The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a...The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.展开更多
The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch ...The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.展开更多
This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t...This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.展开更多
Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.展开更多
Full-waveform inversion(FWI)utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm.Since FWI combines mathematic inversion and full-wave...Full-waveform inversion(FWI)utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm.Since FWI combines mathematic inversion and full-wave equations,it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology.Unfortunately,conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets.By contrast,reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces.Restricted by acquisition geometries,refractions and turning waves in the record usually have limited penetration depth,which may not reach oil/gas reservoirs.Thus,reflections in the record are the only source that carries the information of these reservoirs.Consequently,it is meaningful to develop reflection-waveform inversion(RWI)that utilizes reflections to recover background velocity including the deep part of the model.This review paper includes:analyzing the weaknesses of FWI when inverting reflections;overviewing the principles of RWI,including separation of the tomography and migration components,the objective functions,constraints;summarizing the current status of the technique of RWI;outlooking the future of RWI.展开更多
The perception of a 3D space, in which movement takes place, is subjectively based on experience. The pedestrians＇ perception of subjective duration is one of the related issues that receive tittle attention in urban...The perception of a 3D space, in which movement takes place, is subjectively based on experience. The pedestrians＇ perception of subjective duration is one of the related issues that receive tittle attention in urban design Literature. Pedestrians often misperceive the required time to pass a certain distance. A wide range of factors affects one＇s perception of time in urban environments. These factors include individua（ factors （e.g., gender, age, and psychoLogicaL state）, social and cu（tural contexts, purpose and motivation for being in the space, and knowledge of the given area. This study aims to create an applied checklist that can be used by urban designers in analyzing the effects of individual experience on subjective duration. This checklist wilt enable urban designers to perform a phenomenotogicat assessment of time perception and compare this perception in different urban spaces, thereby improving pedestrians＇ experiences of time through a purposeful design. A combination of exploratory and descriptive anaLyticaL research is used as methodology due to the complexity of time perception.展开更多
The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these...The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.展开更多
The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet,as many employees have transitioned to working from home.Furthermo...The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet,as many employees have transitioned to working from home.Furthermore,with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network(VPN)or Tor Browser(dark web)to keep their data privacy and hidden,network traffic encryption is rapidly becoming a universal approach.This affects and complicates the quality of service(QoS),traffic monitoring,and network security provided by Internet Service Providers(ISPs),particularly for analysis and anomaly detection approaches based on the network traffic’s nature.The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services.Therefore,an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance.In this paper,the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets.Therefore,this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic.The proposed models utilize statistical features(SF),Pearson Correlation(PC),and a Genetic Algorithm(GA),preprocessing the traffic samples into net flow traffic to accomplish the experiment’s objectives.The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features.The PC-based method performs well in removing different features of network traffic.With a microsecond perpacket prediction time,the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task,a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach.This is extremely promising for the development of real-time traffic analyzers.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
Filtration efficiency of portable air cleaner(PAC)is affected by resident perceptions and adherences to when and how to operate the PAC.Incorporating PAC with smart control and sensor technology holds the promise to e...Filtration efficiency of portable air cleaner(PAC)is affected by resident perceptions and adherences to when and how to operate the PAC.Incorporating PAC with smart control and sensor technology holds the promise to effectively reduce indoor air pollutants.This study aims to evaluate the efficiency of a PAC at removing indoor fine particulate matters(PM_(2.5))exposure under two automated operation settings:(1)a time-based mode in which the operation time is determined based on perceived time periods of indoor pollution by residents;(2)a sensor-based mode in which an air sensor monitor is used to determine the PAC based on the actual PM_(2.5) level against the indoor air quality guideline.The study was conducted in a residential room for 55 days with a rolling setting on PAC(no filtration,sensor-based,time-based fil-trations)and a continuous measurement of PM_(2.5).We found that the PAC operated with sensor-based mode removed PM_(2.5) concentrations by 47%and prolonged clean air(<35 μg/m^(3))period by 23%compared to the purifications with time-based mode which reduced PM_(2.5) by 29%and increased clean air period by 13%.The sensor-based filtration identified indoor pollution episodes that are hardly detected by personal perceptions.Our study findings support an automated sensor-based approach to optimize the use of PAC for effectively reducing indoor PM_(2.5) exposure.展开更多
