Operation and Consideration of a Pipe Corrosion Inspection System Based on Human-in-the-Loop Machine Learning

The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places significant workload on human experts owing to the large number of required images.Furthermore,visual assessment of corrosion levels is prone to subjective errors.To address these issues,we developed an AI(artificial intelligence)-based corrosion-diagnosis system(AI corrosion-diagnosis system)and implemented it in a factory.The proposed system architecture was based on HITL(human-in-the-loop)ML(machine learning)[1].To overcome the difficulty of developing a highly accurate ML model during the PoC(proof-of-concept)stage,the system relies on cooperation between humans and the ML model,utilizing human expertise during operation.For instance,if the accuracy of the ML model was initially 60%during the development stage,a cooperative approach would be adopted during the operational stage,with humans supplementing the remaining 40%accuracy.The implemented system’s ML model achieved a recall rate of approximately 70%.The system’s implementation not only contributed to the efficiency of operations by supporting diagnosis through the ML model but also facilitated the transition to systematic data management,resulting in an overall workload reduction of approximately 50%.The operation based on HITL was demonstrated to be a crucial element for achieving efficient system operation through the collaboration of humans and ML models,even when the initial accuracy of the ML model was low.Future efforts will focus on improving the detection of corrosion at elevated locations by considering using video cameras to capture pipe images.The goal is to reduce the workload for inspectors and enhance the quality of inspections by identifying corrosion locations using ML models.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

Bioinspired nanomaterials for wearable sensing and human–machine interfacing

The inculcation of bioinspiration in sensing and human–machine interface(HMI)technologies can lead to distinctive characteristics such as conformability,low power consumption,high sensitivity,and unique properties like self-healing,self-cleaning,and adaptability.Both sensing and HMI are fields rife with opportunities for the application of bioinspired nanomaterials,particularly when it comes to wearable sensory systems where biocompatibility is an additional requirement.This review discusses recent development in bioinspired nanomaterials for wearable sensing and HMIs,with a specific focus on state-of-the-art bioinspired capacitive sensors,piezoresistive sensors,piezoelectric sensors,triboelectric sensors,magnetoelastic sensors,and electrochemical sensors.We also present a comprehensive overview of the challenges that have hindered the scientific advancement in academia and commercialization in the industry.

APPLICATION OF HUMAN COGNITIVE MODEL FOR TIME DEPENDENT OPERATOR BEHAVIOR IN CHINESE NPP

This paper discusses some issues on human reliability model of time dependent human behavior. Some results of the crew reliability experiment on Tsinghua training simulator in China are given, Meanwhile, a case of calculation for human error probability during anticipated transient without scram (ATWS) based on the data drew from the recent experiment is offered.

Analysis of Design Approach for Navy Weapon System Operation Environment

This study is a preparation phase for visualization of utilized information using ergonomic user interface and standardization of elements for anti-air weapon system. Therefore, we investigated the instances of Navy Weapon System operation environment for defense advanced country. Based on the collected data, we compared and analyzed the weapon system operation environment design. Ultimately, it is essential to share a variety of battle field conditions such as enemy threat, enemy/friendly information, terrain information that can be effectively recognized. In this paper, we conduct case study for ergonomically development of Operation Environment. It is expected that this research improves the situational awareness and reduces the operator’s task load.

Design and Analysis of a Novel Shoulder Exoskeleton Based on a Parallel Mechanism

Power-assisted upper-limb exoskeletons are primarily used to improve the handling efficiency and load capacity.However,kinematic mismatch between the kinematics and biological joints is a major problem in most existing exoskeletons,because it reduces the boosting effect and causes pain and long-term joint damage in humans.In this study,a shoulder augmentation exoskeleton was designed based on a parallel mechanism that solves the shoulder dislocation problem using the upper arm as a passive limb.Consequently,the human–machine synergy and wearability of the exoskeleton system were improved without increasing the volume and weight of the system.A parallel mechanism was used as the structural body of the shoulder joint exoskeleton,and its workspace,dexterity,and stiffness were analyzed.Additionally,an ergonomic model was developed using the principle of virtual work,and a case analysis was performed considering the lifting of heavy objects.The results show that the upper arm reduces the driving force requirement in coordinated motion,enhances the load capacity of the system,and achieves excellent assistance.

