The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
An augmented flight dynamics model is developed to extend the existing flight dynamics model of tilt-rotor aircraft for optimal landing procedure analysis in the event of one engine failure.Compared with the existing ...An augmented flight dynamics model is developed to extend the existing flight dynamics model of tilt-rotor aircraft for optimal landing procedure analysis in the event of one engine failure.Compared with the existing flight dynamics model, the augmented model involves with more pilot control information in cockpit and is validated against the flight test data. Based on the augmented flight dynamics model, the optimal landing procedure of XV-15 tilt-rotor aircraft after one engine failure is formulated into a Nonlinear Optimal Control Problem(NOCP), solved by collocation and numerical optimization method. The time histories of pilot controls in cockpit during the optimal landing procedure are obtained for the evaluation of pilot workload. An evaluation method which can synthetically quantify the pilot workload in time and frequency domains is proposed with metrics of aggressiveness and cutoff frequencies of pilot controls. The scale of the pilot workload is compared with those of the shipboard landing procedures, bob-up/bob-down and dash/quickstop maneuvers of UH-60 helicopter. The results show that the aggressiveness of pilot collective and longitudinal controls for the tilt-rotor aircraft optimal landing procedure after one engine failure are higher than those for UH-60 helicopter shipboard landing procedures up to the condition of sea state 4, while the pilot cutoff frequency of collective control is lower than that of the bob-up/bob-down maneuver but the pilot cutoff frequency of longitudinal control is higher than that of the dash/quick-stop maneuver. The evaluated pilot workload level is between Cooper–Harper HQR Level 2 and Level 3.展开更多
With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out ...With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.展开更多
This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of per...This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of performance index,path constraints and boundary conditions based on an augmented six-degree-of-freedom rigid-body flight dynamics model,solved by collocation and numerical optimization method.UH-60 A helicopter is taken as the sample for the demonstration of landing after one engine failure.The numerical simulation was conducted to find the trajectory of helicopter and the controls from pilot for landing after one engine failure with different performance index considering the factor of pilot workload.The reasonable performance index and corresponding landing trajectory and controls are obtained by making a comparison with those from the flight test data.Furthermore,the pilot workload is evaluated based on wavelet transform analysis of the pilot control activities.The workloads of pilot control activities for collective control,longitudinal and lateral cyclic controls and pedal control during the helicopter landing after one engine failure are examined and compared with those of flight test.The results show that when the performance index considers the factor of pilot workload properly,the characteristics of amplitudes and constituent frequencies of pilot control inputs in the optimal solution are consistent with those of the pilot control inputs in the flight test.Therefore,the proposed method provides a tool of predicting the pilot workload in helicopter landing after one engine failure.展开更多
To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migr...To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migration algorithm using Workload-aware Consolidation Technique(ELMWCT).As opposed to traditional energy-aware scheduling algorithms,which often focus on only one-dimensional resource,the two algorithms are based on the fact that multiple resources(such as CPU,memory and network bandwidth)are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics.Both algorithms investigate the problem of consolidating heterogeneous workloads.They try to execute all Virtual Machines(VMs) with the minimum amount of Physical Machines(PMs),and then power off unused physical servers to reduce power consumption.Simulation results show that both algorithms efficiently utilise the resources in cloud data centres,and the multidimensional resources have good balanced utilizations,which demonstrate their promising energy saving capability.展开更多
A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which i...A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.展开更多
In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive work...In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive workload data during an A320 takeoff within a simulator. Experiments were performed during which we collected 136 takeoffs across 13 pilots for more than 9 hours of time-series data. Moreover, this paper investigates the correlations between heart rate, pupil dilation, and cognitive workload during such exercise and found that a spike in cognitive load during a critical moment, such as an engine failure, augments a pilot’s heart rate and pupil dilation. Results show that a critical moment within a takeoff procedure increases a pilot’s cognitive load. Next, we used a stacked-LSTM model to predict cognitive workload 5 seconds into the future. The model was able to produce accurate predictions.展开更多
