IU Indianapolis ScholarWorks :: Browsing by Subject "Computer simulation"

Browsing by Subject "Computer simulation"

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A New Sparse Simplex Model for Brain Anatomical and Genetic Network Analysis
(Springer Nature, 2013) Huang, Heng; Yan, Jingwen; Nie, Feiping; Huang, Jin; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of Medicine
The Allen Brain Atlas (ABA) database provides comprehensive 3D atlas of gene expression in the adult mouse brain for studying the spatial expression patterns in the mammalian central nervous system. It is computationally challenging to construct the accurate anatomical and genetic networks using the ABA 4D data. In this paper, we propose a novel sparse simplex model to accurately construct the brain anatomical and genetic networks, which are important to reveal the brain spatial expression patterns. Our new approach addresses the shift-invariant and parameter tuning problems, which are notorious in the existing network analysis methods, such that the proposed model is more suitable for solving practical biomedical problems. We validate our new model using the 4D ABA data, and the network construction results show the superior performance of the proposed sparse simplex model.
Automatic Modeling and Simulation of Networked Components
(2011) Bruce, Nathaniel William; Koskie, Sarah; Chen, Yaobin; Li, Lingxi
Testing and verification are essential to safe and consistent products. Simulation is a widely accepted method used for verification and testing of distributed components. Generally, one of the major hurdles in using simulation is the development of detailed and accurate models. Since there are time constraints on projects, fast and effective methods of simulation model creation emerge as essential for testing. This thesis proposes to solve these issues by presenting a method to automatically generate a simulation model and run a random walk simulation using that model. The method is automated so that a modeler spends as little time as possible creating a simulation model and the errors normally associated with manual modeling are eliminated. The simulation is automated to allow a human to focus attention on the device that should be tested. The communications transactions between two nodes on a network are recorded as a trace file. This trace file is used to automatically generate a finite state machine model. The model can be adjusted by a designer to add missing information and then simulated in real-time using a software-in-the-loop approach. The innovations in this thesis include adaptation of a synthesis method for use in simulation, introduction of a random simulation method, and introduction of a practical evaluation method for two finite state machines. Test results indicate that nodes can be adequately replaced by models generated automatically by these methods. In addition, model construction time is reduced when comparing to the from scratch model creation method.
Bayesian Optimization of Active Materials for Lithium-Ion Batteries
(SAE, 2021-04) Valladares, Homero; Li, Tianyi; Zhu, Likun; El-Mounayri, Hazim; Tovar, Andres; Hashem, Ahmed; Abdel-Ghany, Ashraf E.; Mechanical Engineering, School of Engineering and Technology
The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs. The cycle life cannot be predicted using a simple closed-form expression but only through the cycling performance test or a numerical simulation. BGO has two main components: a surrogate probabilistic model of the black-box function and an acquisition function that guides the optimization. This research employs BGO in the design of cathode active materials for LIB cells. The training data corresponds to the initial capacity and cycle life of five coin cells with different compositions of LiNixMn2 − xO4 in their cathode, where x is the content of Ni. BGO utilizes the experimental data to identify five new compositions that can produce cells with high initial capacity and\or large cycle life. The surrogate models of the initial capacity and cycle life are Gaussian Processes. The acquisition function is the constrained multi-objective expected improvement. The results show that BGO can identify high-performance active materials for LIBs. Designers can use the data generated during the optimization to decide the composition of the next batch of active materials to be tested, i.e., guide the physical experimentation.
