Electrically pumped semiconductor laser with low spatial coherence and directional emission

We design and fabricate an on-chip laser source that produces a directional beam with low spatial coherence. The lasing modes are based on the axial orbit in a stable cavity and have good directionality. To reduce the spatial coherence of emission, the number of transverse lasing modes is maximized by fine-tuning the cavity geometry. Decoherence is reached in a few nanoseconds. Such rapid decoherence will facilitate applications in ultrafast speckle-free full-field imaging

Suppressing spatio-temporal lasing instabilities with wave-chaotic microcavities

Spatio-temporal instabilities are widespread phenomena resulting from complexity and nonlinearity. In broad-area edge-emitting semiconductor lasers, the nonlinear interactions of multiple spatial modes with the active medium can result in filamentation and spatio-temporal chaos. These instabilities degrade the laser performance and are extremely challenging to control. We demonstrate a powerful approach to suppress spatio-temporal instabilities using wave-chaotic or disordered cavities. The interference of many propagating waves with random phases in such cavities disrupts the formation of self-organized structures like filaments, resulting in stable lasing dynamics. Our method provides a general and robust scheme to prevent the formation and growth of nonlinear instabilities for a large variety of high-power lasers

Estimates on compressed neural networks regression

When the neural element number nn of neural networks is larger than the sample size mm, the overfitting problem arises since there are more parameters than actual data (more variable than constraints). In order to overcome the overfitting problem, we propose to reduce the number of neural elements by using compressed projection AA which does not need to satisfy the condition of Restricted Isometric Property (RIP). By applying probability inequalities and approximation properties of the feedforward neural networks (FNNs), we prove that solving the FNNs regression learning algorithm in the compressed domain instead of the original domain reduces the sample error at the price of an increased (but controlled) approximation error, where the covering number theory is used to estimate the excess error, and an upper bound of the excess error is given

Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target

Electric vehicles (EVs) have become popular in daily life, which influences carbon dioxide emissions and reshapes the curves of community loads. It is crucial to study efficient carbon emission scheduling algorithms to lessen the influence of EVs’ charging demand on carbon dioxide emissions and reduce the carbon emission cost for EVs coming to the community. We study an electric vehicle (EV) carbon emission scheduling problem to shave the peak community load and reduce the carbon emission cost when we do not know future EV data. First, we investigate an offline carbon emission scheduling problem to minimize the carbon emission cost of the community by predicting future data with regard to incoming EVs. Then, we study the online carbon emission problem and propose an online carbon emission algorithm based on a heuristic rolling algorithm. Furthermore, we propose a reinforcement learning smart carbon emission algorithm (RLSCA) to achieve the dispatching plan of the charging carbon emission of EVs. Last but not least, simulation results show that our proposed algorithm can reduce the carbon emission cost by 21.26%, 16.60%, and 8.72% compared with three benchmark algorithms

Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target

Electric vehicles (EVs) have become popular in daily life, which influences carbon dioxide emissions and reshapes the curves of community loads. It is crucial to study efficient carbon emission scheduling algorithms to lessen the influence of EVs’ charging demand on carbon dioxide emissions and reduce the carbon emission cost for EVs coming to the community. We study an electric vehicle (EV) carbon emission scheduling problem to shave the peak community load and reduce the carbon emission cost when we do not know future EV data. First, we investigate an offline carbon emission scheduling problem to minimize the carbon emission cost of the community by predicting future data with regard to incoming EVs. Then, we study the online carbon emission problem and propose an online carbon emission algorithm based on a heuristic rolling algorithm. Furthermore, we propose a reinforcement learning smart carbon emission algorithm (RLSCA) to achieve the dispatching plan of the charging carbon emission of EVs. Last but not least, simulation results show that our proposed algorithm can reduce the carbon emission cost by 21.26%, 16.60%, and 8.72% compared with three benchmark algorithms

A Practical Approach to Testing Calibration Strategies

A calibration strategy tries to match target moments using a model’s parameters. We propose tests for determining whether this is possible. The tests use moments at random parameter draws to assess whether the target moments are similar to the computed ones (evidence of existence) or appear to be outliers (evidence of non-existence). Our experiments show the tests are effective at detecting both existence and non-existence in a non-linear model. Multiple calibration strategies can be quickly tested using just one set of simulated data. Applying our approach to indirect inference allows for the testing of many auxiliary model specifications simultaneously. Code is provided

Spatial structure of lasing modes in wave-chaotic semiconductor microcavities

International audienc

Spatiotemporal lasing dynamics in a Limaçon-shaped microcavity

International audienceLimaçon-shaped microdisk lasers are promising on-chip light sources with low lasing threshold and unidirectional output. We conduct an experimental study on the lasing dynamics of Limaçon-shaped semiconductor microcavities. The edge emission exhibits intensity fluctuations over a wide range of spatial and temporal scales. They result from multiple dynamic processes with different origins and occur on different spatiotemporal scales. The dominant process is an alternate oscillation between two output beams with a period as short as a few nanoseconds