Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Systematic Approach to Training for Design of Nuclear Power Plant Decommissioning Training in South Korea

In 1979, unavailability of MFWS （Main Feedwater System） in TMI （Three Mile Island） Unit-2 happened in the United States. To make it worse, due to malfunction of Isolation Control Valves in AFWS （Auxiliary Feedwater System）, the supply of cooling water to SGs （Steam Generators） was delayed approximately 8 minutes compared to AOP （Abnormal Operating Procedure）. In the long run, on account of deferred heat sink provision to SGs, the reactor core was melted partially. It was the first critical accident in the US commercial NPP （Nuclear Power Plant） history. Hence, after TMI Accident, US NRC （Nuclear Regulatory Committee） suggested more than one hundred alternatives to improve safety and reliability of NPP. Among these countermeasures, one proposal was related to training area. It was SAT （Systematic Approach to Training） methodology. Therefore, the goal of SAT is the enhancement of NPP stability through training point of view. Since the appearance of SAT in the nuclear industry, it has acquired the unwavering position in the US NPP training field. Meanwhile, significance of NPP decommissioning has been soared up in South Korea since the announcement of Kori Unit-1 decommissioning decision. According to the proclaimed plan from Korean government, Kori Unit-1 is scheduled to be decommissioned from June, 2017. Under this circumstance, nurturing sufficient number of NPP decommissioning engineers is one of the most urgent issues in South Korean nuclear industry. Hence, to elevate efficiency and consistency of training quality, SAT methodology can be a reliable solution for the training of decommissioning engineers. For this reason, the present study aims to design SAT based NPP decommissioning engineer training in South Korea.

Teaching Chinese to English Tour Guide Interpreting

In the present study, some junior students of the English major were asked to verbalize or write their thoughts after interpreting. Besides, students＇ errors were also identified through observations by the researchers. As a result some of the mistakes made by students were found： lacking humor and variety in their interpreting, little vocabulary and variation in expression, etc.. Reasons were also analyzed which included that students were generally not familiar with some of the often-used sentence patterns for tour guide interpretation, and teachers are not using the suitable ways of teaching tour guide interpreting. To conclude, ways to solve these problems were recommended for readers＇ reference.

Music Education Advances Through Sino-Foreign Exchanges

MUSIC education in China and the West may differ in approach, but when combined are complementary in outcome. China＇s music programs excels in training young artists under 18, while the Western focus is on musicians no younger than 18. This explains why so many outstanding Chinese musicians study at, and are welcomed by, Western schools after receiving their initial training in China.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.