Identification of critical nodes for cascade faults of grids based on electrical PageRank

In this paper,the electrical PageRank method is proposed to identify the critical nodes in a power grid considering cascading faults as well as directional weighting.This method can rapidly and accurately focus on the critical nodes in the power system.First,the proposed method simulates the scenario in a grid after a node is attacked by cascading faults.The load loss of the grid is calculated.Second,the electrical PageRank algorithm is proposed.The nodal importance of a grid is determined by considering cascading faults as well as directional weights.The electrical PageRank values of the system nodes are obtained based on the proposed electrical PageRank algorithm and ranked to identify the critical nodes in a grid.Finally,the effectiveness of the proposed method is verified using the IEEE39 node system.The proposed method is highly effective in preventing the occurrence of cascading faults in power systems.

Fringe optimization for structured illumination super-resolution microscope with digital micromirror device

Structured illumination microscopy(SIM)is a promising super-resolution technique for imaging subcellular structures and dynamics due to its compatibility with most commonly usedffuorescent labeling methods.Structured illumination can be obtained by either laser interference or projection of fringe patterns.Here,we proposed a fringe projector composed of a compact multiwavelength LEDs module and a digital micromirror device(DMD)which can be directly attached to most commercial invertedffuorescent microscopes and update it into a SIM system.The effects of the period and duty cycle of fringe patterns on the modulation depth of the structured lightfield were studied.With the optimized fringe pattern,1:6×resolution improvement could be obtained with high-end oil objectives.Multicolor imaging and dynamics of subcellular organelles in live cells were also demonstrated.Our method provides a low-cost solution for SIM setup to expand its wide range of applications to most research labs in thefield of life science and medicine.

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical faults.However,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a benchmark.In view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault classifiers.The data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation current.In addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor current.The package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification algorithms.The database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are provided.The developed code is available online,and is free to use.

A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion path.Recently,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training efficiency.In this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment.Furthermore,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm.Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms.Tests showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,respectively.Moreover,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.

High-fidelity structured illumination microscopy by point-spread-function engineering

Structured illumination microscopy(SIM)has become a widely used tool for insight into biomedical challenges due to its rapid,long-term,and super-resolution(SR)imaging.However,artifacts that often appear in SIM images have long brought into question its fidelity,and might cause misinterpretation of biological structures.We present HiFi-SIM,a high-fidelity SIM reconstruction algorithm,by engineering the effective point spread function(PSF)into an ideal form.HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background.In particular,HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters;hence,it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment,thus promoting SIM as a daily imaging tool.

Applying project‐based learning in artificial intelligence and marine discipline:An evaluation study on a robotic sailboat platform

intelligence is penetrating various fields.The demand for interdisciplinary talent is increasingly important,while interdisciplinary educational activities for high school students are lagging behind.Project‐based learning(PBL)in artificial intelligence(AI)and robotic education activities supported by a robotic sailboat platform,the sailboat test arena(STAr),has been shown to popularise AI and robotic knowledge in young students.In the implementation of the programme,PBL was provided for students,and gamification pedagogy was applied to increase participants'learning motivation and engagement.The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels.The assessment results suggest that most students achieve learning outcomes on average.Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs.The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.