Performance Analysis of a Four-Switch Three-Phase Grid-Side Converter with Modulation Simplification in a Doubly-Fed Induction Generator-Based Wind Turbine (DFIG-WT) with Different External Disturbances

This paper investigates the performance of a fault-tolerant four-switch three-phase (FSTP) grid-side converter (GSC) in a doubly-fed induction generator-based wind turbine (DFIG-WT). The space vector pulse width modulation (SVPWM) technique is simplified and unified duty ratios are used for controlling the FSTP GSC. Steady DC-bus voltage, sinusoidal three-phase grid currents and unity power factor are obtained. In addition, the balance of capacitor voltages is accomplished based on the analysis of current flows at the midpoint of DC bus in different operational modes. Besides, external disturbances such as fluctuating wind speed and grid voltage sag are considered to test its fault-tolerant ability. Furthermore, the effects of fluctuating wind speed on the performance of DFIG-WT system are explained according to an approximate expression of the turbine torque. The performance of the proposed FSTP GSC is simulated in Matlab/Simulink 2016a based on a detailed 1.5 MW DFIG-WT Simulink model. Experiments are carried out on a 2 kW platform by using a discrete signal processor (DSP) TMS320F28335 controller to validate the reliability of DFIG-WT for the cases with step change of the stator active power and grid voltage sag, respectively

Enabling Work-conserving Bandwidth Guarantees for Multi-tenant Datacenters via Dynamic Tenant-Queue Binding

Today's cloud networks are shared among many tenants. Bandwidth guarantees and work conservation are two key properties to ensure predictable performance for tenant applications and high network utilization for providers. Despite significant efforts, very little prior work can really achieve both properties simultaneously even some of them claimed so. In this paper, we present QShare, an in-network based solution to achieve bandwidth guarantees and work conservation simultaneously. QShare leverages weighted fair queuing on commodity switches to slice network bandwidth for tenants, and solves the challenge of queue scarcity through balanced tenant placement and dynamic tenant-queue binding. QShare is readily implementable with existing switching chips. We have implemented a QShare prototype and evaluated it via both testbed experiments and simulations. Our results show that QShare ensures bandwidth guarantees while driving network utilization to over 91% even under unpredictable traffic demands.Comment: The initial work is published in IEEE INFOCOM 201

Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI

Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods

Melanoma-associated retinopathy

AbstractA 63-year-old Taiwanese man with a history of cutaneous melanoma presented with a rapid onset of bilateral shimmering light and blurred vision. A fundoscopic examination was normal. However, visual field examination indicated generalized depression in both eyes. Scotopic rod-specific electroretinography (ERG) was undetectable and scotopic maximal combined-cone and rod-specific ERG showed the characteristics of negative ERG (a normal a-wave and a diminished b-wave, with the b-wave smaller than the a-wave), indicating dysfunction of the bipolar cells. Melanoma-associated retinopathy (MAR) was suspected and a systemic work-up gave a diagnosis of metastatic melanoma. This case shows the typical presentation of MAR. Greater awareness of MAR in patients with unexplained visual loss may help to identify an occult focus of metastatic melanoma

Linked optical and gene expression profiling of single cells at high-throughput.

Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis