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Vertical displacement event (VDE) is one of the most important events related to disruptions. A real-time disruption prediction tool for VDE is presented in this article. Vertical growth rate is an essential parameter to describe vertical instability, which can be obtained based on the rigid plasma response model. A real-time vertical growth rate calculation system is built, which utilizes graphics process unit (GPU) parallel computation and reflective memory (RFM) boards communication with plasma control system (PCS). Based on real-time vertical growth rate calculation system, an alarm algorithm for detecting vertical instability is integrated into experimental advanced superconducting tokamak (EAST) PCS. It is designed to provide the capabilities of a switching control method and to trigger a warning signal in PCS when uncontrollable vertical instability is detected. Such a disruption prediction tool has been preliminarily applied in the 2019 EAST spring experiment. [ABSTRACT FROM AUTHOR]

For the purpose of keeping a plasma in steady state and avoiding instabilities, the experimental advanced superconducting tokamak (EAST) plasma control system (PCS) needs more diagnostic data produced by the EAST distributed subsystems to perform its complex control algorithms. To solve the transmission problem of all these data, a real-time communication system based on the messaging, real time, and grid real time operating system and reflective memory (RFM) network is designed. It is expected to enable the data acquisition (DAQ) systems of various EAST subsystems to be able to transfer the diagnostic data to the PCS in real time through the RFM network as the data are acquired. Then, the PCS can use these available data to carry out the necessary calculations according to the control algorithms and send the commands to the subsystems in every control cycle. To reach this goal, a small real-time communication system, adapted from the prototype of the EAST vacuum DAQ system (sampling rate of 10 KHz), was built for a performance test. In this test, the vacuum DAQ system transferred the acquired data to the PCS within the control cycle of $100~\mu \text{s}$ , which tentatively meets the abovementioned requirements. The test was performed for 1000 s to get a stable and reliable result, which is described in detail in this paper as well as plans for future work. [ABSTRACT FROM AUTHOR]

To accomplish experimental advanced superconducting tokamak (EAST) physical targets, the plasma control system (PCS), adapted from DIII-D PCS and deployed on EAST in 2005, keeps in continuous development. Some new control abilities for steady-state operation have been achieved. To avoid the integrator linear drift in long pulse discharge, the linear rate for each integrator channel is precalculated and used to decrease the effect of the acquired raw data during shots. Another strategy applied for long pulse operation is real-time data archiving using data segment technology of MDSplus, which provides the possibility to save all data in segments without increasing the computer memory or reducing the saving frequency. For plasma high-performance and noninductive operations, the plasma beta and loop voltage control was implemented in PCS. Using low hybrid wave, the control algorithm was successfully verified in 2016 EAST campaign. Besides, another two control algorithms are integrated to reduce the divertor heat flux. One is radiation power control, which is successfully feedback controlled by using divertor inert gas puffing and mid-plane supersonic molecular beam injection. The other is quasi-snowflake shape control using PEFIT/ISOFLUX, which shows significant heat load reduction to divertor target according to the experimental result. The present EAST PCS has become a huge system capable of long pulse, high-performance advanced plasma control operation, which is ready to demonstrate ITER-like control contents. [ABSTRACT FROM AUTHOR]

This paper briefly outlines the current status of CODAC system of the EAST tokamak. Integrated central system has been working for synchronizing all the subsystems and responsible for the integral safety protection by interlock. Data acquisition system has been established for the discharge with duration up to 100 seconds. Various data visualization tools such as EASTVIEWER to view the flux surfaces, RTSCOPE to view plasma boundary and CCD image in real-time, WEBSCOPE to view the diagnostic data via a web browser and EASTVOD to view and search plasma discharge image at different time, haven developed for EAST operation. Plasma control system is inherited from DIII-D, and has been matured to a stage to feedback control the EAST plasma shape, density and current in real time. [ABSTRACT FROM AUTHOR]

Recent commissioning of two major fully superconducting (SC)-shaped tokamaks, Experimental Advanced Superconducting Tokamak (EAST) and Korean Superconducting Tokamak Advanced Research (KSTAR), represents a significant advance in magnetic fusion research. The key to commissioning success in these complex and unique tokamaks was as follows: 1) use of a robust, flexible plasma control system (PCS) based on the validated DIII-D design; 2) use of the TokSys design and modeling environment, which is tightly coupled with the DIII-D PCS architecture for first-plasma scenario development and plasma diagnosis; and 3) collaborations with experienced internationally recognized teams of tokamak operations and control experts. We provide an overview of the generic modeling environment and plasma control tools developed and validated within the DIII-D experimental program and applied through an international collaborative program to successfully address the unique constraints associated with the startup of these next-generation tokamaks. The unique characteristics of each tokamak and the machine constraints that must be included in device modeling and simulation, such as SC coil current slew rate limits and the presence of nonlinear magnetic materials, are discussed, along with commissioning and initial operational results. Lessons learned from the startup experience in these devices are summarized, with special emphasis on ramifications for International Thermonuclear Experimental Reactor (ITER). [ABSTRACT FROM AUTHOR]

