Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches

PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning

Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the decision decentralized, which increases the cost and computation of the whole UAV system. This paper researches a low-cost UAV formation system consisting of one leader (equipped with the intelligence chip) with five followers (without the intelligence chip), and proposes a centralized collision-free formation-keeping method. The communication in the whole process is considered and the protocol is designed by minimizing the communication cost. In addition, an analysis of the Proximal Policy Optimization (PPO) algorithm is provided; the paper derives the estimation error bound, and reveals the relationship between the bound and exploration. To encourage the agent to balance their exploration and estimation error bound, a version of PPO named PPO-Exploration (PPO-Exp) is proposed. It can adjust the clip constraint parameter and make the exploration mechanism more flexible. The results of the experiments show that PPO-Exp performs better than the current algorithms in these tasks

Boosting the Lithium-Ion Transport Kinetics of Sn-Based Coordination Polymers through Ligand Aromaticity Manipulation

Tin-based compounds are promising anode materials for lithium-ion batteries owing to their low charge/discharge voltage and high theoretical capacity but are plagued by both huge volume expansion during cycling and complex synthetic procedures. Constructing a coordination network between Sn and the lithium-active organic matrix can effectively relieve the volume expansion and increase the lithium storage active site utilization. Herein, we report a facile method to prepare two one-dimensional Sn-based coordination polymers [Sn(Hcta)]n (1) and [Sn(Hbtc)]n (2) (H3cta = 1,3,5-cyclohexanetricarboxylic acid, H3btc = 1,3,5-benzenetricarboxylic acid) for lithium storage, which differ only in the aromaticity of the ligand. 2 with an aromatic ligand provided a reversible capacity of 833 mAh g–1 at 200 mA g–1 over 160 cycles, higher than that of 1 without an aromatic ligand due to the quick charge transfer. The reversible lithium storage reactions of metal centers and organic ligands and the volume expansion rate of Sn-based coordination polymers during cycling were studied by detailed characterization and density functional theory (DFT) calculations. This research revealed that the structural factor of ligand aromaticity in these Sn-based coordination polymers boosted the utilization of active sites and rapid charge transfer, offering a coordination chemistry strategy for the design and synthesis of advanced anode materials