Technological anxiety:Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood.Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an“icing on the cake”or“adding fuel to the fire”needs further study.This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy.First,from the theoretical logic and the economic development law,this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs.Then,based on the panel data of 269 cities in China from 2006 to 2021,we use the two-way fixed effect model,dynamic threshold model,and two-stage intermediary effect model.The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism.Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the“creation effect”.In other words,advanced digital technology has created additional jobs,and the overall supply of employment in the labor market has increased.The conclusion is still valid after the endogeneity identification and robustness test.In addition,the positive effect has a nonlinear effect on the network scale.When the installation density of industrial robots exceeds a particular threshold value,the division of labor continues to deepen under the combined action of the production efficiency and compensation effects,which will cause enterprises to increase labor demand further.Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions.This study concludes that in the digital China era,the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market.This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

Simulation of a Double-Gate Dynamic Threshold Voltage Fully Depleted Silicon-on-Insulator nMOSFET

A novel planar DGDT FDSOI nMOSFET is presented, and the operation mechanism is discussed. The device fabrication processes and characteristics are simulated with Tsuprem 4 and Medici. The back-gate n-well is formed by implantation of phosphorus at a dosage of 3 × 10^13 cm^-2 and an energy of 250keV and connected directly to a front-gate n^＋ polysilicon. This method is completely compatible with the conventional bulk silicon process. Simulation results show that a DGDT FDSOI nMOSFET not only retains the advantages of a conventional FDSOI nMOSFET over a partially depleted （PD） SOI nMOSFET--that is the avoidance of anomalous subthreshold slope and kink effects but also shows a better drivability than a conventional FDSOI nMOSFET.

Establishing dynamic thresholds for potato nitrogen status diagnosis with the SPAD chlorophyll meter

The hand-held soil plant analysis development （SPAD） chlorophyll meter nitrogen status of the potato and guiding fertilization recommendations N recommendation, it is critical to establish the threshold SPAD value has proved to be a promising tool in evaluating the n the process of N evaluation of potato plants and （SPAD reading）, below which nitrogen supplement is required. And taking convenient using into account, the threshold needs to be dynamic throughout the potato growing season so that the users can test their potato plants and make fertilization decision at any growing time of potato. To complete this goal, field experiments with different nitrogen supply levels were conducted in different sites in northern China from 2009 to 2011. The results showed that threshold SPAD values decrease as the growing season progresses for all cultivars and planting sites. By statistical analysis, the threshold regression models were established respectively as： y=-0.003χ2-0.0507χ＋58.213 （y, threshold SPAD value; χ, days after emergence） for the potato cultivar Kexin 1, and y=-0.003χ2＋0.017χ＋52.489 （y, threshold SPAD value; χ, days after emergence） for the cultivar Shepody, from which, the threshold SPAD value at any day after emergence can be calculated.

Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation （LFM） signals. The smoothed pseudo Winger-Ville distribution （SPWVD） is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio （SNR） circumstance. The performance of this method is confirmed by numerical simulation.

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

The emergence of a new network architecture,known as Software Defined Networking(SDN),in the last two decades has overcome some drawbacks of traditional networks in terms of performance,scalability,reliability,security,and network management.However,the SDN is vulnerable to security threats that target its controller,such as low-rate Distributed Denial of Service(DDoS)attacks,The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component.Therefore,there is an urgent need to propose a detection approach for this type of attack with a high detection rate and low false-positive rates.Thus,this paper proposes an approach to detect low-rate DDoS attacks on the SDN controller by adapting a dynamic threshold.The proposed approach has been evaluated using four simulation scenarios covering a combination of low-rate DDoS attacks against the SDN controller involving(i)a single host attack targeting a single victim;(ii)a single host attack targeting multiple victims;(iii)multiple hosts attack targeting a single victim;and(iv)multiple hosts attack targeting multiple victims.The proposed approach’s average detection rates are 96.65%,91.83%,96.17%,and 95.33%for the above scenarios,respectively;and its average false-positive rates are 3.33%,8.17%,3.83%,and 4.67%for similar scenarios,respectively.The comparison between the proposed approach and two existing approaches showed that it outperformed them in both categories.