We have designed a piezoresistive detector to detect the displacement of an accelerometer.We have used a flexible contact force and impact time detector for sensing the acceleration in the time domain.The advantage of...We have designed a piezoresistive detector to detect the displacement of an accelerometer.We have used a flexible contact force and impact time detector for sensing the acceleration in the time domain.The advantage of using this mechanism is good linearity,compactness,scalability,and the potential to realize a higher precision accelerometer due to time-based measurement.The estimated mechanical and electrical parameters of beam detector are presented.We used COMSOL Multiphysics for designing the detector and Matlab for analysis.展开更多
On-the-go soil sensors measuring apparent electrical conductivity (EC<sub>a</sub>) in agricultural fields have provided valuable information to producers, consultants, and researchers on understanding soil...On-the-go soil sensors measuring apparent electrical conductivity (EC<sub>a</sub>) in agricultural fields have provided valuable information to producers, consultants, and researchers on understanding soil spatial patterns and their relationship with crop components. Nevertheless, more information is needed in Mississippi, USA, on the longevity of EC<sub>a</sub> measurements collected with an on-the-go soil sensor system. That information will be valuable to users interesting in employing the technology to assist them with management decisions. This study compared the spatial patterns of EC<sub>a</sub> data collected at two different periods to determine the temporal stability of map products derived from the data. The study focused on data collected in 2016 and 2021 from a field plot consisting of clay and loam soils. Apparent electrical conductivity shallow (0 - 30 cm) and deep (0 - 90 cm) measurements were obtained with a mobile system. Descriptive statistics, Pearson correlation analysis, paired t-test, and cluster analysis (k-means) were used to compare the data sets. Similar trends were evident in both datasets;apparent electrical conductivity deep measurements were greater (P 0.90) existed between the EC<sub>a</sub> shallow and deep measurements. Also, a high correlation (r ≥ 0.79) was observed between the EC<sub>a </sub>measurements and the y-coordinates recorded by a global positioning system, indicating a spatial trend in the north and south direction (vice versa) of the plot. Comparable spatial patterns were observed between the years in the EC<sub>a</sub> shallow and deep thematic maps developed via clustering. Apparent electrical conductivity data measurement patterns were consistent over the five years of this study. Thus the user has at least a five-year window from the first data collection to the next data collection to determine the relationship of the EC<sub>a</sub> data to other agronomic variables.展开更多
This paper describes a novel energy-efficient, high-speed ADC architecture combining a flash ADC and a TDC. A high conversion rate can be obtained owing to the flash coarse ADC, and low-power dissipation can be attain...This paper describes a novel energy-efficient, high-speed ADC architecture combining a flash ADC and a TDC. A high conversion rate can be obtained owing to the flash coarse ADC, and low-power dissipation can be attained using the TDC as a fine ADC. Moreover, a capacitive coupled ramp circuit is proposed to achieve high linearity. A test chip was fabricated using 65-nm digital CMOS technology. The test chip demonstrated a high sampling frequency of 500 MHz and a low-power dissipation of 2.0 mW, resulting in a low FOM of 32 fJ/conversion-step.展开更多
The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the esti...The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.展开更多
A new high order CMOS temperature compensated current reference is proposed in this paper, which is accomplished by two first order temperature compensation current references. The novel circuit exploits the temperatu...A new high order CMOS temperature compensated current reference is proposed in this paper, which is accomplished by two first order temperature compensation current references. The novel circuit exploits the temperature characteristics of integrated-circuit resistors and gate-source voltage of MOS transistors working in weak inversion. The proposed circuit, designed with a 0.6 Izm standard CMOS technology, gives a good temperature coefficient of 31ppm/℃ [-50-100℃] at a 1.8V supply, and also achieves line regulation of 0.01%/V and-120dB PSR at 1 MHz. Comparing with other presented work, the proposed circuit shows better temperature coefficient and Line regulation.展开更多
