HYDRAULIC ACTIVE GUIDE ROLLER SYSTEM FOR HIGH-SPEED ELEVATOR BASED ON FUZZY CONTROLLER

Increase of elevator speed brings about amplified vibrations of high-speed elevator. In order to reduce the horizontal vibrations of high-speed elevator, a new type of hydraulic active guide roller system based on fuzzy logic controller is developed. First the working principle of the hydraulic guide system is introduced, then the dynamic model of the horizontal vibrations for elevator cage with active guide roller system and the mathematical model of the hydraulic system are given. A fuzzy logic controller for the hydraulic system is designed to control the hydraulic actuator. To improve the control performance, preview compensation for the controller is provided. Finally, simulation and experiments are executed to verify the hydraulic active guide roller system and the control strategy. Both the simulation and experimental results indicate that the hydraulic active guide roller system can reduce the horizontal vibrations of the elevator effectively and has better effects than the passive one, and the fuzzy logic controller with preview compensation can give superior control performance.

Research on Applying Bluetooth to an Elevator Wireless Control System

Compared with other elevator control systems, the wireless control system has many advantages such as easy to install and maintain. Bluetooth is a new technology of short-range wireless communication, and the idea of applying Bluetooth to the elevator wireless control system is expected to get wide application. In this paper, a wireless control prototype system is introduced, and the experiments of this system proved the feasibility of this idea.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

APPLICATION OF INTELLIGENCE FORECASTING METHOD IN TRAFFIC ANALYSIS OF EGCS

Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time series NN traffic flow forecasting model.Simulation results show its validity.

Early aggressive versus initially conservative treatment in elderly patients with non-ST-segment elevation acute coronary syndromeaTitle and subTitle Breakaaaaaaaa randomized controlled trial

Abstract Objectives The authors sought to describe the association between post-procedural bleeding and long-term recurrent bleeding, major adverse cardiac events （MACE）, and mortality among older patients undergoing per-cutaneous coronary intervention （PCI）.