Human Machine Collaborative Support Scheduling System of Intelligence Information from Multiple Unmanned Aerial Vehicles Based on Eye Tracker

Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However, the current systems should take advantage of the operator's attention to obtain the optimal solution.In this paper, we innovatively propose a human-machine collaborative support scheduling system of intelligence information from multi-UAVs based on eye-tracker. Firstly, the target recognition algorithm is applied to the images from the multiple unmanned aerial vehicles(multi-UAVs) to recognize the targets in the images. Then,the support system utilizes the eye tracker to gain the eye-gaze points which are intended to obtain the focused targets in the images. Finally, the heuristic scheduling algorithms take both the attributes of targets and the operator's attention into consideration to obtain the sequence of the images. As the processing time of the images collected by the multi-UAVs is uncertain, however the upper bounds and lower bounds of the processing time are known before. So the processing time of the images is modeled by the interval processing time. The objective of the scheduling problem is to minimize mean weighted completion time. This paper proposes some new polynomial time heuristic scheduling algorithms which firstly schedule the images including the focused targets. We conduct the scheduling experiments under six different distributions. The results indicate that the proposed algorithm is not sensitive to the different distributions of the processing time and has a negligible computational time. The absolute error of the best performing heuristic solution is only about 1%. Then, we incorporate the best performing heuristic algorithm into the human-machine collaborative support systems to verify the performance of the system.

An All‑In‑One Multifunctional Touch Sensor with Carbon‑Based Gradient Resistance Elements

Human–machine interactions using deep-learning methods are important in the research of virtual reality,augmented reality,and metaverse.Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes,signal crosstalk,propagation delay,and demanding configuration requirements.Here,an all-inone multipoint touch sensor(AIOM touch sensor)with only two electrodes is reported.The AIOM touch sensor is efficiently constructed by gradient resistance elements,which can highly adapt to diverse application-dependent configurations.Combined with deep learning method,the AIOM touch sensor can be utilized to recognize,learn,and memorize human–machine interactions.A biometric verification system is built based on the AIOM touch sensor,which achieves a high identification accuracy of over 98%and offers a promising hybrid cyber security against password leaking.Diversiform human–machine interactions,including freely playing piano music and programmatically controlling a drone,demonstrate the high stability,rapid response time,and excellent spatiotemporally dynamic resolution of the AIOM touch sensor,which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

Analysis of Design Directions for Ground Control Station (GCS)

This study is a preparation phase for integrated visualization of battlefield situation. To develop the ground control station for unmanned systems, many factors have to be considered from the design stages, such as layout, information component, representation scheme, and human operation methods. Considering such many factors can be very difficult, hence we conducted an in-depth investigation of design factors from major UAV stations around the world. We analyzed the design characteristics and the specifics. In conclusion, we were able to derive some common aspects of design characteristics, which lead to the successful design approach.

Technology acceptance comparison between on-road dynamic message sign and on-board human machine interface for connected vehicle-based variable speed limit in fog area

Purpose–Connected vehicle-based variable speed limit(CV-VSL)systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur.The purpose of the speed limit is to make the driver’s driving behavior more consistent,so as to improve traffic safety and relieve traffic congestion.The on-road dynamic message sign(DMS)and on-board human–machine interface(HMI)are two types of warning technologies for CV-VSL systems.This study aims to analyze drivers’acceptance of the two types of warning technologies in fog area and its influencing factors.Design/methodology/approach–This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator.The DMS and on-board HMI provided the driver with weather and speed limit information.In all,38 participants participated in the experiment and completed questionnaires on drivers’basic information,perceived usefulness and ease of use of the CV-VSL systems.Technology acceptance model(TAM)was developed to evaluate the drivers’acceptance of CV-VSL systems.A variance analysis method was used to study the influencing factors of drivers’acceptance including drivers’characteristics,technology types and fog density.Findings–The results showed that drivers’acceptance of on-road DMS was significantly higher than that of on-board HMI.The fog density had no significant effect on drivers’acceptance of on-road DMS or on-board HMI.Drivers’gender,age,driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently.This study is beneficial to the functional improvement of on-road DMS,on-board HMI and their market prospects.Originality/value–Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems.However,there were rare studies focused on the drivers’attitude toward using which was also called as acceptance of the CV-VSL systems.Therefore,this research calculated the drivers’acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM.Furthermore,variance analysis was conducted to explore whether the factors such as drivers’characteristics(gender,age,driving year and driving personality),technology types and fog density affected the drivers’acceptance of the CV-VSL systems.

Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication

The wearable sensors have recently attracted considerable attentions as communication interfaces through the information perception,decoding,and conveying process.However,it is still challenging to obtain a sensor that can convert detectable signals into multiple outputs for convenient,e cient,cryptic,and high-capacity information transmission.Herein,we present a capacitive sensor of magnetic field based on a tilted flexible micromagnet array(t-FMA)as the proposed interaction interface.With the bidirectional bending capability of t-FMA actuated by magnetic torque,the sensor can recognize both the magnitude and orientation of magnetic field in real time with non-overlapping capacitance signals.The optimized sensor exhibits the high sensitivity of over 1.3 T-1 and detection limit down to 1 mT with excellent durability.As a proof of concept,the sensor has been successfully demonstrated for convenient,e cient,and programmable interaction systems,e.g.,touchless Morse code and Braille communication.The distinguishable recognition of the magnetic field orientation and magnitude further enables the sensor unit as a high-capacity transmitter for cryptic information interaction(e.g.,encoded ID recognition)and multi-control instruction outputting.We believe that the proposed magnetic field sensor can open up a potential avenue for future applications including information communication,virtual reality device,and interactive robotics.

The imperative need to develop guidelines to manage human versus machine intelligence

Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions.Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area.

一种用于精密铸造工艺的树脂模具高效多功能固化设备的开发与应用

开发了一种基于stereo lithography appearance(SLA)光固化增材制造技术的、用于精密铸造领域树脂模具制作的后处理高效多功能固化设备。通过对核心机械结构的全新设计和加工工艺的深入探究,实现了可变循环散热、可变紫外功率、可变速固化、可变固化时间、舱门互锁等多项新功能。以programmable logic controller(PLC)和human machine interface(HMI)为中央控制器和人机交互平台,通过图形化界面编程实现了一键固化、个性化参数匹配、温差限值安全锁定等控制功能。最后通过多组目标件测试及数据对比,验证了设备具有固化目标高质量、固化过程高效率、界面操作简便、使用过程高安全等特性。其作为高效多功能的SLA光固化树脂模具后处理核心设备,具有很高的使用和推广价值。

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies;2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM;3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.

Towards Human‑Vehicle Interaction: Driving Risk Analysis Under Different Driver Vigilance States and Driving Risk Detection Method

The driver's behavior plays a crucial role in transportation safety.It is widely acknowledged that driver vigilance is a major contributor to traffic accidents.However,the quantitative impact of driver vigilance on driving risk has yet to be fully explored.This study aims to investigate the relationship between driver vigilance and driving risk,using data recorded from 28 drivers who maintain a speed of 80 km/h on a monotonous highway for 2 hours.The k-means and linear fitting methods are used to analyze the driving risk distribution under different driver vigilance states.Additionally,this study proposes a research framework for analyzing driving risk and develops three classification models(KNN,SVM,and DNN)to recognize the driving risk status.The results show that the frequency of low-risk incidents is negatively correlated with the driver's vigilance level,whereas the frequency of moderate-risk and high-risk incidents is positively correlated with the driver's vigilance level.The DNN model performs the best,achieving an accuracy of 0.972,recall of 0.972,precision of 0.973,and f1-score of 0.972,compared to KNN and SVM.This research could serve as a valuable reference for the design of warning systems and intelligent vehicles.