Purpose: Leagile manufacturing is one of the time-based manufacturing practices used to improve factory performance. It is a practice that combines initiatives of Lean and agile manufacturing under certain enabling co...Purpose: Leagile manufacturing is one of the time-based manufacturing practices used to improve factory performance. It is a practice that combines initiatives of Lean and agile manufacturing under certain enabling competences. Therefore, the purpose of this study is investigate the combinative nature of time-based manufacturing practices under unique enabling competences and their impact on performance of factories in Uganda. Methodology: Firstly, the underlying factor structure of competences and time-based manufacturing was examined was conducted using Principal Component Analysis (PCA). Enabling competences and time-base manufacturing practices were modelled and validated for each using confirmatory factor analysis, particularly composite reliability, average variance extracted and convergent validity. A fully fledged structural equation model was used to test the impact of leagile manufacturing on performance of factories. Findings: The study results revealed that time-based manufacturing of lean, and leagile are related but differ, in terms of their enabling competences and philosophical orientation. The findings also revealed that when small and medium factories in Uganda adopt leagile practice, they are likely not improve their performance. This is perhaps due to the fact that small and medium factories have inadequate resources. Practical Implications: The study findings shed more insights on the factors that enable adoption and implementation of time-based manufacturing practices. The extent to which these competences are orchestrated determines the benefits derived from the time-based manufacturing practices. In addition, small and medium enterprises should keenly make a choice on the appropriate practices that purposely reduce their lead time and cost of conversion. Originality: This study investigated the combinative nature of time-based manufacturing practices under unique enabling competences and their impact on performance of factories in Uganda. It is among the few studies that provide evidence on the leagile model anchored in the appropriate enabling competences in the context of developing countries. The empirical survey was done on small and medium factories to validate a leagile manufacturing model and tested its impact on factory performance.展开更多
In real life,we are often motivated to plan things to be performed at specific times in the future.Some of these intended actions help other individuals,and thus involve time-based prospective memory(TBPM)under prosoc...In real life,we are often motivated to plan things to be performed at specific times in the future.Some of these intended actions help other individuals,and thus involve time-based prospective memory(TBPM)under prosocial motivational conditions.Children’s social development is very rapid,and they have relatively stable prosocial motivation during school age.Few studies have paid attention to this issue.This study focuses on three aspects of this issue:(1)the impact of prosocial motivation on the TBPM of school-age children,(2)whether there are sex differences in this effect,and,for the first time,(3)the processing mechanism by which prosocial motivation affects TBPM in school-age children in the framework of the motivation cognitive model.A total of 112 elementary school students,aged between 8 and 12,participated in the experiment,using a 2(group:prosocial motivation,control)×2(sex:boy,girl)between-subjects design.The results showed that prosocial motivation can significantly reduce children’s time difference of TBPM.However,we found no sex differences in the effect of prosocial motivation on TBPM in the above two indicators.With regard to the processing mechanism,we found that the prosocial motivation group paid more attention to external time information throughout the experiment.However,their internal attention and the effectiveness of attention did not improve.These results partially support the motivation cognitive model.Overall,this study found that prosocial motivation relies mainly on external attention to improve the TBPM performance of school-age children.展开更多
Time-based prospective memory(TBPM)is affected by many factors,which include Type A and Type B personality types.Type A individuals have a strong desire to complete tasks and a strong sense of time-urgency to complete...Time-based prospective memory(TBPM)is affected by many factors,which include Type A and Type B personality types.Type A individuals have a strong desire to complete tasks and a strong sense of time-urgency to complete established tasks before the deadline.Type B individuals have fewer time constraints and usually procrastinate until the deadline to complete the task.Compared with Type B individuals,Type A individuals may perform better in TBPM due to their advantages in time cognition and attitude.This study explores the differences in the TBPM ability between Type A individuals and Type B individuals under different time monitoring conditions.In Experiment 1,there was no limit to how many times participants could check the time.The results showed that the performance of TBPM between Type A individuals and Type B individuals was not different.In Experiment 2,participants could only check the time once during each TBPM task.The results showed that,compared to Type B individuals,Type A individuals performed better in TBPM,with higher time monitoring frequency and slower response speed to the ongoing tasks.These findings suggest that the performance of Type A individuals in TBPM has an advantage only under the restricted time monitoring condition.This advantage is then mainly due to the increase in the attention consumption of Type A individuals in both internal and external attention.展开更多