Efficient Dynamic Threshold Group Signature Scheme Based on Elliptic Curve Cryptosystem

The short secret key characteristic of elliptic curve cryptosystem （ECC） are integrated with the （ t, n ） threshold method to create a practical threshold group signature scheme characterized by simultaneous signing. The scheme not only meets the requirements of anonymity and traceability of group signature but also can withstand Tseng and Wang＇s conspiracy attack. It allows the group manager to add new members and delete old members according to actual application, while the system parameters have a little change. Cryptanalysis result shows that the scheme is efficient and secure.

RESEARCH AND APPLICATION OF A NEURAL NETWORK CLASSIFIER BASED ON DYNAMIC THRESHOLD

In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

A Nonparametric Approach to Foreground Detection in Dynamic Backgrounds

Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.

Central Force Optimization with Gravity <0, Elitism, and Dynamic Threshold Optimization: An Antenna Application, 6-Element Yagi-Uda Arrays

This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those extensions are Negative Gravity, Elitism, and Dynamic Threshold Optimization. The basic CFO heuristic does not include any of these, but adding them substantially improves the algorithm’s performance. This paper extends the work reported in a previous paper that considered only negative gravity and which showed a significant performance improvement over a range of optimized arrays. Still better results are obtained by adding to the mix Elitism and DTO. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6- element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimization problem, antenna or not, utilizing Central Force Optimization as the Global Search and Optimization engine will benefit by including all three extensions, probably substantially.

Study on the Characteristics of SOI DTMOS with Reverse Schottky Barriers

Silicon-on-insulator dynamic threshold voltage MOSFETs with TiSi2/pSi as reverse Schottky barriers （RSB） are presented. With this RSB scheme,DTMOS can operate beyond 0.7V, thus overcoming the drawback of DTMOS with the gate and body connected. The experimental results demonstrate that the threshold voltage in DT mode with an RSB is reduced by about 200mV at room temperature. SOI MOSFETs in DT mode with an RSB have advantages such as excellent subthreshold slope and high drivability over those under normal mode operation. The breakdown characteristics of SOI MOSFETs in the off-state are compared for the DT mode with RSB, floating body mode, normal mode.

Fast Iterative Closest Point-Simultaneous Localization and Mapping(ICP-SLAM)with Rough Alignment and Narrowing-Scale Nearby Searching

Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and global map cannot be previously known. As a result, ICP algorithm will take much amount of iterations to reach convergence. On the other hand,establishment of correspondence is done by global searching, which requires enormous computational time. To overcome the two problems,a fast ICP-SLAM with rough alignment and narrowing-scale nearby searching is proposed. As for the decrease of iterative times,rough alignment based on initial pose matrix is proposed. In detail,initial pose matrix is obtained by micro-electro-mechanical system( MEMS) magnetometer and global landmarks. Then rough alignment will be applied between current scan frame and global map at the beginning of ICP algorithm with initial pose matrix. As for accelerating the establishment of correspondence, narrowingscale nearby searching with dynamic threshold is proposed,where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM,the experimental results show that the amount of iteration for ICP algorithm to reach convergence reduces to 92. 34% and ICP algorithm runtime reduces to 98. 86% on average. In addition,computational cost is kept in a stable level due to the eliminating of the accumulation of computational consumption. Moreover,great improvement can also been achieved in SLAM quality and robustness.

Optimization of threshold for local spectrum sensing with energy detector

Spectrum sensing is the first step of cognitive radio （CR）. In this area, previous researches mostly consider distributed local nodes which are under identical channel conditions, hence uniform and fixed detection threshold is set with energy detector. However, the distributions of nodes in real environments are not quite the same. In this paper, the optimal threshold to minimize the total detection error over add＇itive white Gaussion noise （AWGN） channel is derived firstly. Then the dynamic threshold scheme is proposed to reduce the average total detection error. Simulations have shown that, with this scheme, sensing performance is improved.

