeholders in order to explore potential alignment, dissonance and key insights into understanding the relationship between organisational identity and values. The case study will be reported from the perspective of an OD consultant who supported the transformation process over a period of 8 months and aims to provide key insights into values and identity alignment within knowledge economy organisations. From a practical perspective, the paper provides insights into how values are created, perceived and lived within organisations and the impact on employee engagement and culture.

Keywords: culture, organisational development, organisational identity, values

Procedia PDF Downloads 286

Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

Procedia PDF Downloads 205

Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Rhoda N. Kayongo

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As behavior analysts in education continue to debate on how higher institutions can continue to benefit from their social and academic related programs, higher education is facing challenges in the area of character development. This is manifested in the percentages of college completion rates, teen pregnancies, drug abuse, sexual abuse, suicide, plagiarism, lack of academic integrity, and violence among their students. Attending college is a perceived opportunity to positively influence the actions and behaviors of the next generation of society; thus colleges and universities have to provide opportunities to develop students’ values and behaviors. Prior studies were mainly conducted in private institutions and more so in developed countries. However, with the complexity of the nature of student body currently due to the changing world, a multidimensional approach combining multiple factors that enhance character development outcomes is needed to suit the changing trends. The main purpose of this study was to identify opportunities in colleges and develop a model for predicting character development outcomes. A survey questionnaire composed of 7 scales including in-classroom interaction, out-of-classroom interaction, school climate, personal lifestyle, home environment, and peer influence as independent variables and character development outcomes as the dependent variable was administered to a total of five hundred and one students of 3rd and 4th year level in selected public colleges and universities in the Philippines and Rwanda. Using structural equation modelling, a predictive model explained 57% of the variance in character development outcomes. Findings from the results of the analysis showed that in-classroom interactions have a substantial direct influence on character development outcomes of the students (r = .75, p < .05). In addition, out-of-classroom interaction, school climate, and home environment contributed to students’ character development outcomes but in an indirect way. The study concluded that in the classroom are many opportunities for teachers to teach, model and integrate character development among their students. Thus, suggestions are made to public colleges and universities to deliberately boost and implement experiences that cultivate character within the classroom. These may contribute tremendously to the students' character development outcomes and hence render effective models of behaviour analysis in higher education.

Keywords: character development, tertiary institutions, predictive model, behavior analysis

Procedia PDF Downloads 116

Authors: Damian Islas

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Debates on realism in science concern two different questions: (I) whether the unobservable entities posited by theories can be known; and (II) whether any knowledge we have of them is objective or not. Question (I) arises from the doubt that since observation is the basis of all our factual knowledge, unobservable entities cannot be known. Question (II) arises from the doubt that since scientific representations are inextricably laden with the subjective, idiosyncratic, and a priori features of human cognition and scientific practice, they cannot convey any reliable information on how their objects are in themselves. A way of understanding scientific realism (SR) is through three lines of inquiry: ontological, semantic, and epistemological. Ontologically, scientific realism asserts the existence of a world independent of human mind. Semantically, scientific realism assumes that theoretical claims about reality show truth values and, thus, should be construed literally. Epistemologically, scientific realism believes that theoretical claims offer us knowledge of the world. Nowadays, the literature on scientific realism has proceeded rather far beyond the realism versus antirealism debate. This stance represents a middle-ground position between the two according to which science can attain justified true beliefs concerning relational facts about the unobservable realm but cannot attain justified true beliefs concerning the intrinsic nature of any objects occupying that realm. That is, the structural content of scientific theories about the unobservable can be known, but facts about the intrinsic nature of the entities that figure as place-holders in those structures cannot be known. There are two possible versions of SR: Epistemological Structural Realism (ESR) and Ontic Structural Realism (OSR). On ESR, an agnostic stance is preserved with respect to the natures of unobservable entities, but the possibility of knowing the relations obtaining between those entities is affirmed. OSR includes the rather striking claim that when it comes to the unobservables theorized about within fundamental physics, relations exist, but objects do not. Focusing on ESR, questions arise concerning its ability to explain the empirical success of a theory. Empirical success certainly involves predictive success, and predictive success implies a theory’s power to make accurate predictions. But a theory’s power to make any predictions at all seems to derive precisely from its core axioms or laws concerning unobservable entities and mechanisms, and not simply the sort of structural relations often expressed in equations. The specific challenge to ESR concerns its ability to explain the explanatory and predictive power of successful theories without appealing to their substantive ontologies, which are often not preserved by their successors. The response to this challenge will depend on the various and subtle different versions of ESR and OSR stances, which show a sort of progression through eliminativist OSR to moderate OSR of gradual increase in the ontological status accorded to objects. Knowing the relations between unobserved entities is methodologically identical to assert that these relations between unobserved entities exist.

Keywords: eliminativist ontic structural realism, epistemological structuralism, moderate ontic structural realism, ontic structuralism

Procedia PDF Downloads 95

Authors: Omer Burak Iskender, Keck Voon Ling, Vincent Dubanchet, Luca Simonini

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This paper presents Guidance and Control (G&C) strategy to approach and synchronize with potentially rotating targets. The proposed strategy generates and tracks a safe trajectory for space servicing missions, including tasks like approaching, inspecting, and capturing. The main objective of this paper is to validate the G&C laws using a Hardware-In-the-Loop (HIL) setup with realistic rendezvous and docking equipment. Throughout this work, the assumption of full relative state feedback is relaxed by onboard sensors that bring realistic errors and delays and, while the proposed closed loop approach demonstrates the robustness to the above mentioned challenge. Moreover, G&C blocks are unified via the Model Predictive Control (MPC) paradigm, and the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description. In this work, G&C is formulated as a convex optimization problem where constraints such as thruster limits and the output constraints are explicitly handled. Furthermore, the Monte-Carlo method is used to evaluate the robustness of the proposed method to the initial condition errors, the uncertainty of the target's motion and attitude, and actuator errors. A capture scenario is tested with the robotic test bench that has onboard sensors which estimate the position and orientation of a drifting satellite through camera imagery. Finally, the approach is compared with currently used robust H-infinity controllers and guidance profile provided by the industrial partner. The HIL experiments demonstrate that the proposed strategy is a potential candidate for future space servicing missions because 1) the algorithm is real-time implementable as convex programming offers deterministic convergence properties and guarantee finite time solution, 2) critical physical and output constraints are respected, 3) robustness to sensor errors and uncertainties in the system is proven, 4) couples translational motion with rotational motion.

Keywords: dual quaternion, model predictive control, real-time experimental test, rendezvous and docking, spacecraft autonomy, space servicing

Procedia PDF Downloads 118