Computational Methods and Models in Circulatory and Reproductive Systems
(Hindawi, 2016) Tian, Fang-Bao; Sui, Yi; Zhu, Luoding; Shu, Chang; Sung, Hyung J.; Department of Mathematical Sciences, School of Science
A Computational Study of the Mechanism for F1-ATPase Inhibition by the Epsilon Subunit
(2013) Thomson, Karen J.; Pu, Jingzhi; Ge, Haibo; Sardar, Rajesh; Long, Eric C. (Eric Charles)
The multi-protein complex of F0F1 ATP synthase has been of great interest in the fields of microbiology and biochemistry, due to the ubiquitous use of ATP as a biological energy source. Efforts to better understand this complex have been made through structural determination of segments based on NMR and crystallographic data. Some experiments have provided useful data, while others have brought up more questions, especially when structures and functions are compared between bacteria and species with chloroplasts or mitochondria. The epsilon subunit is thought to play a signi cant role in the regulation of ATP synthesis and hydrolysis, yet the exact pathway is unknown due to the experimental difficulty in obtaining data along the transition pathway. Given starting and end point protein crystal structures, the transition pathway of the epsilon subunit was examined through computer simulation.The purpose of this investigation is to determine the likelihood of one such proposed mechanism for the involvement of the epsilon subunit in ATP regulation in bacterial species such as E. coli.
Decentralized and Partially Decentralized Multi-Agent Reinforcement Learning
(2013-08-22) Tilak, Omkar Jayant; Mukhopadhyay, Snehasis; Si, Luo; Neville, Jennifer; Raje, Rajeev; Tuceryan, Mihran; Gorman, William J.
Multi-agent systems consist of multiple agents that interact and coordinate with each other to work towards to certain goal. Multi-agent systems naturally arise in a variety of domains such as robotics, telecommunications, and economics. The dynamic and complex nature of these systems entails the agents to learn the optimal solutions on their own instead of following a pre-programmed strategy. Reinforcement learning provides a framework in which agents learn optimal behavior based on the response obtained from the environment. In this thesis, we propose various novel de- centralized, learning automaton based algorithms which can be employed by a group of interacting learning automata. We propose a completely decentralized version of the estimator algorithm. As compared to the completely centralized versions proposed before, this completely decentralized version proves to be a great improvement in terms of space complexity and convergence speed. The decentralized learning algorithm was applied; for the first time; to the domains of distributed object tracking and distributed watershed management. The results obtained by these experiments show the usefulness of the decentralized estimator algorithms to solve complex optimization problems. Taking inspiration from the completely decentralized learning algorithm, we propose the novel concept of partial decentralization. The partial decentralization bridges the gap between the completely decentralized and completely centralized algorithms and thus forms a comprehensive and continuous spectrum of multi-agent algorithms for the learning automata. To demonstrate the applicability of the partial decentralization, we employ a partially decentralized team of learning automata to control multi-agent Markov chains. More flexibility, expressiveness and flavor can be added to the partially decentralized framework by allowing different decentralized modules to engage in different types of games. We propose the novel framework of heterogeneous games of learning automata which allows the learning automata to engage in disparate games under the same formalism. We propose an algorithm to control the dynamic zero-sum games using heterogeneous games of learning automata.
Developing a Neural Signal Processor Using the Extended Analog Computer
(2013-08-21) Soliman, Muller Mark; Yoshida, Ken; Eberhart, Russell C.; Mills, Jonathan W. (Jonathan Wayne); Berbari, Edward J.
Neural signal processing to decode neural activity has been an active research area in the last few decades. The next generation of advanced multi-electrode neuroprosthetic devices aim to detect a multiplicity of channels from multiple electrodes, making the relatively time-critical processing problem massively parallel and pushing the computational demands beyond the limits of current embedded digital signal processing (DSP) techniques. To overcome these limitations, a new hybrid computational technique was explored, the Extended Analog Computer (EAC). The EAC is a digitally confgurable analog computer that takes advantage of the intrinsic ability of manifolds to solve partial diferential equations (PDEs). They are extremely fast, require little power, and have great potential for mobile computing applications. In this thesis, the EAC architecture and the mechanism of the formation of potential/current manifolds was derived and analyzed to capture its theoretical mode of operation. A new mode of operation, resistance mode, was developed and a method was devised to sample temporal data and allow their use on the EAC. The method was validated by demonstration of the device solving linear diferential equations and linear functions, and implementing arbitrary finite impulse response (FIR) and infinite impulse response (IIR) linear flters. These results were compared to conventional DSP results. A practical application to the neural computing task was further demonstrated by implementing a matched filter with the EAC simulator and the physical prototype to detect single fiber action potential from multiunit data streams derived from recorded raw electroneurograms. Exclusion error (type 1 error) and inclusion error (type 2 error) were calculated to evaluate the detection rate of the matched filter implemented on the EAC. The detection rates were found to be statistically equivalent to that from DSP simulations with exclusion and inclusion errors at 0% and 1%, respectively.
