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- Creators: School of Life Sciences
Intelligent agents learn from experiences, and in times of uncertainty use the knowl-
edge acquired to make decisions and accomplish their individual or team objectives.
Agent objectives are defined using cost functions designed uniquely for the collective
task being performed. Individual agent costs are coupled in such a way that group ob-
jective is attained while minimizing individual costs. Information Asymmetry refers
to situations where interacting agents have no knowledge or partial knowledge of cost
functions of other agents. By virtue of their intelligence, i.e., by learning from past
experiences agents learn cost functions of other agents, predict their responses and
act adaptively to accomplish the team’s goal.
Algorithms that agents use for learning others’ cost functions are called Learn-
ing Algorithms, and algorithms agents use for computing actuation (control) which
drives them towards their goal and minimize their cost functions are called Control
Algorithms. Typically knowledge acquired using learning algorithms is used in con-
trol algorithms for computing control signals. Learning and control algorithms are
designed in such a way that the multi-agent system as a whole remains stable during
learning and later at an equilibrium. An equilibrium is defined as the event/point
where cost functions of all agents are optimized simultaneously. Cost functions are
designed so that the equilibrium coincides with the goal state multi-agent system as
a whole is trying to reach.
In collective load transport, two or more agents (robots) carry a load from point
A to point B in space. Robots could have different control preferences, for example,
different actuation abilities, however, are still required to coordinate and perform
load transport. Control preferences for each robot are characterized using a scalar
parameter θ i unique to the robot being considered and unknown to other robots.
With the aid of state and control input observations, agents learn control preferences
of other agents, optimize individual costs and drive the multi-agent system to a goal
state.
Two learning and Control algorithms are presented. In the first algorithm(LCA-
1), an existing work, each agent optimizes a cost function similar to 1-step receding
horizon optimal control problem for control. LCA-1 uses recursive least squares as
the learning algorithm and guarantees complete learning in two time steps. LCA-1 is
experimentally verified as part of this thesis.
A novel learning and control algorithm (LCA-2) is proposed and verified in sim-
ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear
quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-
gorithm similar to line search methods, and guarantees learning convergence to true
values asymptotically.
Simulations and hardware implementation show that the LCA-2 is stable for a
variety of systems. Load transport is demonstrated using both the algorithms. Ex-
periments running algorithm LCA-2 are able to resist disturbances and balance the
assumed load better compared to LCA-1.
Non-destructive testing (NDT) and structural health monitoring (SHM) are widely used for this purpose. Different types of NDT techniques have been proposed for the damage detection, such as optical image, ultrasound wave, thermography, eddy current, and microwave. The focus in this study is on the wave-based detection method, which is grouped into two major categories: feature-based damage detection and model-assisted damage detection. Both damage detection approaches have their own pros and cons. Feature-based damage detection is usually very fast and doesn’t involve in the solution of the physical model. The key idea is the dimension reduction of signals to achieve efficient damage detection. The disadvantage is that the loss of information due to the feature extraction can induce significant uncertainties and reduces the resolution. The resolution of the feature-based approach highly depends on the sensing path density. Model-assisted damage detection is on the opposite side. Model-assisted damage detection has the ability for high resolution imaging with limited number of sensing paths since the entire signal histories are used for damage identification. Model-based methods are time-consuming due to the requirement for the inverse wave propagation solution, which is especially true for the large 3D structures.
The motivation of the proposed method is to develop efficient and accurate model-based damage imaging technique with limited data. The special focus is on the efficiency of the damage imaging algorithm as it is the major bottleneck of the model-assisted approach. The computational efficiency is achieved by two complimentary components. First, a fast forward wave propagation solver is developed, which is verified with the classical Finite Element(FEM) solution and the speed is 10-20 times faster. Next, efficient inverse wave propagation algorithms is proposed. Classical gradient-based optimization algorithms usually require finite difference method for gradient calculation, which is prohibitively expensive for large degree of freedoms. An adjoint method-based optimization algorithms is proposed, which avoids the repetitive finite difference calculations for every imaging variables. Thus, superior computational efficiency can be achieved by combining these two methods together for the damage imaging. A coupled Piezoelectric (PZT) damage imaging model is proposed to include the interaction between PZT and host structure. Following the formulation of the framework, experimental validation is performed on isotropic and anisotropic material with defects such as cracks, delamination, and voids. The results show that the proposed method can detect and reconstruct multiple damage simultaneously and efficiently, which is promising to be applied to complex large-scale engineering structures.
