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1. Frequency Response Improvement of PMSG Wind Turbines Using a Washout Filter

   https://doi.org/10.3390/en13184797  

   Shabestari, Parisa M.; Mehrizi-Sani, Ali (September 2020, Energies)

   null (Ed.)

   High integration of renewable energy resources, such as wind turbines, to the power grid decreases the power system inertia. To improve the frequency response of a low-inertia system, virtual inertia approach can be used. This letter proposes a control method to decrease the frequency transients and restore frequency to its nominal value. A wind turbine usually works based on maximum power point tracking (MPPT) curves to achieve the maximum power. In this letter, the proposed controller uses a non-MPPT method to leave power for frequency regulation during transients. Moreover, it uses a washout filter-based method to remove the steady-state error in the frequency. Simulation results in the PSCAD environment validate the improved performance of the proposed method during load changes by comparing it with the MPPT and non-MPPT methods. 

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2. Sparse Iterative Beamforming Using Spherical Microphone Arrays for Low-Latency Direction of Arrival Estimation in Reverberant Environments

   https://doi.org/https://doi.org/10.17743/jaes.2021.0057  

   Mathews, Jonathan; Braasch, Jonas (December 2021, Journal of the Audio Engineering Society)

   Acoustic direction of arrival estimation methods allows positional information about sound sources to be transmitted over a network using minimal bandwidth. For these purposes,methods that prioritize low computational overhead and consistent accuracy under non-ideal conditions are preferred. The estimation method introduced in this paper uses a set of steered beams to estimate directional energy at sparsely distributed orientations around a spherical microphone array. By iteratively adjusting beam orientations based on the orientation of maximum energy, an accurate orientation estimate of a sound source may be produced with minimal computational cost. Incorporating conditions based on temporal smoothing and diffuse energy estimation further refines this process. Testing under simulated conditions indicates favorable accuracy under reverberation and source discrimination when compared with several other contemporary localization methods. Outcomes include an average localization error of less than 10◦ under 2 s of reverberation time (T60) and the potential to separate up to four sound sources under the same conditions. Results from testing in a laboratory environment demonstrate potential for integration into real-time frameworks. 

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3. Grasping Objects Big and Small: Human Heuristics Relating Grasp-Type and Object Size

   https://doi.org/10.1109/icra.2018.8460860  

   Kothari, Ammar; Morrow, John; Thrasher, Victoria; Engle, Kadon; Balasubramanian, Ravi; Grimm, Cindy (May 2018, IEEE International Conference on Robotics and Automation)

   This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size. 

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4. A Single Stage Boost Converter for Body Heat Energy Harvesting with Maximum Power Point Tracking and Output Voltage Regulation

   https://doi.org/10.1109/ISCAS.2019.8702772  

   Brogan, Quinn; Ha, Dong Sam (May 2019, International Symposium on Circuits and Systems (ISCAS))

   The proposed circuit aims to harvest energy from body heat, in which the thermal gradient is only a few degrees. The boost converter operating in a burst mode offers a high conversion ratio while minimizing power loss. Maximum power point tracking based on the fractional open circuit voltage method ensures that the proposed circuit can be applied for a variety of thermoelectric generators (TEGs) and TEG setups. The proposed circuit is designed and laid out in CMOS 0.25 μm technology. Post layout simulation results indicate the converter is able to boost input voltages as low as 50 mV to the regulated output of 3 V, while achieving peak efficiency of 81%. 

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5. Boosting the power of kernel two-sample tests

   https://doi.org/10.1093/biomet/asae048  

   Chatterjee, A; Bhattacharya, B B (January 2025, Biometrika)

   The kernel two-sample test based on the maximum mean discrepancy is one of the most popular methods for detecting differences between two distributions over general metric spaces. In this paper we propose a method to boost the power of the kernel test by combining maximum mean discrepancy estimates over multiple kernels using their Mahalanobis distance. We derive the asymptotic null distribution of the proposed test statistic and use a multiplier bootstrap approach to efficiently compute the rejection region. The resulting test is universally consistent and, since it is obtained by aggregating over a collection of kernels/bandwidths, is more powerful in detecting a wide range of alternatives in finite samples. We also derive the distribution of the test statistic for both fixed and local contiguous alternatives. The latter, in particular, implies that the proposed test is statistically efficient, that is, it has nontrivial asymptotic (Pitman) efficiency. The consistency properties of the Mahalanobis and other natural aggregation methods are also explored when the number of kernels is allowed to grow with the sample size. Extensive numerical experiments are performed on both synthetic and real-world datasets to illustrate the efficacy of the proposed method over single-kernel tests. The computational complexity of the proposed method is also studied, both theoretically and in simulations. Our asymptotic results rely on deriving the joint distribution of the maximum mean discrepancy estimates using the framework of multiple stochastic integrals, which is more broadly useful, specifically, in understanding the efficiency properties of recently proposed adaptive maximum mean discrepancy tests based on kernel aggregation and also in developing more computationally efficient, linear-time tests that combine multiple kernels. We conclude with an application of the Mahalanobis aggregation method for kernels with diverging scaling parameters. 

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