A synthesis technique for the optimization of the element excitations of conformal phased array with improved NSGA-H (INSGA-ll) algorithm is introduced in this communication. Firstly, the patterns of all the elements are measured in a microwave chamber when they are terminated by their own characteristic impedances. As these patterns called as active patterns are experimentally obtained, they have already included the mutual coupling information of the environment. Then the pattern of the conformal antenna array can be obtained by a superposition of all these active patterns with various weights. And because the pattern of each element has been pre-stored, one can avoid the full-wave numerous computations in the optimization process. Using the optimization algorithm, the weights including amplitude and phase of each element can be optimized quickly. Therefore this treatment has the advantages of less computation and high accuracy. In order to further accelerate the optimization, INSGA-II algorithm is applied. By using this algorithm, the entire optimal solutions, i.e., the optimal solutions of the main beam of the conformal phased array directing to different angles, can be solved at once,, The idea is demonstrated through the designs of two conformal antenna arrays mounted on hemisphere and cylinder-hemisphere platforms, respectively. Comparisons between the experimental and simulated results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Provides information on a study which developed digital bearing estimators to replace the analog signal processing portion of the Adcock-Butler matrix antenna array direction finder. Information on the derivation of the estimators; Results of the simulation study made on the estimators; Conclusions.

Recent studies have shown that balance performance assessment based on artificial intelligence (AI) is feasible. However, balance control is very complex and requires different subsystems to participate, which have not been evaluated individually yet. Furthermore, these studies only classified individual's balance performance across limited grades. Therefore, in this study we attempted to implement AI to precisely evaluate different types of balance control subsystems (BCSes). First, a total of 224 commonly used and newly developed features were extracted from the center of pressure (CoP) data for each participant, respectively. Then, regressors were employed in order to map these features to the evaluation scores given by physical therapists, which include the total score in Mini-Balance-Evaluation-Systems-Tests (Mini-BESTest) and its sub-scores on BCSes, namely anticipatory postural adjustments (APA), reactive postural control (RPC), sensory orientation (SO), and dynamic gait (DG). Their scoring ranges should be 0-28, 0-6, 0-6, 0-6, and 0-10, respectively. The results show that their minimum mean absolute errors from AI estimation reach up to 2.658, 0.827, 0.970, 0.642, and 0.98, respectively. In sum, our study is a preliminary study for assessing BCSes based on AI, which shows its possibility to be used in the clinics in the future.

We present a sub-mm 3 , fully wireless, implantable intraocular pressure monitor microsystem (IMM) that comprises a powering coil, an antenna, a piezoresistive micro-electro-mechanical system pressure sensor, and a pressure sensing IC. The system provides a 24-h intraocular pressure monitoring, which is not possible with currently used tonometric measurements. The IMM volume is limited to 0.38 mm 3  (4 × smaller than previous state-of-the-art) for the studies on laboratory rodents prior to human use. A cavity resonator magnetic coupling delivers the wireless power to the chip with 4.89% efficiency. The chip senses a change in a differential sensor resistance by providing a low-power differential resistance to frequency conversion with the measured standard deviation in differential resistance sensing of . The data packets are wirelessly transmitted by an ultralow power 2.4-GHz ISM band OOK transmitter. The IMM is integrated on a 5-μm-thick biocompatible Parylene C substrate. Implemented in a 0.18-μm CMOS process, the system achieves 0.67-mmHg pressure sensitivity with differential resistance sensing and dissipates only 6.3 nW with 30 min of measurement intervals. We verify the IMM functionality in the in vivo biological experiment.

Reliable prediction of forthcoming seizures will be a milestone in epilepsy research. A method capable of timely predicting the occurrence of seizures could significantly improve the quality of life for epilepsy patients and open new therapeutic approaches. Seizures are usually characterized by generalized spike wave discharges. With the advent of seizures, the variation of spike rate (SR) will have different manifestations. In this study, a seizure prediction approach based on spike rate is proposed and evaluated. Firstly, a low-pass filter is applied to remove the high frequency artifacts in electroencephalogram (EEG). Then, the morphology filter is used to detect spikes and compute SR, and SR is smoothed with an average filter. Finally, the performance of smoothed SR (SRm) in EEG during interictal, preictal, and ictal periods is analyzed and employed as an index for seizure prediction. Experiments with long-term intracranial EEGs of 21 patients show that the proposed seizure prediction approach achieves a sensitivity of 75.8% with an average false prediction rate of 0.09/h. The low computational complexity of the proposed approach enables its possibility of applications in an implantable device for epilepsy therapy.

Automatic seizure detection is of great significance for epilepsy long-term monitoring, diagnosis, and rehabilitation, and it is the key to closed-loop brain stimulation. This paper presents a novel wavelet-based automatic seizure detection method with high sensitivity. The proposed method first conducts wavelet decomposition of multi-channel intracranial EEG (iEEG) with five scales, and selects three frequency bands of them for subsequent processing. Effective features are extracted, such as relative energy, relative amplitude, coefficient of variation and fluctuation index at the selected scales, and then these features are sent into the support vector machine for training and classification. Afterwards a postprocessing is applied on the raw classification results to obtain more accurate and stable results. Postprocessing includes smoothing, multi-channel decision fusion and collar technique. Its performance is evaluated on a large dataset of 509 h from 21 epileptic patients. Experiments show that the proposed method could achieve a sensitivity of 94.46% and a specificity of 95.26% with a false detection rate of 0.58/h for seizure detection in long-term iEEG.