Time-based prospective memory(TBPM)is the ability to remember to do a planned task at the right time.In social interactions,people are often motivated to do things for others,which reflects an important factor that in...Time-based prospective memory(TBPM)is the ability to remember to do a planned task at the right time.In social interactions,people are often motivated to do things for others,which reflects an important factor that influences prospective memory,namely prosocial motivation.According to the motivational cognitive model,prosocial motivation promotes TBPM by paying more attention or adopting more effective strategies.This study explored the effect of prosocial motivation on TBPM under different time-monitoring conditions within the motivational cognitive model framework.One hundred and thirty-one university students participated in this experiment that adopted a 2(groups:control,prosocial motivation)×2(viewing time conditions:limited,unlimited)between-subjects design.The results revealed that the prosocial motivation group had better TBPM performance than the control group under both limited and unlimited viewing time conditions.At the same time,compared with the control group,the prosocial motivation group consumed more internal attention and utilized more strategies under both viewing time conditions,and their external attention was more effective.In addition,the external attention of the prosocial motivation group was higher only when time-monitoring was unlimited.The results of this study further extend knowledge of the motivational cognitive model and expand its scope of application,which has theoretical significance.展开更多
This paper introduces a novel inventory model tailored for retailing systems aligned with the Product Life Cycle(PLC),encompassing the introduction-growth,maturity,and decline stages.Diverging from previous inventory ...This paper introduces a novel inventory model tailored for retailing systems aligned with the Product Life Cycle(PLC),encompassing the introduction-growth,maturity,and decline stages.Diverging from previous inventory models that integrate sales incentive policies without consideration of PLC characteristics,this study pioneers an integrated framework.The paper introduces an innovative decreasing time-based discount policy tailored for the introduction-growth stage.Following that,a quantity discount policy is devised for the maturity phase,while a delay payment policy and an incremental time-based discount policy are formulated for the decline phase.Recognizing the product’s heightened market recognition during the maturity phase,a partial backorder system with variable shortage costs is proposed to address shortages.The findings indicate that extending the duration of the maturity phase,along with elevating the costs associated with each unit of shortage in this phase,results in an augmentation of the retailer’s profit.展开更多
基金Supported by the National Natural Science Foun-dation of China (60403027) Natural Science Foundation of HubeiProvince (2005ABA258) Open Foundation of State Key Labora-tory of Software Engineering (SKLSE05-07)
文摘The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.
基金Supported by the USDA Cooperative State Research,Education and Extension Service,Hatch Project(No.0210510)the National Natural Science Foundations of China(Nos.31270527,40801225)+1 种基金the Natural Science Foundation of Zhejiang Province(No.LY13D010005)the Young Academic Leaders Climbing Program of Zhejiang Province(No.pd2013222)
文摘The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.
基金supported by the National Key Research and Development Program of China(2025YFE0213100)the National Natural Science Foundation of China(62422315,62573348)+1 种基金the Natural Science Basic Research Program of Shaanxi(2025JC-YBMS-667)the“Shuang Yi Liu”Construction Foundation(25GH02010366)。
文摘This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
基金supported by National Key R&D Program of China(No.2018YFA0702502)NSFC(Grant No.41974142)Science Foundation of China University of petroleum,Beijing(No.2462019YJRC005).
文摘Full-waveform inversion(FWI)utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm.Since FWI combines mathematic inversion and full-wave equations,it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology.Unfortunately,conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets.By contrast,reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces.Restricted by acquisition geometries,refractions and turning waves in the record usually have limited penetration depth,which may not reach oil/gas reservoirs.Thus,reflections in the record are the only source that carries the information of these reservoirs.Consequently,it is meaningful to develop reflection-waveform inversion(RWI)that utilizes reflections to recover background velocity including the deep part of the model.This review paper includes:analyzing the weaknesses of FWI when inverting reflections;overviewing the principles of RWI,including separation of the tomography and migration components,the objective functions,constraints;summarizing the current status of the technique of RWI;outlooking the future of RWI.
文摘The perception of a 3D space, in which movement takes place, is subjectively based on experience. The pedestrians＇ perception of subjective duration is one of the related issues that receive tittle attention in urban design Literature. Pedestrians often misperceive the required time to pass a certain distance. A wide range of factors affects one＇s perception of time in urban environments. These factors include individua（ factors （e.g., gender, age, and psychoLogicaL state）, social and cu（tural contexts, purpose and motivation for being in the space, and knowledge of the given area. This study aims to create an applied checklist that can be used by urban designers in analyzing the effects of individual experience on subjective duration. This checklist wilt enable urban designers to perform a phenomenotogicat assessment of time perception and compare this perception in different urban spaces, thereby improving pedestrians＇ experiences of time through a purposeful design. A combination of exploratory and descriptive anaLyticaL research is used as methodology due to the complexity of time perception.