Effects of input method and display mode of situation map on early warning aircraft reconnaissance task performance with different information complexities

Early Warning Aircraft(EWA)are the main force for air detection and its Human-Machine Interface(HMI)should be designed to support task efficiency and safety.With the appli-cation of advanced input method and interface design in EWA,little is known about their actual usability in terms of human factors and ergonomics.The aim of this study was to investigate the effects of the input method and display mode of the situation map on EWA reconnaissance task performance with different information complexities.Eighteen participants attended a three-factor within-subject design experiment with input method(touch screen and mouse),display mode of the situation map(color and grayscale),and information complexity(high and low)as the inde-pendent variables.Participant behavior performance,subjective workload,heart rate/heart rate variability,and eye movements were recorded as the dependent variables.The results suggest that a touch screen requires greater task completion time and has greater physical demands than mouse operation;however,it also facilitates information processing by reducing the average fixation time.Color mode significantly decreases saccade counts compared to grayscale mode and is considered more appropriate for target search tasks as it induces less visual search load.High information complexity produces significant negative effects on behavior performance and subjective workload.It also has significant interaction effects with input method on fixation and saccade counts.The findings have implications in the optimization design of Human–Machine Interface for EWA task systems.

Digitally programmable organic light‐emitting tetrodes

Limited to the structure of traditional light‐emitting devices,electronic devices that can directly convert machine language into human visual information without introducing any back‐end circuit are still not easy to achieve.Based on a specially designed three‐phase co‐planar electrode structure,a new type of three‐phase alternating current driven organic light‐emitting device with the integration of emission and control functions,full‐color tunability and simple device structure is demonstrated in this study.We integrate the light‐emitting function of color‐tunable light‐emitting devices and the switching of three triodes in a single three phase organic light‐emitting device.The state control of luminous color and luminance intensity merely requires the introduction of a kind of machine language,that is an easy‐to‐program 6‐bit binary number coded digital signals.The color adjustable area covers 66%of the color triangle of the National Television System Committee.Such simple and easy‐to‐integrate light‐emitting system has great potential applications in the next‐generation man‐machine interface.

Real-Time Control Strategy of Exoskeleton Locomotion Trajectory Based on Multi-modal Fusion

The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confrontation during motion.In order to achieve an ideal man–machine state,a Time-varying Adaptive Gait Trajectory Generator(TAGT)is designed to estimate the motion intention of the wearer and generate a personalized gait trajectory.TAGT can enhance the hybrid intelligent decision-making ability under human–machine collaboration,promote good motion coordination between the exoskeleton and the wearer,and reduce metabolic consumption.An important feature of this controller is that it utilizes a multi-layer control strategy to provide locomotion assistance to the wearer,while allowing the user to control the gait trajectory based on human–robot Interaction(HRI)force and locomotion information.In this article,a Temporal Convolutional Gait Prediction(TCGP)model is designed to learn the personalized gait trajectory of the wearer,and the control performance of the model is further improved by fusing the predefined gait trajectory method with an adaptive interactive force control model.A human-in-the-loop control strategy is formed with the feedback information to stabilize the motion trajectory of the output joints and update the system state in real time based on the feedback from the inertial and interactive force signal.The experimental study employs able-bodied subjects wearing the exoskeleton for motion trajectory control to evaluate the performance of the proposed TAGT model in online adjustments.Data from these evaluations demonstrate that the controller TAGT has good motor coordination and can satisfy the subject to control the motor within a certain range according to the walking habit,guaranteeing the stability of the closed-loop system.

Wearable sensors for activity monitoring and motion control:A review

Wearable sensors for activity monitoring currently are being designed and developed,driven by an increasing demand in health care for noninvasive patient monitoring and rehabilitation training.This article reviews state-of-the-art wearable sensors for activity monitoring and motion control.Different technologies,including electromechanical,bioelectrical,and biomechanical sensors,are reviewed,along with their broad applications.Moreover,an overview of existing commercial wearable products and the computation methods for motion analysis are provided.Future research issues are identified and discussed.

Can ChatGPT reduce human financial analysts’optimistic biases?

This paper examines the potential of ChatGPT,a large language model,as a financial advisor for listed firm performance forecasts.We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’forecasts and the realised values.Our findings suggest that ChatGPT can correct the optimistic biases of human analysts.This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making.