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.

Integrated Simulation Method for Interaction between Manufacturing Process and Machine Tool

The interaction between the machining process and the machine tool （IMPMT） plays an important role on high precision components manufacturing. However, most researches are focused on the machining process or the machine tool separately, and the interaction between them has been always overlooked. In this paper, a novel simplified method is proposed to realize the simulation of IMPMT by combining use the finite element method and state space method. In this method, the transfer function of the machine tool is built as a small state space. The small state space is obtained from the complicated finite element model of the whole machine tool. Furthermore, the control system of the machine tool is integrated with the transfer function of the machine tool to generate the cutting trajectory. Then, the tool tip response under the cutting force is used to predict the machined surface. Finally, a case study is carried out for a fly-cutting machining process, the dynamic response analysis of an ultra-precision fly-cutting machine tool and the machined surface verifies the effectiveness of this method. This research proposes a simplified method to study the IMPMT, the relationships between the machining process and the machine tool are established and the surface generation is obtained.

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.

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.

Feature Setup Determination in Integrated CAD/CAM System For Concurrent Engineering

This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.

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.

Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning(ML)systems are difficult to design because of the‘‘Two Black Boxes’’problem that exists at the interface between human and machine.Many algorithms that are used in interactive ML systems are black boxes that are presented to users,while the human cognition represents a second black box that can be difficult for the algorithm to interpret.These black boxes create cognitive gaps between the user and the interactive ML model.In this paper,we identify several cognitive gaps that exist in a previously-developed interactive visual analytics(VA)system,Andromeda,but are also representative of common problems in other VA systems.Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system.These include designing new visual features to help people better understand how Andromeda processes and interacts with data,as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process.We evaluate our designs through both qualitative and quantitative analysis,and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.

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.

DATA CRYSTALLIZATION APPLIED FOR DESIGNING NEW PRODUCTS

It is only the observable part of the real world that can be stored in data. For such incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable events. This is realized by data crystallization, where dummy items, corresponding to potential existence ofunobservable events, are inserted to the given data. These dummy items and their relations with observable events are visualized by applying KeyGraph to the data with dummy items, like the crystallization of snow where dusts are involved in the formation of crystallization of water molecules. For tuning the granularity level of structure to be visualized, the tool of data crystallization is integrated with human＇s process of understanding significant scenarios in the real world. This basic method is expected to be applicable for various real world domains where previous methods of chance-discovery lead human to successful decision making. In this paper, we apply the data crystallization with human-interactive annealing （DCHA） to the design of products in a real company. The results show its effect to industrial decision making.

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.

Flexible,anti-damage,and non-contact sensing electronic skin implanted with MWCNT to block public pathogens contact infection

If a person comes into contact with pathogens on public facilities,there is a threat of contact(skin/wound)infections.More urgently,there are also reports about COVID-19 coronavirus contact infection,which once again reminds that contact infection is a very easily overlooked disease exposure route.Herein,we propose an innovative implantation strategy to fabricate a multi-walled carbon nanotube/polyvinyl alcohol(MWCNT/PVA,MCP)interpenetrating interface to achieve flexibility,anti-damage,and non-contact sensing electronic skin(E-skin).Interestingly,the MCP E-skin had a fascinating non-contact sensing function,which can respond to the finger approaching 0−20 mm through the spatial weak field.This non-contact sensing can be applied urgently to human–machine interactions in public facilities to block pathogen.The scratches of the fruit knife did not damage the MCP E-skin,and can resist chemical corrosion after hydrophobic treatment.In addition,the MCP E-skin was developed to real-time monitor the respiratory and cough for exercise detection and disease diagnosis.Notably,the MCP E-skin has great potential for emergency applications in times of infectious disease pandemics.

A Feature Selection Method for Prediction Essential Protein

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.

DevelopmentofaNewColumnDesignSoftwareUsingVisualBasic

A new column design software,TCD,was developed using visual basic for windows.The TCD interfaces are very friendly to facilitate communication between the user and the computer.TCD can be used not only to design new columns,but also to check old columns for retray application.TCD can be easily learned and mastered.Several design examples are using TCD to display the advantages of TCD.This paper introduces the interface design process,functions and advantages of TCD.