Deep Reinforcement Learning with the Random Neural Network

Over the past decade, wearable activity trackers (WATs) have become increasingly popular. However, despite many research studies in different fields (such as psychology, health, and design), few have sought to jointly examine the critical aspects of utility (i.e., benefits brought by these devices), privacy, and security (i.e., risks and vulnerabilities associated with them). To fill this gap, we reviewed 236 studies that researched the benefits of using WATs, the implications for the privacy of users of WATs, and the security vulnerabilities of these devices. Our survey revealed that these devices expose users to several threats. For example, WAT data can be mined to infer private information, such as the personality traits of the user. Whereas many works propose empirical findings about users’ privacy perceptions and their behaviors in relation to privacy, we found relatively few studies researching technologies to better protect users’ privacy with these devices. This survey contributes to systematizing knowledge on the utility, privacy, and security of WATs, shedding light on the state-of-the-art approaches with these devices, and discussing open research opportunities.

Wearable Activity Trackers: A Survey on Utility, Privacy, and Security

Background: Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes’ physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina. Materials and Methods: IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation. Results: The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes’ physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance. Conclusions: In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model’s efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets

With the development of modern science and technology, through real-time and continuous tracking and monitoring of distribution network equipment, analysis of historical data, introduction of neural network calculation and cloud big object intelligence transfer technology, it provides basic support for the application and research of intelligent operation and inspection management and control system of distribution network, such as state evaluation, intelligent error prevention, intelligent dispatch orders, and so on, so as to improve the overall level of distribution network construction, development, operation and maintenance. To realize full information monitoring of equipment status.

Application and Research of Distribution Network Intelligent Operation Inspection Management and Control System

Distribution network equipment has the characteristics of multiple types and wide distribution, and the factors that advance its decommissioning are difficult to be identified. In order to get the important factors affecting the early decommissioning of equipment, this paper proposed a correlation analysis algorithm, which introduced the three-dimensional matrix into Apriori algorithm, operating more efficiency by reducing the computational redundancy in the classical algorithm, and digs out the set of important factors affecting the early decommissioning of distribution network equipment. By comparing the experimental results, it can be seen that the three-dimensional matrix algorithm can obtain the association rules of distribution network equipment decommissioning factors and significantly improve the computational efficiency.

Correlation analysis of distribution network equipment decommissioning data based on three-dimensional matrix

Equipment Operation Analysis and Application Research Based on Big Data Electric Power Distribution Network

The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.

/pdf/combating-false-data-injection-attacks-on-human-centric-1gdkvinh.pdf

Combating False Data Injection Attacks on Human-Centric Sensing Applications

In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

/pdf/multivariate-time-series-prediction-of-high-dimensional-data-23csvhq8af.pdf

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

The embodiment of the invention provides a big data analysis model algorithm type selection method and device, electronic equipment and a medium. The method comprises the steps of matching a corresponding model category according to an application scene and data characteristics of power grid business data; respectively processing the power grid business data based on at least two analysis models corresponding to the model category to obtain a processing result; and evaluating the analysis model according to the processing result and evaluation parameters corresponding to the model category, and performing model recommendation based on the evaluation result. According to the technical scheme provided by the embodiment of the invention, the model category required for big data analysis is quickly determined through the application scene and the data characteristics, and the big data analysis efficiency is improved.

Big data analysis model algorithm type selection method and device, electronic equipment and medium

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Papers

Equipment Operation Analysis and Application Research Based on Big Data Electric Power Distribution Network

Combating False Data Injection Attacks on Human-Centric Sensing Applications

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

Big data analysis model algorithm type selection method and device, electronic equipment and medium