Desire

Through the argument that the concept of phase transition also applies to the unfolding of the information processing system that is creation, the author arrives at the phase stage described in the Standard Model of particle physics, where this system and the information flowing through it also form a part that gets coupled to matter and spacetime. The author then concludes that this stage, together with those that came before it, form one complex cybernetic processing system which allows for information to flow back and forth through various feedback and feedforward loops. Further arguments are that the sources for the information flowing through this system are coming from Desire in the broadest sense of the word, as the main, driving feedforward loop; with emotion—as a further explication of motion—as the regulating feedback loop; and that combined they account for the fluctuation called life.

Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.

The Negation of Impossibility

The impossibility theorems of Arrow and Gibbard-Satterthwaite have been thought to rule out economic democracy and welfare economics. This paper demonstrates an information processing system which accords with the premises of these authors, and, consequently, proves their conclusions of impossibility to be untrue except as mathematical tautologies.

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

Internet of things-assisted advanced dynamic information processing system for physical education system

BACKGROUND: In recent years the Internet of Things (IoT) has become a popular technological culture in the physical education system. Though several technologies have grown in the physical education system domain, IoT plays a significant role due to its optimized health information processing framework for students during workouts. OBJECTIVE: In this paper, an advanced dynamic information processing system (ADIPS) has been proposed with IoT assistance to explore the traditional design architecture for physical activity tracking. METHOD: To track and evaluate human physical activity in day-to-day living, a new paradigm has been integrated with wearable IoT devices for effective information processing during physical workouts. Continuous observation and review of the condition and operations of various students by ADIPS helps to evaluate the sensed information to analyze the health condition of the students. RESULTS: The result of ADIPS has been implemented based on the performance factor correlation with the traditional system.

The Negation of Impossibility

The impossibility theorems of Arrow and Gibbard-Satterthwaite have been thought to rule out economic democracy and welfare economics. This paper demonstrates an information processing system which accords with the premises of these authors, and, consequently, proves their conclusions of impossibility to be untrue except as mathematical tautologies.