Air transparent soundproof window

A soundproof window or wall which is transparent to airflow is presented. The design is based on two wave theories of diffraction and acoustic metamaterials. It consists of a three-dimensional array of strong diffraction-type resonators with many holes centered at each individual resonator. The acoustic performance levels of two soundproof windows with air holes of $20mm$ and $50mm$ diameters were measured. Sound waves of 80dB in the frequency range of $400 - 5,000Hz$ were applied to the windows. It was observed that the sound level was reduced by about $30 - 35$dB in the above frequency range with the $20mm$ window and by about $20 - 35$dB in the frequency range of $700 - 2,200Hz$ with the $50mm$ window. It is an extraordinary acoustic anti-transmission. The geometric factors which produced the effective negative modulus were obtained.Comment: 4 pages, 6 figures, 1 tabl

Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on Applications of Artificial Intelligence in Wireless Communication

A Suspended Nanogap Formed by Field-Induced Atomically Sharp Tips

A sub-nanometer scale suspended gap (nanogap) defined by electric field-induced atomically sharp metallic tips is presented. A strong local electric field (\u3e109 V=m) across micro/nanomachined tips facing each other causes the metal ion migration in the form of dendrite-like growth at the cathode. The nanogap is fully isolated from the substrate eliminating growth mechanisms that involve substrate interactions. The proposed mechanism of ion transportation is verified using real-time imaging of the metal ion transportation using an in situ biasing in transmission electron microscope (TEM). The configuration of the micro/nanomachined suspended tips allows nanostructure growth of a wide variety of materials including metals, metal-oxides, and polymers. VC 2012 American Institute of Physics

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.Comment: to appear in IEEE J. Sel. Areas Commu

Learning Autonomy in Management of Wireless Random Networks

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distributed universal optimization policy robust to a random topology of the wireless network, which has not been properly addressed by conventional deep neural networks (DNNs) with rigid structural configurations. We develop a flexible DNN formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology. A key enabler of this approach is an iterative message-sharing strategy through arbitrarily connected backhaul links. The DMPNN provides a convergent solution for iterative coordination by learning numerous random backhaul interactions. The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.Comment: to appear in IEEE TW

Test Platform Development of Vessel???s Power Management System Using Hardware- in-the-Loop-Simulation Technique

A PMS (Power Management System) controls vessel's power systems to improve the system efficiency and to protect a blackout condition. The PMS should be developed with considering the type and the capacity of the vessel???s power system. It is necessary to test the PMS functions developed for vessel???s safe operations under various sailing situations. Therefore, the function tests in cooperation with practical power systems are required in the PMS development. In this paper, a hardware-in-the-loop (HIL) simulator is developed for the purposes of the PMS function tests. The HIL simulator can be more cost-effective, more time-saved, easier to reproduce, and safer beyond the normal operating range than conventional off-line simulators, especially at early stages in development processes or during fault tests. Vessel's power system model is developed by using a MATLAB/SIMULINK software and by communicating between an OPAL-RT???s OP5600 simulator. The PMS uses a Modbus communication protocol implemented using LabVIEW software. Representative tests of the PMS functions are performed to verify the validity of the proposed HIL-based test platform

DPRS transformer - Dynamic pressure resistant system - Part I

In general, a transformer is designed and manufactured to operate under normal conditions. However, unexpected fault events occur due to various reasons in real-life substations. When such events do occur, an electric arc inside a transformer vaporizes the insulating oil, leading to a generation of very high expansion pressure. Once this pressure exceeds the designed threshold, the tank is then compromised, and oil starts to leak, becoming a potential cause of fire or explosion. DPRS (Dynamic Pressure Resistant System) transformer has been developed to cope with such unexpected events. In general, a PRD (Pressure Relief Device) is installed on a transformer to stabilize the pressure inside the tank. However, it requires a certain amount of time for this device to operate. DPRS transformer is designed to withstand the immediate pressure increase without severely damaging the tank (severe enough to cause an oil leak) until the PRD starts operating. Although not as much as to cause a leak, the tank will still be deformed as a result of the pressure increase. Then, insulating oil expanded by the arc is emitted safely through a designated path as the PRD starts to operate. DPRS transformer does not require additional equipment to prevent damage to the tank and is also capable of preventing fire while maintaining a similar configuration to common transformers. Due to these merits, the global demand for DPRS transformers is steadily increasing. In this article, the DPRS transformer tank design procedure and tank deformation prediction technology are presented. Additionally, a brief introduction to the explosion-proof performance verification test is addressed

Stability of hydrogenation states of graphene and conditions for hydrogen spillover

The hydrogen spillover mechanism has been discussed in the field of hydrogen storage and is believed to have particular advantage over the storage as metal or chemical hydrides. We investigate conditions for practicality realizing the hydrogen spillover mechanism onto carbon surfaces, using first-principles methods. Our results show that contrary to common belief, types of hydrogenation configurations of graphene (the aggregated all-paired configurations) can satisfy the thermodynamic requirement for room-temperature hydrogen storage. However, the peculiarity of the paired adsorption modes gives rise to a large kinetic barrier against hydrogen migration and desorption. It means that an extremely high pressure is required to induce the migration-derived hydrogenation. However, if mobile catalytic particles are present inside the graphitic interstitials, hydrogen migration channels can open and the spillover phenomena can be realized. We suggest a molecular model for such a mobile catalyst which can exchange hydrogen atoms with the wall of graphene.open151

Dynamical mean-field theory of Hubbard-Holstein model at half-filling: Zero temperature metal-insulator and insulator-insulator transitions

We study the Hubbard-Holstein model, which includes both the electron-electron and electron-phonon interactions characterized by $U$ and $g$, respectively, employing the dynamical mean-field theory combined with Wilson's numerical renormalization group technique. A zero temperature phase diagram of metal-insulator and insulator-insulator transitions at half-filling is mapped out which exhibits the interplay between $U$ and $g$. As $U$ ($g$) is increased, a metal to Mott-Hubbard insulator (bipolaron insulator) transition occurs, and the two insulating states are distinct and can not be adiabatically connected. The nature of and transitions between the three states are discussed.Comment: 5 pages, 4 figures. Submitted to Physical Review Letter