High frequency impedance based fault location in distribution system with DGs

Distributed Generations (DGs) with power electronic devices and their control loops will cause distortion to the fault currents and result in errors for power frequency measurement based fault locations. This might jeopardize the distribution system fault restoration and reduce the grid resilience. The proposed method uses high frequency (up to 3kHz) fault information and short window measurement to avoid the influence of DG control loops. Applying the DG high frequency impedance model, faults can be accurately located by measuring the system high frequency line reactance. Assisted with the DG side recorded unsynchronized data, this method can be employed to distribution systems with multiple branches and laterals

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding

There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.Comment: To appear at Machine Learning for Healthcare Conference (MLHC2022

The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant

Molecular Design for Electron-Driven Double-Proton Transfer: A New Scenario for Excited-State Proton-Coupled Electron Transfer

Proton-coupled electron transfer (PCET) reactions play important roles in solar energy conversion processes. Designing efficient artificial photosystems with PCET mechanisms is a promising solution for the growing demands of energy resources. Compared to ground states, inducing the PCET reactions directly from electronically excited states, named excited-state PCET (ES-PCET) reactions, is a more direct and efficient avenue to the formation of solar fuels. Here, based on benzimidazole phenolic derivatives, we have designed and studied some molecular structures that can undergo the electron-driven double-proton transfer (EDDPT) reactions within the ES-PCET framework. According to our DFT/TDDFT calculation results, the two protons transfer in a stepwise manner in the EDDPT process, and compared to the common way of electron-driven single-proton transfer (EDSPT) reactions, the proton transfer in the EDDPT process not only has a smaller energy barrier but also experiences a longer transferring distance, which has beneficial effects on producing solar fuels. The study of ES-PCET reactions under the mechanism of EDDPT may cast light on the regulation of proton transfer at defined distances and time scales, which is important in energy conversion processes

Research on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural Network

The nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex. In the case of an operation fault in these systems, there will be a large number of alarm parameters, which can cause humans to be hurt in the accidents under great pressure. Therefore, it is necessary to predict the values of the key parameters of a device system. The prediction of the key parameters’ values can help operators determine the changing trends of system parameters in advance, which can effectively improve system safety. In this paper, a deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. The proposed network is verified by simulations and compared with the traditional grey theory. The simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant

Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI

International audienceIn this paper, we investigate a reconfigurable intelligent surface (RIS) aided multi-pair communication system, in which multi-pair users exchange information via an RIS. We derive an approximate expression for the achievable rate by assuming that statistical channel state information (CSI) is available. A genetic algorithm (GA) to solve the rate maximization problem is proposed as well. In particular, we consider implementations of RISs with continuous phase shifts (CPSs) and discrete phase shifts (DPSs). Simulation results verify the obtained results and show that the proposed GA method has almost the same performance as the globally optimal solution. In addition, numerical results show that three quantization bits can achieve a large portion of the achievable rate for the CPSs setup. Index Terms-Reconfigurable intelligent surface (RIS), intelligent reflecting surface (IRS), statistical channel state information (CSI), multi-pair communication, genetic algorithm (GA)