Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- 3D (1)
- Automated (1)
- Blockchain (1)
- DQN (1)
- DRM (1)
-
- Data market (1)
- Deep Reinforcement Learning (1)
- Distributed computing (1)
- Economics (1)
- Experience Replay (1)
- Explainable AI (1)
- Explainer (1)
- Geospatial (1)
- Isolation Forest (1)
- LIME Model (1)
- Machine Learning (1)
- Model Interpretability (1)
- Models (1)
- Prioritized Experience Replay (1)
- Privacy (1)
- Pump (1)
- Python (1)
- SAP (1)
- Seeq (1)
- Site (1)
- Unsupervised Learning (1)
- Work Order (1)
- XAI (1)
Articles 1 - 6 of 6
Full-Text Articles in Entire DC Network
Well Oiled Machine: Classifying Machinery Performance Reductions Using Work Order Data, Jacob Brionez, Amber Burnett, Cho Kim, Scott M. Whitney, Thomas N. Anderson, Sumeet Treehan
Well Oiled Machine: Classifying Machinery Performance Reductions Using Work Order Data, Jacob Brionez, Amber Burnett, Cho Kim, Scott M. Whitney, Thomas N. Anderson, Sumeet Treehan
SMU Data Science Review
Work Order (WO) data from System Applications and Products in Data Processing (SAP) software contains valuable information about what WOs intend to accomplish. Using SAP work order data, with time-series machinery sensor data combined into the same dataset, provides an opportunity to optimize prediction models to increase performance. Ideally, WO data can be utilized to help predict machinery's anticipated performance and can help prioritize a WO among others based on the anticipated machinery performance. It is possible to identify anomalies in pump sensor data using the Isolation Forest algorithm as the method for anomaly detection. The relationship between the sensor …
Automated Interactive 3d Geospatial Data Assimilation, Formatting And Visualization System For Development Of Subsurface Conceptual Site Models, Aaron Cattley, Gavin Hudgeons, Bruce Lee
Automated Interactive 3d Geospatial Data Assimilation, Formatting And Visualization System For Development Of Subsurface Conceptual Site Models, Aaron Cattley, Gavin Hudgeons, Bruce Lee
SMU Data Science Review
The natural evolution of the collection and storage of sub- surface data in Texas has resulted in the current state where data for certain resources, such as water resources, have not been assimilated with state oil and gas and injection data in a meaningful way that allows for rapid understanding and data analysis for a physical land site. The consequences that result due to data from different spheres not being in sync are often duplication of work being performed but not in a consistent manner. However, the reality is that the infrastructure and impacts of these sectors are deeply intertwined. …
Accelerating Reinforcement Learning With Prioritized Experience Replay For Maze Game, Chaoshun Hu, Mehesh Kuklani, Paul Panek
Accelerating Reinforcement Learning With Prioritized Experience Replay For Maze Game, Chaoshun Hu, Mehesh Kuklani, Paul Panek
SMU Data Science Review
In this paper we implemented two ways of improving the performance of reinforcement learning algorithms. We proposed a new equation to prioritize transition samples to improve model accuracy, and by deploying a generalized solver of randomly-generated two-dimensional mazes on a distributed computing platform, our dual-network model is available to others for further research and development. Reinforcement Learning is concerned with identifying the optimal sequence of actions for an agent to take in order to reach an objective to achieve the highest score in the future. Complex situations can lead to computational challenges in terms of both finding the best answer …
Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia
Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia
SMU Data Science Review
In this paper, we introduce a proof of concept that addresses the assumption and limitation of linear local boundaries by Local Interpretable Model-Agnostic Explanations (LIME), a popular technique used to add interpretability and explainability to black box models. LIME is a versatile explainer capable of handling different types of data and models. At the local level, LIME creates a linear relationship for a given prediction through generated sample points to present feature importance. We redefine the linear relationships presented by LIME as quadratic relationships and expand its flexibility in non-linear cases and improve the accuracy of feature interpretations. We coin …
The Data Market: A Proposal To Control Data About You, David Shaw, Daniel W. Engels
The Data Market: A Proposal To Control Data About You, David Shaw, Daniel W. Engels
SMU Data Science Review
The current legal and economic infrastructure facilitating data collection practices and data analysis has led to extreme over-collection of data and the overall loss of personal privacy. Data over-collection has led to a secondary market for consumer data that is invisible to the consumer and results in a person's data being distributed far beyond their knowledge or control. In this paper, we propose a Data Market framework and design for personal data management and privacy protection in which the individual controls and profits from the dissemination of their data. Our proposed Data Market uses a market-based approach utilizing blockchain distributed …
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
SMU Data Science Review
In the current market, successful fitness tracking devices utilize heart rate and GPS to determine performance. These devices are useful, but don't extensively classify stationary exercise. This paper proposes a modern approach for tuning and investigating optimal neural network types on stationary exercises using Inertial Measurement Units (IMUs). Using three IMUs located on the ankle, waist, and wrist, data is collected to map the body as it moves during the stationary physical activity. A novel five-stage deep learning tuning system was written and deployed to classify user movement as one of three classes: air squats, jumping jacks, and kettlebell swings. …