Intelligent monitoring-based safety system of massage robot

As an important attribute of robots, safety is involved in each link of the full life cycle of robots, including the design, manufacturing, operation and maintenance. The present study on robot safety is a systematic project. Traditionally, robot safety is defined as follows: robots should not collide with humans, or robots should not harm humans when they collide. Based on this definition of robot safety, researchers have proposed ex ante and ex post safety standards and safety strategies and used the risk index and risk level as the evaluation indexes for safety methods. A massage robot realizes its massage therapy function through applying a rhythmic force on the massage object. Therefore, the traditional definition of safety, safety strategies, and safety realization methods cannot satisfy the function and safety requirements of massage robots. Based on the descriptions of the environment of massage robots and the tasks of massage robots, the present study analyzes the safety requirements of massage robots; analyzes the potential safety dangers of massage robots using the fault tree tool; proposes an error monitoring-based intelligent safety system for massage robots through monitoring and evaluating potential safety danger states, as well as decision making based on potential safety danger states; and verifies the feasibility of the intelligent safety system through an experiment.

CNN-Based Intelligent Safety Surveillance in Green IoT Applications

Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things(IoT)applications.However,traditional safety monitoring methods require a lot of labor source.In this paper,we propose intelligent safety surveillance(ISS)method using a convolutional neural network(CNN),which is an autosupervised method to detect workers whether or not wearing helmets.First,to train the CNN-based ISS model,the labeled datasets mainly come from two aspects:1)our labeled datasets with the full labeled on both helmet and pedestrian;2)public labeled datasets with the parts labeled either on the helmet or pedestrian.To fully take advantage of all datasets,we redesign CNN structure of network and loss functions based on YOLOv3.Then,we test our proposed ISS method based on the specific detection evaluation metrics.Finally,experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets.When the threshold of intersection over union(IoU)between the predicted box and ground truth is set to 0.5,the average precision of pedestrians and helmets can reach 0.864 and 0.891,respectively.

AIGC challenges and opportunities related to public safety:A case study of ChatGPT

Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.