Publications
Modular Aid and Power Pallet (MAPP): FY18 Energy Technical Investment Program
Summary
Summary
Electric power is a critical element of rapid response disaster relief efforts. Generators currently used have high failure rates and require fuel supply chains, and standardized renewable power systems are not yet available. In addition, none of these systems are designed for easy adaptation or repairs in the field to...
Artificial intelligence: short history, present developments, and future outlook, final report
- Artificial Intelligence Technology and Systems
- Homeland Sensors and Analytics
- Lincoln Laboratory Supercomputing Center
- Cyber System Assessments
- Cyber Operations and Analysis Technology
- Cyber-Physical Systems
- Artificial Intelligence Software Architectures and Algorithms
- Space Systems Analysis and Test
Summary
Summary
The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the...
Component standards for stable microgrids
Summary
Summary
This paper is motivated by the need to ensure fast microgrid stability. Modeling for purposes of establishing stability criterion and possible implementations are described. In particular, this paper proposes that highly heterogeneous microgrids comprising both conventional equipment and equipment based on rapidly emerging new technologies can be modeled as purely...
High performance computing techniques with power systems simulations
Summary
Summary
Small electrical networks (i.e., microgrids) and machine models (synchronous generators, induction motors) can be simulated fairly easily, on sequential processes. However, running a large simulation on a single process becomes infeasible because of complexity and timing issues. Scalability becomes an increasingly important issue for larger simulations, and the platform for...
GraphChallenge.org: raising the bar on graph analytic performance
Summary
Summary
The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...
Simulation approach to sensor placement using Unity3D
Summary
Summary
3D game simulation engines have demonstrated utility in the areas of training, scientific analysis, and knowledge solicitation. This paper will make the case for the use of 3D game simulation engines in the field of sensor placement optimization. Our study used a series of parallel simulations in the Unity3D simulation...
Fuel production systems for remote areas via an aluminum energy vector
Summary
Summary
Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid...
Detecting intracranial hemorrhage with deep learning
Summary
Summary
Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our...
Adversarial co-evolution of attack and defense in a segmented computer network environment
Summary
Summary
In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of attackers. We consider network segmentation, a widely recommended...
Learning network architectures of deep CNNs under resource constraints
Summary
Summary
Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyperparameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully...