Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
This work addresses this issue with a tool used to predict and calculate the Minimum Control Speed on the Ground (VMCG) as well as the Minimum Control Airspeed (VMCA) of any existing or design-stage airplane. With simple line art of an airplane, a program called VORLAX is used to generate an aerodynamic database used to calculate the stability derivatives of an airplane. Using another program called Numerical Propulsion System Simulation (NPSS), a propulsion database is generated to use with the aerodynamic database to calculate both VMCG and VMCA.
This tool was tested using two airplanes, the Airbus A320 and the Lockheed Martin C130J-30 Super Hercules. The A320 does not use an Automatic Thrust Control System (ATCS), whereas the C130J-30 does use an ATCS. The tool was able to properly calculate and match known values of VMCG and VMCA for both of the airplanes. The fact that this tool was able to calculate the known values of VMCG and VMCA for both airplanes means that this tool would be able to predict the VMCG and VMCA of an airplane in the preliminary stages of design. This would allow design engineers the ability to use an Automatic Thrust Control System (ATCS) as part of the design of an airplane and still have the ability to predict the VMCG and VMCA of the airplane.
Sensorimotor adaptation is a type of learning that allows sustaining accurate movements by adjusting motor output. This allows the brain to adapt to temporary changes when engaged in a certain task. Within sensorimotor adaptation, visuomotor adaptation (VMA) is one’s ability to correct a visual perturbation. In this study, we present preliminary results on the effects of VMA with the control group, compared to groups undergoing trigeminal nerve stimulation (TNS) or SHAM (placebo) effects. Twenty-two healthy subjects with no past medical history participated in this study. Subjects performed a visuomotor rotation task, which required gradually adapting to a perturbation between hand motion and corresponding visual feedback. Five total blocks were completed: two familiarization blocks, one baseline block, one rotation block with a 30◦ counterclockwise rotation, and one washout block with no rotation. The control group performed better than the 120 Hz (TNS) and SHAM groups due to less directional error (DE) on the respective learning curves. Additionally, the control group adapted faster (less DE) than the SHAM groups that either felt stimulation, or did not feel the stimulation. The results yield new information regarding VMA which can be used in the future when comparing sensorimotor adaptation and its many applications.
As a result of the increase of pollution related to industrialization in Vietnam, acid rain has become a prevalent issue for Vietnamese farmers who are forced to rinse their crops – risking damage due to overwatering and poor harvest. Thus, the team was motivated to develop a solution to harmful impacts of acidic rainwater by creating a system with the ability to capture rainwater and determine its level of acidity in order to optimize the crop watering process, and promote productive crops. By conducting preliminary research on rainfall and tropical climate in Vietnam, existing products on the market, and pH sensors for monitoring and device material, the team was able to design a number of devices to collect, store, and measure the pH of rainwater. After developing a number of initial design requirements based on the needs of the farmers, a final prototype was developed using the best aspects of each initial design. Tests were conducted with varying structural and aqueous materials to represent a broad range of environmental conditions. While the scope of the project was ultimately limited to prototyping purposes, the principles explored throughout this thesis project can successfully be applied to a fully-functioning production model available for commercial use on Vietnamese farms. Given more time for development, improvements would be made in the extent of materials tested, and the configuration of electronics and data acquisition, in order to further optimize the process of determining rainwater acidity.