OPTIMIZATION OF SHAPE AND CONTROL OF LINEAR AND NONLINEAR WAVE ENERGY CONVERTERS

In this dissertation, we address the optimal control and shape optimization of Wave Energy Converters. The wave energy converters considered in this study are the single-body heaving wave energy converters, and the two-body heaving wave energy converters. Different types of wave energy converters are modeled mathematically, and different optimal controls are developed for them. The concept of shape optimization is introduced in this dissertation; the goal is to leverage nonlinear hydrodynamic forces which are dependent on the buoy shape. In this dissertation, shape optimization is carried out and its impact on energy extraction is investigated. In all the studies conducted in this dissertation the objective is set to maximize the harvested energy, in various wave climates. The development of a multi-resonant feedback controller is first introduced which targets both amplitude and phase through feedback that is constructed from individual frequency components that comes from the spectral of the measurements signal. Each individual frequency uses a Proportional-Derivative control to provide both optimal resistive and reactive elements. Two-body heaving pointer absorbers are also investigated. Power conversion is from the relative have oscillation between the two bodies. The oscillation is controlled on a wave-by-wave basis using near-optimal feed-forward control. Chapter 4 presents the dynamic formulation used to evaluate the near-optimal, wave-by-wave control forces in the time domain. Also examined are the reaction-frame geometries for their impact on overall power capture through favorable hydrodynamic inter-actions. Performance is evaluated in a range of wave conditions sampled over a year at a chosen site of deployment. It is found that control may be able to provide the required amounts of power to sustain instrument operation at the chosen site, but also that energy storage options be worth pursuing. Chapter 5 presents an optimization approach to design axisymmetric wave energy converters (WECs) based on a non-linear hydrodynamic model. The time domain nonlinear Froude-Krylov force can be computed for a complex buoy shape, by adopting analytical formulas of its basic shape components. The time domain Forude-Krylov force is decomposed into its dynamic and static components, and then contribute to the calculation of the excitation force and the hydro-static force. A non-linear control is assumed in the form of the combination of linear and non-linear damping terms. A variable size genetic algorithm (GA) optimization tool is developed to search for the optimal buoy shape along with the optimal control coefficients simultaneously. Chromosome of the GA tool is designed to improve computational efficiency and to leverage variable size genes to search for the optimal non-linear buoy shape. Different criteria of wave energy conversion can be implemented by the variable size GA tool. Simulation results presented in this thesis show that it is possible to find non-linear buoy shapes and non-linear controllers that take advantage of non-linear hydrodynamics to improve energy harvesting efficiency with out adding reactive terms to the system

Uncovering hidden geometry in Transformers via disentangling position and context

Transformers are widely used to extract semantic meanings from input tokens, yet they usually operate as black-box models. In this paper, we present a simple yet informative decomposition of hidden states (or embeddings) of trained transformers into interpretable components. For any layer, embedding vectors of input sequence samples are represented by a tensor $\boldsymbol{h} \in \mathbb{R}^{C \times T \times d}$. Given embedding vector $\boldsymbol{h}_{c,t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a sequence (or context) $c \le C$, extracting the mean effects yields the decomposition $$\boldsymbol{h}_{c,t} = \boldsymbol{\mu} + \mathbf{pos}_t + \mathbf{ctx}_c + \mathbf{resid}_{c,t}$$ where $\boldsymbol{\mu}$ is the global mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across contexts and across positions respectively, and $\mathbf{resid}_{c,t}$ is the residual vector. For popular transformer architectures and diverse text datasets, empirically we find pervasive mathematical structure: (1) $(\mathbf{pos}_t)_{t}$ forms a low-dimensional, continuous, and often spiral shape across layers, (2) $(\mathbf{ctx}_c)_c$ shows clear cluster structure that falls into context topics, and (3) $(\mathbf{pos}_t)_{t}$ and $(\mathbf{ctx}_c)_c$ are mutually nearly orthogonal. We argue that smoothness is pervasive and beneficial to transformers trained on languages, and our decomposition leads to improved model interpretability.Comment: 38 pages, 34 figure

Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.Comment: ECCV 201

Decoupled Knowledge Distillation

State-of-the-art distillation methods are mainly based on distilling deep features from intermediate layers, while the significance of logit distillation is greatly overlooked. To provide a novel viewpoint to study logit distillation, we reformulate the classical KD loss into two parts, i.e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD). We empirically investigate and prove the effects of the two parts: TCKD transfers knowledge concerning the "difficulty" of training samples, while NCKD is the prominent reason why logit distillation works. More importantly, we reveal that the classical KD loss is a coupled formulation, which (1) suppresses the effectiveness of NCKD and (2) limits the flexibility to balance these two parts. To address these issues, we present Decoupled Knowledge Distillation (DKD), enabling TCKD and NCKD to play their roles more efficiently and flexibly. Compared with complex feature-based methods, our DKD achieves comparable or even better results and has better training efficiency on CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object detection tasks. This paper proves the great potential of logit distillation, and we hope it will be helpful for future research. The code is available at https://github.com/megvii-research/mdistiller.Comment: Accepted by CVPR2022, fix typ

Multi-resonant feedback control of a single degree-of-freedom wave energy converter

A multi-resonant wide band controller decomposes the wave energy converter control problem into sub-problems; an independent single-frequency controller is used for each sub-problem. Thus, each sub-problem controller can be optimized independently. The feedback control enables actual time-domain realization of multi-frequency complex conjugate control. The feedback strategy requires only measurements of the buoy position and velocity. No knowledge of excitation force, wave measurements, nor wave prediction is needed. As an example, the feedback signal processing can be carried out using Fast Fourier Transform with Hanning windows and optimization of amplitudes and phases. Given that the output signal is decomposed into individual frequencies, the implementation of the control is very simple, yet generates energy similar to the complex conjugate control.https://digitalcommons.mtu.edu/patents/1148/thumbnail.jp

Real-time visualization of clustering and intracellular transport of gold nanoparticles by correlative imaging.

Mechanistic understanding of the endocytosis and intracellular trafficking of nanoparticles is essential for designing smart theranostic carriers. Physico-chemical properties, including size, clustering and surface chemistry of nanoparticles regulate their cellular uptake and transport. Significantly, even single nanoparticles could cluster intracellularly, yet their clustering state and subsequent trafficking are not well understood. Here, we used DNA-decorated gold (fPlas-gold) nanoparticles as a dually emissive fluorescent and plasmonic probe to examine their clustering states and intracellular transport. Evidence from correlative fluorescence and plasmonic imaging shows that endocytosis of fPlas-gold follows multiple pathways. In the early stages of endocytosis, fPlas-gold nanoparticles appear mostly as single particles and they cluster during the vesicular transport and maturation. The speed of encapsulated fPlas-gold transport was critically dependent on the size of clusters but not on the types of organelle such as endosomes and lysosomes. Our results provide key strategies for engineering theranostic nanocarriers for efficient health management

EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning

As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards the pair of traffic itself without considering the interrelationships among them, thus limiting the monitoring of complex IoT network attack events. Besides, anomalous traffic in real networks accounts for only a small fraction, which leads to a severe imbalance problem in the dataset that makes algorithmic learning and prediction extremely difficult. In this paper, we propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. In addition, we incorporate contrastive learning to discern the difference between normal and malicious traffic samples, facilitating the extraction of key features. Extensive experiments on two publicly available datasets demonstrate the superior intrusion detection performance of EG-ConMix compared to state-of-the-art methods. Remarkably, it exhibits significant advantages in terms of training speed and accuracy for large-scale graphs