基金This work was supported by Research on the Influences of Network Security Threat Intelligence on Sichuan Government and Enterprises and the Development Countermeasure(Project ID 2018ZR0220)Research on Key Technologies of Network Security Protection in Intelligent Vehicle Based on(Project ID 2018JY0510)+3 种基金the Research on Abnormal Behavior Detection Technology of Automotive CAN Bus Based on Information Entropy(Project ID 2018Z105)the Research on the Training Mechanism of Driverless Network Safety Talents for Sichuan Auto Industry Based on Industry-University Synergy(Project ID 18RKX0667),Research and implementation of traffic cooperative perception and traffic signal optimization of main road(Project ID 2018YF0500707SN)Research and implementation of intelligent traffic control and monitoring system(Project ID 2019YGG0201)Remote upgrade system of intelligent vehicle software(Project ID 2018GZDZX0011).
文摘The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.
文摘The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet,as many employees have transitioned to working from home.Furthermore,with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network(VPN)or Tor Browser(dark web)to keep their data privacy and hidden,network traffic encryption is rapidly becoming a universal approach.This affects and complicates the quality of service(QoS),traffic monitoring,and network security provided by Internet Service Providers(ISPs),particularly for analysis and anomaly detection approaches based on the network traffic’s nature.The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services.Therefore,an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance.In this paper,the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets.Therefore,this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic.The proposed models utilize statistical features(SF),Pearson Correlation(PC),and a Genetic Algorithm(GA),preprocessing the traffic samples into net flow traffic to accomplish the experiment’s objectives.The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features.The PC-based method performs well in removing different features of network traffic.With a microsecond perpacket prediction time,the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task,a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach.This is extremely promising for the development of real-time traffic analyzers.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金supported by the start-up funding of University at Buffalo.
文摘Filtration efficiency of portable air cleaner(PAC)is affected by resident perceptions and adherences to when and how to operate the PAC.Incorporating PAC with smart control and sensor technology holds the promise to effectively reduce indoor air pollutants.This study aims to evaluate the efficiency of a PAC at removing indoor fine particulate matters(PM_(2.5))exposure under two automated operation settings:(1)a time-based mode in which the operation time is determined based on perceived time periods of indoor pollution by residents;(2)a sensor-based mode in which an air sensor monitor is used to determine the PAC based on the actual PM_(2.5) level against the indoor air quality guideline.The study was conducted in a residential room for 55 days with a rolling setting on PAC(no filtration,sensor-based,time-based fil-trations)and a continuous measurement of PM_(2.5).We found that the PAC operated with sensor-based mode removed PM_(2.5) concentrations by 47%and prolonged clean air(<35 μg/m^(3))period by 23%compared to the purifications with time-based mode which reduced PM_(2.5) by 29%and increased clean air period by 13%.The sensor-based filtration identified indoor pollution episodes that are hardly detected by personal perceptions.Our study findings support an automated sensor-based approach to optimize the use of PAC for effectively reducing indoor PM_(2.5) exposure.
文摘We have designed a piezoresistive detector to detect the displacement of an accelerometer.We have used a flexible contact force and impact time detector for sensing the acceleration in the time domain.The advantage of using this mechanism is good linearity,compactness,scalability,and the potential to realize a higher precision accelerometer due to time-based measurement.The estimated mechanical and electrical parameters of beam detector are presented.We used COMSOL Multiphysics for designing the detector and Matlab for analysis.