This research studies integration of Ultra-Capacitor (UC) to FCHEV. The objective is to analyze the effect of integrating UCs on the transient response of FCHEV powertrain. UCs has higher power density which can overcome slow dynamics of fuel cell. A power management strategy utilizing peak power shaving strategy is implemented. The goal is to decrease power load on batteries and operate fuel cell stack in it’s most efficient region. Complete model to simulate the physical behavior of UC-Integrated FCHEV (UC-FCHEV) is developed using Matlab/SIMULINK. The fuel cell polarization curve is utilized to devise operating points of the fuel cell to maintain its operation at most efficient region. Results show reduction of hydrogen consumption in aggressive US06 drive cycle from 0.29 kg per drive cycle to 0.12 kg. The maximum charge/discharge battery current was reduced from 286 amperes to 110 amperes in US06 drive cycle. Results for the FUDS drive cycle show a reduction in fuel consumption from 0.18 kg to 0.05 kg in one drive cycle. This reduction in current increases the life of the battery since its protected from overcurrent. The SOC profile of the battery also shows that the battery is not discharged to its minimum threshold which increasing the health of the battery based on number of charge/discharge cycles.
TSPO was discovered in 1977 and it’s function is still currently unknown. Significant research has suggested that TSPO functions in steroidogenesis to import cholesterol from the mitochondrial outer membrane (MOM) to the mitochondrial inner membrane (MIM) where it is converted into steroids. There were two indications that this is TSPOs main function: its elevated levels in steroidogenic tissue and its primary location in the MOM. There is evidence of TSPO binding cholesterol with high affinity, however there is not currently evidence of TSPO transporting cholesterol. STAR, ACBD1, and ACBD3 are proteins thought to be associated with TSPO and steroidogenesis. However, the distribution of these proteins in various eukaryotes show little similarity suggesting that TSPO functions independently. The function of TSPO in steroid synthesis has been called into question because a well-cited research paper claimed that TSPO knockdown resulted in embryonic lethal mice, however there was no evidence presented from their study and this experiment did not produce the same results when repeated in later studies. There are also studies that show TSPO may not be involved in regulation of sterols, but instead may regulate cell stress. The elevated levels of TSPO during inflammation suggest a role for TSPO in cellular stress. Binding interactions with porphyrins and heme also support that TSPO may modulate stress levels. We used the phylogeny of TSPO in order to gain greater insight into the evolutionary function of TSPO. NCBI BLAST searches revealed that TSPO was present in bacteria and had a widespread but patchy distribution in a small set of eukaryotes. From these initial results, we were prompted to search a larger set of eukaryotes for TSPO. All of the prokaryotic and eukaryotic TSPO sequences were used to create a phylogenetic tree that would provide greater insight into the evolution and function of TSPO. If TSPO was from a common ancestor, it is probable that its function is related to sterol regulation whereas if gained in eukaryotes by horizontal gene transfer from bacteria its function is related to stress regulation. The phylogenetic tree was most consistent with an ancestral origin of TSPO with an evolutionary function related to steroid synthesis regulation. However, there is not sufficient research to confirm the function of TSPO.
Recent studies indicate that words containing /ӕ/ and /u/ vowel phonemes can be mapped onto the emotional dimension of arousal. Specifically, the wham-womb effect describes the inclination to associate words with /ӕ/ vowel-sounds (as in “wham”) with high-arousal emotions and words with /u/ vowel-sounds (as in “womb”) with low-arousal emotions. The objective of this study was to replicate the wham-womb effect using nonsense pseudowords and to test if findings extend with use of a novel methodology that includes verbal auditory and visual pictorial stimuli, which can eventually be used to test young children. We collected data from 99 undergraduate participants through an online survey. Participants heard pre-recorded pairs of monosyllabic pseudowords containing /ӕ/ or /u/ vowel phonemes and then matched individual pseudowords to illustrations portraying high or low arousal emotions. Two t-tests were conducted to analyze the size of the wham-womb effect across pseudowords and across participants, specifically the likelihood that /ӕ/ sounds are paired with high arousal images and /u/ sounds with low arousal images. Our findings robustly confirmed the wham-womb effect. Participants paired /ӕ/ words with high arousal emotion pictures and /u/ words with low arousal ones at a 73.2% rate with a large effect size. The wham-womb effect supports the idea that verbal acoustic signals tend to be tied to embodied facial musculature that is related to human emotions, which supports the adaptive value of sound symbolism in language evolution and development.