Search Results (8)

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change. Full article

The upset state is an unexpected flight state, which is characterized by an unintentional deviation from normal operating parameters. It is difficult for the pilot to recover the aircraft from the upset state accurately and quickly. In this paper, an upset recovery strategy and pilot assistance system (PAS) based on reinforcement learning is proposed. The man–machine closed-loop system was established and the upset state, such as a high angle of attack and large attitude angle, was induced. The upset recovery problem was transformed into a sequential decision problem, and the Markov decision model of upset recovery was established by taking the deflection change of the control surface as the action. The proximal policy optimization (PPO) algorithm was selected for the strategy training. The adaptive pilot model and the reinforcement learning method proposed in this paper were used to make the aircraft recover from the upset state. Based on the correspondence between the flight state, the recovery method, and the recovery result, the aircraft upset recovery safety envelopes were formed, and the four-level upset recovery PAS with alarm warning, coordinated control, and autonomous recovery modes was constructed. The results of the digital virtual flight simulation and ground flight test show that compared with a traditional single pilot, the aircraft upset recovery strategy, the upset recovery safety envelopes, and the PAS established in this study could reduce the handling burden of the pilot and improve the success rate and effect of upset recovery. This research has certain theoretical reference values for flight safety and pilot training. Full article

Digital technology has broken the traditional “individual–system” trust dichotomy and has brought in a new “increment” of trust state to society. Taking an engineering project as an example, the key to a successful digital design, digital construction and digital operation is that digital technology has built an inclusive trust system and coordination mechanism in the whole life cycle of the project. In this process, the integration of the people, technology and system has broken the dimensional barrier of “man, machine and object” in the project, which not only exceeds the dependence on traditional individuals and systems, but also reduces the cost of system operation and improves the efficiency of project construction. Full article

Unexpected instances have posed challenges to production lines over the last few years. The latest COVID-19 global epidemic is one notable example. In addition to its social impact, the virus has destroyed the traditional industrial production system. Industry 4.0 requires adapting to changing prerequisites with adaptability. However, the next movement, Industry 5.0, has emerged in recent years. Industry 5.0 takes a more coordinated approach than Industry 4.0, with increased collaboration among humans and machines. With a human-centered strategy, Industry 5.0 improves Industry 4.0 for greater sustainability and resilience. The concept of Industry 4.0 is the interconnection via cyber-physical systems. Industry 5.0, also associated with systems enabled by Industry 4.0, discusses the relationship between “man and machine,” called robots or cobots. This paper discusses the industry 5.0 possibilities, the restrictions, and future analysis potentials. Industry 5.0 is a new paradigm change that tends to bring negotiated settlement because it places less prominence on technology and assumes that the possibilities for advancement are predicated on collaboration between humans and machines. This paper aims to examine the potential implementations of Industry 5.0. Once the current progress and problem were discovered, the previous research on the investigated topic was reviewed, research limitations were found, and the systematic analysis procedure was developed. The classifications of industry 5.0 and the sophisticated technology required for this industry revolution are the first subjects of discussion. There is additional discussion of the application domains enabled by Industry 5.0, such as healthcare, supply chain, production growth, cloud industrial production, and so on. The research also included challenges and problems investigated in this paper to understand better the issues caused by organizations among some robotic systems and individuals on the production lines. Full article

With the vigorous development of e-commerce, efficient order picking in e-commerce warehouses has attracted the attention of many scholars. To analyze the issues about order-picking efficiency currently being studied by relevant scholars in e-commerce warehouses, this paper reviews the literature on the application of order-picking strategy and efficiency optimization direction from 2020 to 2022. That mainly falls into two categories of picking systems: “picker-to-parts” and “parts-to-picker”. In the “picker-to-parts” picking system, more attention is paid to the picking strategies of storage assignment and order batching and the integration of multiple picking strategies. In contrast, in the “parts-to-picker” picking system, the main attention is on the man-machine cooperation in the Mobile Robot Fulfillment System (RMFS) and the Automated Storage and Retrieval System (AS/RS), as well as the coordination of the picking station. Further, this paper proposes future research directions for two categories of picking systems: further studying the order splitting strategy and order delivery issues; considering the dynamic uncertainties; combining the automated picking system with different picking strategies, and so on. Full article

Supine rollover plays an important role in the prevention of pressure sores in long-term bedridden patients. It is of great significance to study the mechanism of human supine rollover movement and to design the rehabilitation rollover mechanism in line with man-machine cooperation. In human supine rollover movement, shoulder and hip are the key parts of force application. Based on anatomical theory, the motion trajectory information of shoulder and hip skeletal rehabilitation parts is collected by combining optical motion capture and rigid body modeling. Following a kinematics simulation analysis, the simulation curve was compared with the experimental curve track; the numerical difference was small. It is proved that the simulation model is correct, and it is also shown that the designed rehabilitation rollover mechanism can better reproduce the natural rolling motion state of the human body. It can meet the requirements of human-machine synergistic assisted lateral roll rehabilitation aids and provides a solution for pressure sore prevention. Full article

A typical man–machine coupling system could provide the wearer a coordinated and assisted movement by the lower limb exoskeleton. The process of cooperative movement relies on the accurate perception of the wearer’s human movement information and the accurate planning and control of the joint movement of the lower limb exoskeleton. In this paper, a neural network and a Long-Short Term Memory (LSTM) machine learning model method is proposed to predict the actual movement trajectory of the human body’s lower limbs. Then a wearable joint angle measurement device was designed for gait trajectory prediction, which can be used for predictive control through machine learning methods. The experimental results show that the LSTM model can accurately predict the gait trajectory with an average mean square error. This method has practical significance for prediction the trajectory of the lower limb exoskeleton. Full article

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion. Full article