Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.

The beetle antennae search (BAS) algorithm is a new meta-heuristic algorithm which has been shown to be very useful in many applications. However, the algorithm itself still has some problems, such as low precision and easy to fall into local optimum when solving complex problems, and excessive dependence on parameter settings. In this paper, an algorithm called beetle antennae search algorithm based on Lévy flights and adaptive strategy (LABAS) is proposed to solve these problems. The algorithm turns the beetle into a population and updates the population with elite individuals' information to improve the convergence rate and stability. At the same time, Lévy flights and scaling factor are introduced to enhance the algorithm's exploration ability. After that, the adaptive step size strategy is used to solve the problem of difficult parameter setting. Finally, the generalized opposition-based learning is applied to the initial population and elite individuals, which makes the algorithm achieve a certain balance between global exploration and local exploitation. The LABAS algorithm is compared with 6 other heuristic algorithms on 10 benchmark functions. And the simulation results show that the LABAS algorithm is superior to the other six algorithms in terms of solution accuracy, convergence rate and robustness.

The development of artificial intelligence (AI) game agents that use deep reinforcement learning (DRL) algorithms to process visual information for decision-making has emerged as a key research focus in both academia and industry. However, previous game agents have struggled to execute multiple commands simultaneously in a single decision, failing to accurately replicate the complex control patterns that characterize human gameplay. In this paper, we utilize the ViZDoom environment as the DRL research platform and transform the agent–environment interactions into a Partially Observable Markov Decision Process (POMDP). We introduce an advanced multi-agent deep reinforcement learning (DRL) framework, specifically a Multi-Agent Proximal Policy Optimization (MA-PPO), designed to optimize target acquisition while operating within defined ammunition and time constraints. In MA-PPO, each agent handles distinct parallel tasks with custom reward functions for performance evaluation. The agents make independent decisions while simultaneously executing multiple commands to mimic human-like gameplay behavior. Our evaluation compares MA-PPO against other DRL algorithms, showing a 30.67% performance improvement over the baseline algorithm.

Given the uncertainty from renewable production, local loads and battery operating states in microgrid, it is vital to develop an efficient energy management scheme to improve system economics and enhance grid reliability. In this paper, we consider a renewable integrated microgrid scenario including an energy storage system (ESS), bidirectional energy flow from/to conventional power grid and ESS health estimation. We develop a novel demand response-based scheme for microgrid energy management with a long short-term memory (LSTM) network and reinforcement learning (RL), aiming to improve the system operating profit from energy-trading and reduce the battery health cost from energy-scheduling. Specifically, to overcome the uncertainty from future, we utilize LSTM to forecast the unknown demand and electricity price. To obtain the desired ESS control scheme, we apply RL to learn an optimal energy-scheduling strategy. To improve the critical performance of the RL paradigm, we propose a random greedy strategy to encourage exploration. Numerical results show that our proposed algorithm outperforms the baselines by improve the system operating profit by 8.27% and 17.31% while ensuring ESS operating safety. By integrating energy efficiency with sustainable energy management practices, our scheme contributes to long-term environmental and economic resilience.