Stabilization and carbonization studies of polyacrylonitrile /carbon nanotube composite fibers

Carbon fibers contain more than 90 wt. % carbon. They have low density, high specific strength and modulus, and good temperature and chemical resistance. Therefore, they are important candidate as reinforcement materials. Carbon fiber is made by pyrolysing precursor polymers. Polyacrylonitrile (PAN) which has been used as precursor to produce high strength carbon fiber is used as precursor in this study. The theoretical tensile strength of carbon fibers can reach over 100 GPa. Currently, the best commercial carbon fibers reach only 7.5 GPa. To make good quality carbon fiber and to narrow the gap between theoretical values and currently achieved experimental properties, the entire manufacturing process including fiber spinning, stabilization and carbonization, needs to be improved optimized. In this dissertation, the stabilization processes of gel-spun PAN/carbon nanotubes (CNTs) composite fibers are studied. PAN/CNT (1 wt. % CNT) composite fibers are spun by dry-jet gel-spinning. Three types of CNTs with different number of walls and varying catalyst content are used as additives. The effect of different types of CNTs on the properties of the stabilized fibers was compared. It is found that the CNTs with the highest surface area shows the best reinforcement efficiency on the tensile modulus, and reduces the formation of β-amino nitrile. The residual catalyst in the range of 1 to 4 wt. % shows little effect on the mechanical properties of the stabilized fibers. Stabilization involves complex chemical reactions, including cyclization, oxidation, dehydration, and cross-linking. These complex reactions are separated by using different gas environments during stabilization. The cross-linking reaction has the highest activation energy among all stabilization reactions, and requires a temperature higher than 300 DegC to be completed. The effect of applied tension on the stabilized fiber properties are investigated, and it is found that higher tension leads to better properties for the stabilized fiber, including higher Young's modulus, higher orientation, less formation of β-amino nitrile, and less shrinkage. The relationship between stabilization conditions and the mechanical properties of the carbonized fiber is investigated, and the methods to identify optimum stabilization conditions are proposed. It is observed that the highest tension should be applied during both stabilization and carbonization, and the mechanical properties of the resulting carbon fibers are increased if fibers are further stabilized at a temperature of ~ 320 DegC to improve the cross-linking degree as compared with the fibers only stabilized at 255 DegC. The optimum stabilization time depends on both the stabilization temperature and on the applied tension. A new characterization method by monitoring the dynamic mechanical properties, while stabilization is in progress is used to narrow down the range of the optimum stabilization time. Also, the effect of carbonization temperature on the ultimate carbon fiber properties is studied in the batch process carbonization. Preliminary studies are carried out to find the relationship between the structure and properties of precursor fibers and the tensile strength of carbon fibers, including mechanical properties and co-monomers of precursor fibers.PhDCommittee Chair: Kumar, Satish; Committee Member: Graham, Samuel; Committee Member: Griffin, Anselm; Committee Member: Shofner, Meisha; Committee Member: Yao, Donggan

Maximizing the Information Authenticity in a Social Network

Information often distorts during the process of transmission in a social network, which is very common in many real-life applications. In this paper, we study the problem of maximizing the information authenticity of a social network. We propose a new model to characterize information distortion during the diffusion of influence. In order to trade off between optimality and complexity, we design a framework of greedy algorithms. Finally, we carry out a numerical experiment to show the effectiveness of the proposed algorithms

An Implementation of Multimodal Fusion System for Intelligent Digital Human Generation

With the rapid development of artificial intelligence (AI), digital humans have attracted more and more attention and are expected to achieve a wide range of applications in several industries. Then, most of the existing digital humans still rely on manual modeling by designers, which is a cumbersome process and has a long development cycle. Therefore, facing the rise of digital humans, there is an urgent need for a digital human generation system combined with AI to improve development efficiency. In this paper, an implementation scheme of an intelligent digital human generation system with multimodal fusion is proposed. Specifically, text, speech and image are taken as inputs, and interactive speech is synthesized using large language model (LLM), voiceprint extraction, and text-to-speech conversion techniques. Then the input image is age-transformed and a suitable image is selected as the driving image. Then, the modification and generation of digital human video content is realized by digital human driving, novel view synthesis, and intelligent dressing techniques. Finally, we enhance the user experience through style transfer, super-resolution, and quality evaluation. Experimental results show that the system can effectively realize digital human generation. The related code is released at https://github.com/zyj-2000/CUMT_2D_PhotoSpeaker

Identification of discharge regimes of cyclone dipleg-trickle valve system based on pressure fluctuation profiles

An experiment was conducted on the Φ150mm×5000mmcyclone dipleg-trickle valve setup, which was focused on analyzing the discharge characteristics of trickle valve of cyclone dipleg by means of the dynamic pressure measurement. The effects of two operating parameters, negative pressure drop (0~11kPa) and solids flux rate (0~50 kg/m2.s), on the discharge patterns were investigated. The experimental results show that there are two kinds of discharge patterns in the trickle valve. One is continuous trickling discharge at low negative pressure drop and high solids flux rate, which is characterized by valve plate opening continuously, and the measured pressure with high frequency and low amplitude. The other is intermittent periodic dumping discharge at high negative pressure drop and low solids flux rate, which has the properties of valve plate opening interval, and the measured pressure with low frequency and high amplitude. The two discharge patterns could transform each other as varying the negative pressure drop or solids flux rate. The discharge regime map was proposed based on the experimental data, which is related to the negative. Please click Additional Files below to see the full abstract