文摘On-the-go soil sensors measuring apparent electrical conductivity (EC<sub>a</sub>) in agricultural fields have provided valuable information to producers, consultants, and researchers on understanding soil spatial patterns and their relationship with crop components. Nevertheless, more information is needed in Mississippi, USA, on the longevity of EC<sub>a</sub> measurements collected with an on-the-go soil sensor system. That information will be valuable to users interesting in employing the technology to assist them with management decisions. This study compared the spatial patterns of EC<sub>a</sub> data collected at two different periods to determine the temporal stability of map products derived from the data. The study focused on data collected in 2016 and 2021 from a field plot consisting of clay and loam soils. Apparent electrical conductivity shallow (0 - 30 cm) and deep (0 - 90 cm) measurements were obtained with a mobile system. Descriptive statistics, Pearson correlation analysis, paired t-test, and cluster analysis (k-means) were used to compare the data sets. Similar trends were evident in both datasets;apparent electrical conductivity deep measurements were greater (P 0.90) existed between the EC<sub>a</sub> shallow and deep measurements. Also, a high correlation (r ≥ 0.79) was observed between the EC<sub>a </sub>measurements and the y-coordinates recorded by a global positioning system, indicating a spatial trend in the north and south direction (vice versa) of the plot. Comparable spatial patterns were observed between the years in the EC<sub>a</sub> shallow and deep thematic maps developed via clustering. Apparent electrical conductivity data measurement patterns were consistent over the five years of this study. Thus the user has at least a five-year window from the first data collection to the next data collection to determine the relationship of the EC<sub>a</sub> data to other agronomic variables.
文摘This paper describes a novel energy-efficient, high-speed ADC architecture combining a flash ADC and a TDC. A high conversion rate can be obtained owing to the flash coarse ADC, and low-power dissipation can be attained using the TDC as a fine ADC. Moreover, a capacitive coupled ramp circuit is proposed to achieve high linearity. A test chip was fabricated using 65-nm digital CMOS technology. The test chip demonstrated a high sampling frequency of 500 MHz and a low-power dissipation of 2.0 mW, resulting in a low FOM of 32 fJ/conversion-step.
文摘The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.
文摘A new high order CMOS temperature compensated current reference is proposed in this paper, which is accomplished by two first order temperature compensation current references. The novel circuit exploits the temperature characteristics of integrated-circuit resistors and gate-source voltage of MOS transistors working in weak inversion. The proposed circuit, designed with a 0.6 Izm standard CMOS technology, gives a good temperature coefficient of 31ppm/℃ [-50-100℃] at a 1.8V supply, and also achieves line regulation of 0.01%/V and-120dB PSR at 1 MHz. Comparing with other presented work, the proposed circuit shows better temperature coefficient and Line regulation.
文摘Purpose: Leagile manufacturing is one of the time-based manufacturing practices used to improve factory performance. It is a practice that combines initiatives of Lean and agile manufacturing under certain enabling competences. Therefore, the purpose of this study is investigate the combinative nature of time-based manufacturing practices under unique enabling competences and their impact on performance of factories in Uganda. Methodology: Firstly, the underlying factor structure of competences and time-based manufacturing was examined was conducted using Principal Component Analysis (PCA). Enabling competences and time-base manufacturing practices were modelled and validated for each using confirmatory factor analysis, particularly composite reliability, average variance extracted and convergent validity. A fully fledged structural equation model was used to test the impact of leagile manufacturing on performance of factories. Findings: The study results revealed that time-based manufacturing of lean, and leagile are related but differ, in terms of their enabling competences and philosophical orientation. The findings also revealed that when small and medium factories in Uganda adopt leagile practice, they are likely not improve their performance. This is perhaps due to the fact that small and medium factories have inadequate resources. Practical Implications: The study findings shed more insights on the factors that enable adoption and implementation of time-based manufacturing practices. The extent to which these competences are orchestrated determines the benefits derived from the time-based manufacturing practices. In addition, small and medium enterprises should keenly make a choice on the appropriate practices that purposely reduce their lead time and cost of conversion. Originality: This study investigated the combinative nature of time-based manufacturing practices under unique enabling competences and their impact on performance of factories in Uganda. It is among the few studies that provide evidence on the leagile model anchored in the appropriate enabling competences in the context of developing countries. The empirical survey was done on small and medium factories to validate a leagile manufacturing model and tested its impact on factory performance.