A Dynamically Configurable Discrete Event Simulation Framework for Many-Core System-on-Chips
(2010) Barnes, Christopher J.; Lee, Jaehwan John; King, Brian S.; Chien, Yung Ping Stanley
Industry trends indicate that many-core heterogeneous processors will be the next-generation answer to Moore's law and reduced power consumption. Thus, both academia and industry are focused on the challenges presented by many-core heterogeneous processor designs. In many cases, researchers use discrete event simulators to research and validate new computer architecture innovations. However, there is a lack of dynamically configurable discrete event simulation environments for the testing and development of many-core heterogeneous processors. To fulfill this need we present Mhetero, a retargetable framework for cycle-accurate simulation of heterogeneous many-core processors along with the cycle-accurate simulation of their associated network-on-chip communication infrastructure. Mhetero is the result of research into dynamically configurable and highly flexible simulation tools with which users are free to produce custom instruction sets and communication methods in a highly modular design environment. In this thesis we will discuss our approach to dynamically configurable discrete event simulation and present several experiments performed using the framework to exemplify how Mhetero, and similarly constructed simulators, may be used for future innovations.
Electromagnetic Field Stimulation Therapy for Alzheimer’s Disease
(Wolters Kluwer, 2024) Perez, Felipe P.; Morisaki, Jorge; Kanakri, Haitham; Rizkalla, Maher; Medicine, School of Medicine
Alzheimer's disease (AD) is the most common neurodegenerative dementia worldwide. AD is a multifactorial disease that causes a progressive decline in memory and function precipitated by toxic beta-amyloid (Aβ) proteins, a key player in AD pathology. In 2022, 6.5 million Americans lived with AD, costing the nation $321billion. The standard of care for AD treatment includes acetylcholinesterase inhibitors (AchEIs), NMDA receptor antagonists, and monoclonal antibodies (mAbs). However, these methods are either: 1) ineffective in improving cognition, 2) unable to change disease progression, 3) limited in the number of therapeutic targets, 4) prone to cause severe side effects (brain swelling, microhemorrhages with mAb, and bradycardia and syncope with AchEIs), 5) unable to effectively cross the blood-brain barrier, and 6) lack of understanding of the aging process on the disease. mAbs are available to lower Aβ, but the difficulties of reducing the levels of the toxic Aβ proteins in the brain without triggering brain swelling or microhemorrhages associated with mAbs make the risk-benefit profile of mAbs unclear. A novel multitarget, effective, and safe non-invasive approach utilizing Repeated Electromagnetic Field Stimulation (REMFS) lowers Aβ levels in human neurons and memory areas, prevents neuronal death, stops disease progression, and improves memory without causing brain edema or bleeds in AD mice. This REMFS treatment has not been developed for humans because current EMF devices have poor penetration depth and inhomogeneous E-field distribution in the brain. Here, we discussed the biology of these effects in neurons and the design of optimal devices to treat AD.
Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions
(American Chemical Society, 2023) Pan, Xiaoliang; Van, Richard; Pu, Jingzhi; Nam, Kwangho; Mao, Yuezhi; Shao, Yihan; Chemistry and Chemical Biology, School of Science
In enzyme mechanistic studies and mutant design, it is highly desirable to know the individual residue contributions to the reaction free energy and barrier. In this work, we show that such free energy contributions from each residue can be readily obtained by postprocessing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through a mean force integration along the minimum free energy pathway, one can obtain the electrostatic, polarization, and van der Waals contributions from each residue to the free energy barrier. Separately, a similar analysis procedure allows us to assess the contribution from different collective variables along the reaction coordinate. The chorismate mutase reaction is used to demonstrate the utilization of these two trajectory analysis tools.

Browsing by Subject "Computer simulation"

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