Masked Pretraining for Multi-Agent Decision Making

Building a single generalist agent with zero-shot capability has recently sparked significant advancements in decision-making. However, extending this capability to multi-agent scenarios presents challenges. Most current works struggle with zero-shot capabilities, due to two challenges particular to the multi-agent settings: a mismatch between centralized pretraining and decentralized execution, and varying agent numbers and action spaces, making it difficult to create generalizable representations across diverse downstream tasks. To overcome these challenges, we propose a \textbf{Mask}ed pretraining framework for \textbf{M}ulti-\textbf{a}gent decision making (MaskMA). This model, based on transformer architecture, employs a mask-based collaborative learning strategy suited for decentralized execution with partial observation. Moreover, MaskMA integrates a generalizable action representation by dividing the action space into actions toward self-information and actions related to other entities. This flexibility allows MaskMA to tackle tasks with varying agent numbers and thus different action spaces. Extensive experiments in SMAC reveal MaskMA, with a single model pretrained on 11 training maps, can achieve an impressive 77.8% zero-shot win rate on 60 unseen test maps by decentralized execution, while also performing effectively on other types of downstream tasks (\textit{e.g.,} varied policies collaboration and ad hoc team play).Comment: 17 page

Effects of EGR rates on combustion and emission characteristics in a diesel engine with n-butanol/PODE3-4/diesel blends

An experimental investigation is conducted on the influence of EGR (Exhaust Gas Recirculation) rates (0–40%) on the combustion and emission characteristics of n-butanol/diesel/PODE3-4 blends at low-temperature combustion mode in diesel engine. The results show that at identical EGR rate, compared to D100 (diesel fuel), the peak values both of the mean cylinder pressure and the heat release rate of BD20 (20% butanol and 80% diesel in volume) are increased, ignition delay is extended, and the brake thermal efficiency is enhanced. Concerning BD20 blended with PODE3-4, the ignition delay is shortened, while both the brake thermal efficiency and the combustion efficiency increase. At the EGR rate below 30%, as the EGR rate grows, the effects on emission of soot, CO and HC are not significant, while the emission of NOx is sharply reduced; when the EGR rate is above 30%, as it grows, the emissions of soot, CO, and HC drastically rise. As EGR rate grows, the total particulate matter (PM) number concentrations of four fuels firstly decline and then rise, the total PM mass concentrations keep stable firstly and then rise drastically. As the proportion of added PODE3-4 in BD20 grows, the particle geometric mean diameters further decrease

Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning

Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost of conducting planning online, recent practices tend to distill optimized action sequences into an RL policy during the training phase. Although the distillation can incorporate both the foresight of planning and the exploration ability of RL policies, the theoretical understanding of these methods is yet unclear. In this paper, we extend the policy improvement step of Soft Actor-Critic (SAC) by developing an approach to distill from model-based planning to the policy. We then demonstrate that such an approach of policy improvement has a theoretical guarantee of monotonic improvement and convergence to the maximum value defined in SAC. We discuss effective design choices and implement our theory as a practical algorithm -- Model-based Planning Distilled to Policy (MPDP) -- that updates the policy jointly over multiple future time steps. Extensive experiments show that MPDP achieves better sample efficiency and asymptotic performance than both model-free and model-based planning algorithms on six continuous control benchmark tasks in MuJoCo

Grasp Multiple Objects with One Hand

The human hand's complex kinematics allow for simultaneous grasping and manipulation of multiple objects, essential for tasks like object transfer and in-hand manipulation. Despite its importance, robotic multi-object grasping remains underexplored and presents challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a two-stage method for multi-object grasping on a tabletop with a multi-finger dexterous hand. It involves (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Experimental results primarily focus on dual-object grasping and report a 44.13% success rate, showcasing adaptability to unseen object configurations and imprecise grasps. The framework also demonstrates the capability to grasp more than two objects, albeit at a reduced inference speed

Maximum Entropy Heterogeneous-Agent Mirror Learning

Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample inefficiency, brittleness regarding hyperparameters, and the risk of converging to a suboptimal Nash Equilibrium. To resolve these issues, in this paper, we propose a novel theoretical framework, named Maximum Entropy Heterogeneous-Agent Mirror Learning (MEHAML), that leverages the maximum entropy principle to design maximum entropy MARL actor-critic algorithms. We prove that algorithms derived from the MEHAML framework enjoy the desired properties of the monotonic improvement of the joint maximum entropy objective and the convergence to quantal response equilibrium (QRE). The practicality of MEHAML is demonstrated by developing a MEHAML extension of the widely used RL algorithm, HASAC (for soft actor-critic), which shows significant improvements in exploration and robustness on three challenging benchmarks: Multi-Agent MuJoCo, StarCraftII, and Google Research Football. Our results show that HASAC outperforms strong baseline methods such as HATD3, HAPPO, QMIX, and MAPPO, thereby establishing the new state of the art. See our project page at https://sites.google.com/view/mehaml

MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Robust multi-agent reinforcement learning (MARL) necessitates resilience to uncertain or worst-case actions by unknown allies. Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios. Attempts to simplify this complexity often yield overly pessimistic policies, inadequate robustness across scenarios and high computational demands. Unlike these approaches, humans naturally learn adaptive and resilient behaviors without the necessity of preparing for every conceivable worst-case scenario. Motivated by this, we propose MIR2, which trains policy in routine scenarios and minimize Mutual Information as Robust Regularization. Theoretically, we frame robustness as an inference problem and prove that minimizing mutual information between histories and actions implicitly maximizes a lower bound on robustness under certain assumptions. Further analysis reveals that our proposed approach prevents agents from overreacting to others through an information bottleneck and aligns the policy with a robust action prior. Empirically, our MIR2 displays even greater resilience against worst-case adversaries than max-min optimization in StarCraft II, Multi-agent Mujoco and rendezvous. Our superiority is consistent when deployed in challenging real-world robot swarm control scenario. See code and demo videos in Supplementary Materials