THE DDTCI SWITCH ALGORITHM FOR ABR TRAFFIC CONTROL IN ATM NETWORKS

For the issue of flow control for Available Bit Rate (ABR) traffic in ATM network,a new improved Explicit Rate (ER) algorithm named Dynamic Double Threshold Congestion Indication (DDTCI) algorithm is presented based on the Explicit Forward Congestion Indication (EFCI) Current Cell Rate (CCR) algorithm and Relative Rate (RR) algorithm. Different from the early ER algorithm, both the high-level and the low-level threshold is dynamically changing according to the state of the bottleneck node. We determinate the congestion state with the information of the two dynamic threshold, and control the cell rate of the source by feed back mechanism. Except for the well performance in both link utilization and fairness in distribution of available bandwidth, the improved algorithm can alleviate the fluctuation of sending rate dramatically. The mechanism is modeled by a fluid model, and the useful expressions are derived.Simulation results show up our conclusion.

Extinction and Persistent of a Stochastic Multi-group SIR Epidemic Model

We establish a stochastic differential equation epidemic model of multi-group SIR type based on the deterministic multi-group SIR mode. Then, we define the basic reproduction number R0^S and show that it is a sharp threshold for the dynamic of the stochastic multi-group SIR model. More specially, if R0^S 〈 1, then the disease-free equilibrium will be asymptotically stable which means the disease will die out, if R0^S 〉 1, the disease-free equilibrium will unstable, and our model will positively recurrence to a positive domain which implies the persistence of our model. Numerical simulation examples are carried out to substantiate the analytical results.

High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold

Heavy-duty diesel vehicles are important sources of urban nitrogen oxides(NOx)in actual applications for environmental compliance,emitting more than 80%of NOx and more than 90%of particulate matter(PM)in total vehicle emissions.The detection and control of heavy-duty diesel emissions are critical for protecting public health.Currently,vehicles on the road must be regularly tested,every six months or once a year,to filter out high-emission mobile sources at vehicle inspection stations.However,it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections,and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions.An on-board diagnostic device(OBD)is installed inside the vehicle and can record the vehicle’s emission data in real time.In this paper,we propose a temporal optimization long short-term memory(LSTM)and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data,which can continuously track and record the emission status in real time.First,a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice.Then,the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria,calculated by an adaptive dynamic threshold with changing driving conditions.Finally,a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results.Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

Threshold dynamics of a cooperation-diffusion-advection model in open advective environments

This paper is concerned with a Lotka-Volterra cooperation-reaction-diffusion-advection model in open advective environments.It is found that there are two critical advection rates,which classify the dynamic behavior of this system into three different scenarios,namely,(i)both species go extinct;(ii)one species survives in the long run,the other goes extinct and(ii)both species can persistently survive.The theoretical results provide some interesting highlights in ecological protection in streams and rivers.

Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

An improved RRT∗algorithm,referred to as the AGP-RRT∗algorithm,is proposed to address the problems of poor directionality,long generated paths,and slow convergence speed in multi-axis robotic arm path planning.First,an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency.Second,a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm.Finally,the planning path is processed by pruning,removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm.Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform,the results show that the AGP-RRT∗algorithm reduces 87.34%in terms of the average running time and 40.39%in terms of the average path cost;Meanwhile,under two sets of complex environments A and B,the average running time of the AGP-RRT∗algorithm is shortened by 94.56%vs.95.37%,and the average path cost is reduced by 55.28%vs.47.82%,which proves the effectiveness of the AGP-RRT∗algorithm in improving the efficiency of multi-axis robotic arm path planning.

An Adaptive Threshold Dynamics Method for Three-Dimensional Wetting on Rough Surfaces

We propose an adaptive threshold dynamics method for wetting problems in three space dimensions.The method is based on solving a linear heat equation and a thresholding step in each iteration.The heat equation is discretized by a cell-centered finite volume method on an adaptively refined mesh.An efficient technique for volume conservation is developed on the nonuniform meshes based on a quick-sorting operation.By this method,we compute some interesting wetting problems on complicated surfaces.Numerical results verify some recent theory for the apparent contact angle on rough and chemically patterned surfaces.