Ground Vehicle Navigation with Depth Camera and Tracking Camera

The aim of this research is to provide autonomous navigation of a 4 wheel vehicle using commercial, off-the-shelf depth and tracking cameras. Some sensitive operations need accuracy within a few inches of navigation ability for indoor or outdoor scenarios where GPS signals are not available. Combination of the Visual Odometry (VO), Distance-Depth (D-D), and Object Detection data from the cameras can be used for accurate navigation and object avoidance. The Intel RealSense D435i, a depth camera, generates depth measurements and the relative position vector of an object. The Intel RealSense T265, a tracking camera, generates its own coordinate system and grabs coordinate goals. Both of them can generate Simultaneous Localization and Mapping (SLAM) data. The cameras share their data to provide a more robust capability. Combining the Intel cameras with a Pixhawk autopilot, it was demonstrated that the vehicle can follow a desired path and avoid objects along that path

Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimization

Conventional solvers are often computationally expensive for constrained optimization, particularly in large-scale and time-critical problems. While this leads to a growing interest in using neural networks (NNs) as fast optimal solution approximators, incorporating the constraints with NNs is challenging. In this regard, we propose deep Lagrange dual with equality embedding (DeepLDE), a framework that learns to find an optimal solution without using labels. To ensure feasible solutions, we embed equality constraints into the NNs and train the NNs using the primal-dual method to impose inequality constraints. Furthermore, we prove the convergence of DeepLDE and show that the primal-dual learning method alone cannot ensure equality constraints without the help of equality embedding. Simulation results on convex, non-convex, and AC optimal power flow (AC-OPF) problems show that the proposed DeepLDE achieves the smallest optimality gap among all the NN-based approaches while always ensuring feasible solutions. Furthermore, the computation time of the proposed method is about 5 to 250 times faster than DC3 and the conventional solvers in solving constrained convex, non-convex optimization, and/or AC-OPF.Comment: 11 pages, 5 figure

Flow-Induced Voltage Generation Over Monolayer Graphene in the Presence of Herringbone Grooves

While flow-induced voltage over a graphene layer has been reported, its origin remains unclear. In our previous study, we suggested different mechanisms for different experimental configurations: phonon dragging effect for the parallel alignment and an enhanced out-of-plane phonon mode for the perpendicular alignment (Appl. Phys. Lett. 102:063116, 2011). In order to further examine the origin of flow-induced voltage, we introduced a transverse flow component by integrating staggered herringbone grooves in the microchannel. We found that the flow-induced voltage decreased significantly in the presence of herringbone grooves in both parallel and perpendicular alignments. These results support our previous interpretation

Regularizing Towards Soft Equivariance Under Mixed Symmetries

Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However, if some or all of these symmetries are only approximate, which frequently happens in practice, these models may be suboptimal due to the architectural restrictions imposed on them. We tackle this issue of approximate symmetries in a setup where symmetries are mixed, i.e., they are symmetries of not single but multiple different types and the degree of approximation varies across these types. Instead of proposing a new architectural restriction as in most of the previous approaches, we present a regularizer-based method for building a model for a dataset with mixed approximate symmetries. The key component of our method is what we call equivariance regularizer for a given type of symmetries, which measures how much a model is equivariant with respect to the symmetries of the type. Our method is trained with these regularizers, one per each symmetry type, and the strength of the regularizers is automatically tuned during training, leading to the discovery of the approximation levels of some candidate symmetry types without explicit supervision. Using synthetic function approximation and motion forecasting tasks, we demonstrate that our method achieves better accuracy than prior approaches while discovering the approximate symmetry levels correctly.Comment: Proceedings of the International Conference on Machine Learning (ICML), 202

Learning Symmetrization for Equivariance with Orbit Distance Minimization

We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group. We build upon the proposals of Kim et al. (2023); Kaba et al. (2023) for symmetrization, and improve them by replacing their conversion of neural features into group representations, with an optimization whose loss intuitively measures the distance between group orbits. This change makes our approach applicable to a broader range of matrix groups, such as the Lorentz group O(1, 3), than these two proposals. We experimentally show our method's competitiveness on the SO(2) image classification task, and also its increased generality on the task with O(1, 3). Our implementation will be made accessible at https://github.com/tiendatnguyen-vision/Orbit-symmetrize.Comment: 16 pages, 1 figur

Analysis and Introduction of Effective Permeability with Additional Air-Gaps on Wireless Power Transfer Coils for Electric Vehicle based on SAE J2954 Recommended Practice

The wireless power transfer (WPT) method for electric vehicles (EVs) is becoming more popular, and to ensure the interoperability of WPT systems, the Society of Automotive Engineers (SAE) established the J2954 recommended practice (RP). It includes powering frequency, electrical parameters, specifications, testing procedures, and other contents for EV WPT. Specifically, it describes the ranges of self-inductances of the transmitting coil, the receiving coil, and coupling coefficient (k), as well as the impedance matching values of the WPT system. Following the electrical parameters listed in SAE J2954 RP is crucial to ensure the EV wireless charging system is interoperable. This paper introduces a method for adjusting the effective permeability of the ferrite blocks in the standard model, to tune the self-inductance of the coils as well as the coupling coefficient. To guarantee the given values of the self-inductance of the coil and coupling coefficient matched those in the standard, we slightly modified the air-gap between the ferrite tiles in a specific region. Based on this method, it was possible to successfully tune the self-inductance of the transmitting coil and receiving coil as well as the coupling coefficient. The proposed method was verified by simulation and experimental measurements

The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies

<p>Abstract</p> <p>Background</p> <p><it>Zymomonas mobilis </it>ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, <it>via </it>the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of <it>Z. mobilis </it>is required to systematically analyze <it>in silico </it>the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.</p> <p>Results</p> <p>The genome-scale metabolic model of <it>Z. mobilis </it>ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of <it>Z. mobilis </it>were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of <it>Z. mobilis </it>as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum <it>in silico </it>yields of ethanol for <it>Z. mobilis </it>and <it>Escherichia coli </it>were compared and analyzed. Furthermore, the substrate utilization range of <it>Z. mobilis </it>was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow <it>Z. mobilis </it>to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of <it>Z. mobilis</it>.</p> <p>Conclusion</p> <p>The genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of <it>Z. mobilis </it>under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of <it>Z. mobilis </it>metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.</p

Spin-coated ultrathin multilayers and their micropatterning using microfluidic channels

A new method is introduced to build up organic/organic multilayer films composed of cationic poly(allylamine hydrochloride) (PAH) and negatively charged poly (sodium 4-styrenesulfonate) (PSS) using the spinning process. The adsorption process is governed by both the viscous force induced by fast solvent elimination and the electrostatic interaction between oppositely charged species. On the other hand, the centrifugal and air shear forces applied by the spinning process significantly enhances desorption of weakly bound polyelectrolyte chains and also induce the planarization of the adsorbed polyelectrolyte layer. The film thickness per bilayer adsorbed by the conventional dipping process and the spinning process was found to be about 4 Å and 24 Å, respectively. The surface of the multilayer films prepared with the spinning process is quite homogeneous and smooth. Also, a new approach to create multilayer ultrathin films with welldefined micropatterns in a short process time is introduced. To achieve such micropatterns with high line resolution in organic multilayer films, microfluidic channels were combined with the convective self-assembly process employing both hydrogen bonding and electrostatic intermolecular interactions. The channels were initially filled with polymer solution by capillary pressure and the residual solution was then removed by the spinning process.This work was financially supported by the National Research Laboratory Program (Grant M1-0104-00-0191) and funded in part by the Ministry of Education through the Brain Korea 21 Program at Seoul National University