Generalized lock-in amplifier for precision measurement of high frequency signals

We herein formulate the concept of a generalized lock-in amplifier for the precision measurement of high frequency signals based on digital cavities. Accurate measurement of signals higher than 200 MHz using the generalized lock-in is demonstrated. The technique is compared with a traditional lock-in and its advantages and limitations are discussed. We also briefly point out how the generalized lock-in can be used for precision measurement of giga-hertz signals by using parallel processing of the digitized signals

Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit

Geometric quantum manipulation and Landau-Zener interferometry have been separately explored in many quantum systems. In this Letter, we combine these two approaches to study the dynamics of a superconducting phase qubit. We experimentally demonstrate Landau-Zener interferometry based on the pure geometric phases in this solid-state qubit. We observe the interference caused by a pure geometric phase accumulated in the evolution between two consecutive Landau-Zener transitions, while the dynamical phase is canceled out by a spin-echo pulse. The full controllability of the qubit state as a function of the intrinsically robust geometric phase provides a promising approach for quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia

Optimal Inter-area Oscillation Damping Control: A Transfer Deep Reinforcement Learning Approach with Switching Control Strategy

Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems. The recent breakthroughs in deep learning and the broad deployment of phasor measurement units (PMU) promote the development of datadriven IAO damping controllers. In this paper, the damping control of IAOs is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL) approach. The proposed approach optimizes the eigenvalue distribution of the system, which determines the IAO modes in nature. The eigenvalues are evaluated by the data-driven method called dynamic mode decomposition. For a given power system, only a subset of generators selected by participation factors needs to be controlled, alleviating the control and computing burdens. A Switching Control Strategy (SCS) is introduced to improve the transient response of IAOs. Numerical simulations of the IEEE-39 New England power grid model validate the effectiveness and advanced performance of the proposed approach as well as its robustness against communication delays. In addition, we demonstrate the transfer ability of the DRL model trained on the linearized power grid model to provide effective IAO damping control in the non-linear power grid model environment

Planner-Oriented Soil Evaluation in China with TUSEC (Technique for Soil Evaluation and Categorization for Natural and Anthropogenic Soils)

Purpose China is a fast developing country. In order to more rational use of soil resources, it is necessary to evaluate the functions of soils on the regional level, which can provide a reference to rational land-use planning. Method In this paper, by using the method of TUSEC (Technique for Soil Evaluation and Categorization for Natural and Anthropogenic Soils), with some modifications of the parameters according to the local situation, the soil resource in suburb of Zhengzhou City in China has been evaluated for different functions. Results The evaluation results show that the function of soil as component of water and nutrient cycles is in high levels while the function of wheat production is in the medium level in most of the area. Opposite to the function of transformation, the function of soils as filter and buffer for heavy metals in the southwest is relatively higher than the northeast. Conclusions As urbanization is inevitable, the soil functions should be considered for a sustainable land use

Creative Commons Quiz/Lecture notes/Lecture slides(group 22)

The resource set of info2009 coursework 2 is produced by group22. it contains: 1. poster 2. internet link of a set of multiple questions 3. a pdf file of a set of multiple questions 4. reference list 5. lecture slides 6. lecture notes ps: Edward Payne ([email protected]) has not contributed to any part of the activities

Rethinking Data Augmentation in Knowledge Distillation for Object Detection

Knowledge distillation (KD) has shown its effectiveness for object detection, where it trains a compact object detector under the supervision of both AI knowledge (teacher detector) and human knowledge (human expert). However, existing studies treat the AI knowledge and human knowledge consistently and adopt a uniform data augmentation strategy during learning, which would lead to the biased learning of multi-scale objects and insufficient learning for the teacher detector causing unsatisfactory distillation performance. To tackle these problems, we propose the sample-specific data augmentation and adversarial feature augmentation. Firstly, to mitigate the impact incurred by multi-scale objects, we propose an adaptive data augmentation based on our observations from the Fourier perspective. Secondly, we propose a feature augmentation method based on adversarial examples for better mimicking AI knowledge to make up for the insufficient information mining of the teacher detector. Furthermore, our proposed method is unified and easily extended to other KD methods. Extensive experiments demonstrate the effectiveness of our framework and improve the performance of state-of-the-art methods in one-stage and two-stage detectors, bringing at most 0.5 mAP gains.Comment: 8 pages, 5 figure