Hierarchically Clustered Representation Learning

The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically-clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Compared with a few prior works, HCRL firstly attempts to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape

Frequency-Based Decentralized Conservation Voltage Reduction Incorporated Into Voltage-Current Droop Control for an Inverter-Based Islanded Microgrid

Conservation voltage reduction (CVR) aims to decrease load demands by regulating bus voltages at a low level. This paper proposes a new strategy for decentralized CVR (DCVR), incorporated into the current-based droop control of inverter-interfaced distributed energy resources (IDERs), to improve the operational reliability of an islanded microgrid. An $I_{dq}$ controller is developed as an outer feedback controller for each IDER, consisting of $I_{d}$ $V$ controllers for the DCVR and $I_{d}$ $\omega$ and $I_{q}$ $V$ controllers for power sharing. In particular, the $I_{d}$ $V$ controllers adjust the output voltages of the IDERs in proportion to the frequency variation determined by the $I_{d}$ $\omega$ controllers. This enables the output voltages to be reduced by the same amount, without communication between the IDERs. The $I_{q}$ $V$ controllers are responsible for reactive power sharing by adjusting the voltages while taking into account the $I_{d}$ $V$ controllers. Small-signal analysis is used to verify the performance of the proposed DCVR with variation in the $I_{d}$ $\omega$ and $I_{q}$ $V$ droop gains. Case studies are also carried out to demonstrate that the DCVR effectively mitigates an increase in the load demand, improving the operational reliability, under various load conditions determined by power factors and load compositions.11Ysciescopu

Critical Velocity for Vortex Shedding in a Bose-Einstein Condensate

We present measurements of the critical velocity for vortex shedding in a highly oblate Bose-Einstein condensate with a moving repulsive Gaussian laser beam. As a function of the barrier height $V_0$, the critical velocity $v_c$ shows a dip structure having a minimum at $V_0 \approx \mu$, where $\mu$ is the chemical potential of the condensate. At fixed $V_0\approx 7\mu$, we observe that the ratio of $v_c$ to the speed of sound $c_s$ monotonically increases for decreasing $\sigma/\xi$, where $\sigma$ is the beam width and $\xi$ is the condensate healing length. The measured upper bound for $v_c/c_s$ is about 0.4, which is in good agreement with theoretical predictions for a two-dimensional superflow past a circular cylinder. We explain our results with the density reduction effect of the soft boundary of the Gaussian obstacle, based on the local Landau criterion for superfluidity.Comment: 5 pages, 4 figure

Adversarial Dropout for Supervised and Semi-supervised Learning

Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.Comment: submitted to AAAI-1

Relaxation of superfluid turbulence in highly oblate Bose-Einstein condensates

We investigate thermal relaxation of superfluid turbulence in a highly oblate Bose-Einstein condensate. We generate turbulent flow in the condensate by sweeping the center region of the condensate with a repulsive optical potential. The turbulent condensate shows a spatially disordered distribution of quantized vortices and the vortex number of the condensate exhibits nonexponential decay behavior which we attribute to the vortex pair annihilation. The vortex-antivortex collisions in the condensate are identified with crescent-shaped, coalesced vortex cores. We observe that the nonexponential decay of the vortex number is quantitatively well described by a rate equation consisting of one-body and two-body decay terms. In our measurement, we find that the local two-body decay rate is closely proportional to $T^2/\mu$, where $T$ is the temperature and $\mu$ is the chemical potential.Comment: 7 pages, 9 figure

Fabrication of a Silicon Nanowire on a Bulk Substrate by Use of a Plasma Etching and Total Ionizing Dose Effects on a Gate-All-Around Field-Effect Transistor

The gate all around transistor is investigated through experiment. The suspended silicon nanowire for the next generation is fabricated on bulk substrate by plasma etching method. The scallop pattern generated by Bosch process is utilized to form a floating silicon nanowire. By combining anisotropic and istropic silicon etch process, the shape of nanowire is accurately controlled. From the suspended nanowire, the gate all around transistor is demonstrated. As the silicon nanowire is fully surrounded by the gate, the device shows excellent electrostatic characteristics

One Time Programmable Antifuse Memory Based on Bulk Junctionless Transistor

One time programmable (OTP) antifuse base memory is demonstrated based on a bulk junctionless gate-all-around (GAA) nanowire transistor technology. The presented memory consists of a single transistor (1T) footprint without any process modification. The source/drain (S/D) and gate respectively become bit line and word line where the antifuse is formed by oxide breakdown across the gate and the channel. The channel is connected directly to the bit line due to junctionless S/D and inherently isolated from the neighboring cell by the GAA channel. Therefore, an array of 1T antifuse OTP can be a candidate for the sub-5-nanometer technology node

Korea’s technical assistance for better governance

노트 : - Paper for International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperation - Organized by Asia Foundation October 17-18, 2011 Seoul, Korea 행사명 : International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperatio

Optimal Voltage Control Using an Equivalent Model of a Low-Voltage Network Accommodating Inverter-Interfaced Distributed Generators

The penetration of inverter-based distributed generators (DGs), which can control their reactive power outputs, has increased for low-voltage (LV) systems. The power outputs of DGs affect the voltage and power flow of both LV and medium-voltage (MV) systems that are connected to the LV system. Therefore, the effects of DGs should be considered in the volt/var optimization (VVO) problem of LV and MV systems. However, it is inefficient to utilize a detailed LV system model in the VVO problem because the size of the VVO problem is increased owing to the detailed LV system models. Therefore, in order to formulate and solve the VVO problem in an efficient way, in this paper, a new equivalent model for an LV system including inverter-based DGs is proposed. The proposed model is developed based on an analytical approach rather than a heuristic-fitting one, and it therefore enables the VVO problem to be solved using a deterministic algorithm (e.g., interior point method). In addition, a method to utilize the proposed model for the VVO problem is presented. In the case study, the results verify that the computational burden to solve the VVO problem is significantly reduced without loss of accuracy by the proposed model.11Ysciescopu