基金General Project of Humanities and Social Science Research in Colleges and Universities of Henan Province,Grant/Award Number:2023-ZZJH-307Philosophy and Social Science Planning Project of Henan Province,Grant/Award Number:2022CJY045Scientific research laboratory(platform)open project for undergraduates of Henan University,Grant/Award Number:20221404070。
文摘In real life,we are often motivated to plan things to be performed at specific times in the future.Some of these intended actions help other individuals,and thus involve time-based prospective memory(TBPM)under prosocial motivational conditions.Children’s social development is very rapid,and they have relatively stable prosocial motivation during school age.Few studies have paid attention to this issue.This study focuses on three aspects of this issue:(1)the impact of prosocial motivation on the TBPM of school-age children,(2)whether there are sex differences in this effect,and,for the first time,(3)the processing mechanism by which prosocial motivation affects TBPM in school-age children in the framework of the motivation cognitive model.A total of 112 elementary school students,aged between 8 and 12,participated in the experiment,using a 2(group:prosocial motivation,control)×2(sex:boy,girl)between-subjects design.The results showed that prosocial motivation can significantly reduce children’s time difference of TBPM.However,we found no sex differences in the effect of prosocial motivation on TBPM in the above two indicators.With regard to the processing mechanism,we found that the prosocial motivation group paid more attention to external time information throughout the experiment.However,their internal attention and the effectiveness of attention did not improve.These results partially support the motivation cognitive model.Overall,this study found that prosocial motivation relies mainly on external attention to improve the TBPM performance of school-age children.
基金Henan Philosophy and Social Science Planning Project,Grant/Award Number:2020BJY010Post-funded Project of National Social Science Fund,Grant/Award Number:20FJKB005。
文摘Time-based prospective memory(TBPM)is affected by many factors,which include Type A and Type B personality types.Type A individuals have a strong desire to complete tasks and a strong sense of time-urgency to complete established tasks before the deadline.Type B individuals have fewer time constraints and usually procrastinate until the deadline to complete the task.Compared with Type B individuals,Type A individuals may perform better in TBPM due to their advantages in time cognition and attitude.This study explores the differences in the TBPM ability between Type A individuals and Type B individuals under different time monitoring conditions.In Experiment 1,there was no limit to how many times participants could check the time.The results showed that the performance of TBPM between Type A individuals and Type B individuals was not different.In Experiment 2,participants could only check the time once during each TBPM task.The results showed that,compared to Type B individuals,Type A individuals performed better in TBPM,with higher time monitoring frequency and slower response speed to the ongoing tasks.These findings suggest that the performance of Type A individuals in TBPM has an advantage only under the restricted time monitoring condition.This advantage is then mainly due to the increase in the attention consumption of Type A individuals in both internal and external attention.
基金Philosophy and Social Science Planning Project of Henan Province,Grant/Award Numbers:2022CJY045,2022CJY047。
文摘Time-based prospective memory(TBPM)is the ability to remember to do a planned task at the right time.In social interactions,people are often motivated to do things for others,which reflects an important factor that influences prospective memory,namely prosocial motivation.According to the motivational cognitive model,prosocial motivation promotes TBPM by paying more attention or adopting more effective strategies.This study explored the effect of prosocial motivation on TBPM under different time-monitoring conditions within the motivational cognitive model framework.One hundred and thirty-one university students participated in this experiment that adopted a 2(groups:control,prosocial motivation)×2(viewing time conditions:limited,unlimited)between-subjects design.The results revealed that the prosocial motivation group had better TBPM performance than the control group under both limited and unlimited viewing time conditions.At the same time,compared with the control group,the prosocial motivation group consumed more internal attention and utilized more strategies under both viewing time conditions,and their external attention was more effective.In addition,the external attention of the prosocial motivation group was higher only when time-monitoring was unlimited.The results of this study further extend knowledge of the motivational cognitive model and expand its scope of application,which has theoretical significance.
文摘This paper introduces a novel inventory model tailored for retailing systems aligned with the Product Life Cycle(PLC),encompassing the introduction-growth,maturity,and decline stages.Diverging from previous inventory models that integrate sales incentive policies without consideration of PLC characteristics,this study pioneers an integrated framework.The paper introduces an innovative decreasing time-based discount policy tailored for the introduction-growth stage.Following that,a quantity discount policy is devised for the maturity phase,while a delay payment policy and an incremental time-based discount policy are formulated for the decline phase.Recognizing the product’s heightened market recognition during the maturity phase,a partial backorder system with variable shortage costs is proposed to address shortages.The findings indicate that extending the duration of the maturity phase,along with elevating the costs associated with each unit of shortage in this phase,results in an augmentation of the retailer’s profit.