Objectives:The study aimed to the multicenter application of a nursing workload measurement scale in the internal medicine and surgery adults hospitalization units.Methods:The study design was a multicenter,observatio...Objectives:The study aimed to the multicenter application of a nursing workload measurement scale in the internal medicine and surgery adults hospitalization units.Methods:The study design was a multicenter,observational,and descriptive study.A multicenter application of the MIDENF®nursing workload measurement scale was carried out,which consists of 21 items,and covers the four nursing functions(patient care items,teaching,manager,and researcher),in units of hospitalization of adults of internal medicine and surgery of four different hospitals.Each item contains one or more of the nursing interventions of Nursing Interventions Classification(NIC)and has an assigned time,after comparing the real time it takes to perform each intervention with the North American Nursing Diagnosis Association(NANDA)standardized time.The study was carried out during nine months of the year 2020,measuring two days each month in the three work shifts(morning,evening,and night)to all patients admitted on the days of measurement in the indicated units.Results:The descriptive and inferential analysis of 11,756 completed scales,5,695 in general surgery and 6,061 in internal medicine,showed a greater care load for the two units during the morning shift(227,034 min in general surgery,261,835 min in internal medicine),especially in the items of“self-care”,“medication”,“common invasive procedures”,“fluid therapy”,and“patient and family support”,while the managerial function was similar during the three work shifts in the two units studied,getting values between 57,348 and 62,901min.In the analysis by shift and unit,statistical significance was obtained both for the total workload and the four nursing functions(P<0.001).Conclusions:It is shown that the use of validated scales with the standardized language of nursing functions,adapted to the units,provides objective information to adjust the nursing staff to the real situation of care in any hospital and unit where it is applied,improving quality and patient safety.展开更多
We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models - benchmar...We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models - benchmark application and workload models are described. We give the design and implementation of a synthetic workload generator that accepts the benchmark and workload model specifications generated by the characterization and modeling of workloads of cloud computing applications. We propose the Georgia Tech Cloud Workload Specification Language (GT-CWSL) that provides a structured way for specification of application workloads. The GT-CWSL combines the specifications of benchmark and workload models to create workload specifications that are used by a synthetic workload generator to generate synthetic workloads for performance evaluation of cloud computing applications.展开更多
The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it...The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.展开更多
A consensus has been reached that the tanks need to be integrated into the informatization battlefield. With the development of technology,the tank crew has being gradually decreased, so the research on two-soldier cr...A consensus has been reached that the tanks need to be integrated into the informatization battlefield. With the development of technology,the tank crew has being gradually decreased, so the research on two-soldier crew tank has become a hotspot. The workload of tank crew under the conditions of informatization is analyzed based on the combat mission of tank and the typical combat scenarios, and the impact of new technologies on workload is evaluated. The crew members in tank can be reduced from three to two, but it is necessary to substantially improve the automation of target search and the reliability of each subsystem and component.展开更多
Exposure to sound,heat,and increased physical workload can change physiological parameters.This study was conducted to evaluate the effect of concomitant exposure to sound,heat,and physical workload changes on physiol...Exposure to sound,heat,and increased physical workload can change physiological parameters.This study was conducted to evaluate the effect of concomitant exposure to sound,heat,and physical workload changes on physiological parameters in controlled laboratory conditions.This experimental crosssectional study was conducted in 35 male university students with a mean age of 25.75 years and a mean BMI of 22.69 kg/m2.Systolic and diastolic blood pressure and heart rate were measured after 15 min rest in the laboratory,5 and 10 min after starting the experiment,and then after 20 min in controlled laboratory conditions in five combination modes.The combination modes were(Sound:65 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%),(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:32°C,Speed:1.7,Slope:10%),and(Sound:95 dB,WBGT:32°C,Speed:3.4,Slope:14%).Mixed model analysis and paired t-test were applied for analysis.The results showed that the mean physiological parameters(Systolic and diastolic blood pressure and heart rate)increased when different combination modes worsened(Sound from 65 to 95 dB,WBGT from 22°C to 32°C,speed from 1.7 to 3.4,and slope from 10%to 14%,and when sound:95 dB,WBGT:32°C,Speed:3.4,and Slope:14%).Moreover,the mean changes of systolic and diastolic blood pressure were significant in all conditions when compared with the reference condition(Sound:65 dB,WBGT:22°C,Speed:1.7,and Slope:10%).The mean heart rate changes were also significant except for exposure to the second condition(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%)and the third condition(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%).Exposure to hazardous levels of sound,heat,and workload has adverse effects on physiological parameters.Concomitant exposure to all three hazards has a synergistic effect and increases the adverse effect.展开更多
We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report dat...We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.展开更多
Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmet...Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload.In addition,a finite impulse response(FIR)filter,independent component analysis(ICA),and multiple artifact rejection algorithms(MARAs)were used to filter event-related potentials(ERPs).Then,the data consisting of ERPs,subjective ratings of mental workload,and task performance,were analyzed through the use of variance and Spearman’s correlation during a simulated computer task.We found that participants responded faster and performed better in the easy task condition,followed by the medium and high-difficulty conditions,which verifies the validity of the ERP filtering.Moreover,larger P2 and P3 waveforms were evoked as the task difficulty increased,and a higher task difficulty elicited a more enhanced N300.Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings,and a positive relationship between the P3 amplitude and accuracy.The results presented in this paper demonstrate that a combination of FIR,ICA,and MARA methods can filter ERPs in the non-invasive real-time measurement of workload.Additionally,frontocentral P2,N3,and parietal P3 components showed differences between genders.The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.展开更多
Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell...Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.展开更多
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金supported by the National Natural Science Foundation of China (No. 11672128)
文摘An augmented flight dynamics model is developed to extend the existing flight dynamics model of tilt-rotor aircraft for optimal landing procedure analysis in the event of one engine failure.Compared with the existing flight dynamics model, the augmented model involves with more pilot control information in cockpit and is validated against the flight test data. Based on the augmented flight dynamics model, the optimal landing procedure of XV-15 tilt-rotor aircraft after one engine failure is formulated into a Nonlinear Optimal Control Problem(NOCP), solved by collocation and numerical optimization method. The time histories of pilot controls in cockpit during the optimal landing procedure are obtained for the evaluation of pilot workload. An evaluation method which can synthetically quantify the pilot workload in time and frequency domains is proposed with metrics of aggressiveness and cutoff frequencies of pilot controls. The scale of the pilot workload is compared with those of the shipboard landing procedures, bob-up/bob-down and dash/quickstop maneuvers of UH-60 helicopter. The results show that the aggressiveness of pilot collective and longitudinal controls for the tilt-rotor aircraft optimal landing procedure after one engine failure are higher than those for UH-60 helicopter shipboard landing procedures up to the condition of sea state 4, while the pilot cutoff frequency of collective control is lower than that of the bob-up/bob-down maneuver but the pilot cutoff frequency of longitudinal control is higher than that of the dash/quick-stop maneuver. The evaluated pilot workload level is between Cooper–Harper HQR Level 2 and Level 3.
基金supported by the National Basic Research Program of China (No. 2010CB734104)
文摘With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals（SDNN） significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future.
基金supported by the National Natural Science Foundation of China(No.11672128)。
文摘This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of performance index,path constraints and boundary conditions based on an augmented six-degree-of-freedom rigid-body flight dynamics model,solved by collocation and numerical optimization method.UH-60 A helicopter is taken as the sample for the demonstration of landing after one engine failure.The numerical simulation was conducted to find the trajectory of helicopter and the controls from pilot for landing after one engine failure with different performance index considering the factor of pilot workload.The reasonable performance index and corresponding landing trajectory and controls are obtained by making a comparison with those from the flight test data.Furthermore,the pilot workload is evaluated based on wavelet transform analysis of the pilot control activities.The workloads of pilot control activities for collective control,longitudinal and lateral cyclic controls and pedal control during the helicopter landing after one engine failure are examined and compared with those of flight test.The results show that when the performance index considers the factor of pilot workload properly,the characteristics of amplitudes and constituent frequencies of pilot control inputs in the optimal solution are consistent with those of the pilot control inputs in the flight test.Therefore,the proposed method provides a tool of predicting the pilot workload in helicopter landing after one engine failure.
基金supported by the Opening Project of State key Laboratory of Networking and Switching Technology under Grant No.SKLNST-2010-1-03the National Natural Science Foundation of China under Grants No.U1333113,No.61303204+1 种基金the Sichuan Province seedling project under Grant No.2012ZZ036the Scientific Research Fund of Sichuan Normal University under Grant No.13KYL06
文摘To reduce energy consumption in cloud data centres,in this paper,we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique(ESWCT) and the Energyaware Live Migration algorithm using Workload-aware Consolidation Technique(ELMWCT).As opposed to traditional energy-aware scheduling algorithms,which often focus on only one-dimensional resource,the two algorithms are based on the fact that multiple resources(such as CPU,memory and network bandwidth)are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics.Both algorithms investigate the problem of consolidating heterogeneous workloads.They try to execute all Virtual Machines(VMs) with the minimum amount of Physical Machines(PMs),and then power off unused physical servers to reduce power consumption.Simulation results show that both algorithms efficiently utilise the resources in cloud data centres,and the multidimensional resources have good balanced utilizations,which demonstrate their promising energy saving capability.
基金The National Natural Science Foundation of China (60742117)
文摘A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.
文摘In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive workload data during an A320 takeoff within a simulator. Experiments were performed during which we collected 136 takeoffs across 13 pilots for more than 9 hours of time-series data. Moreover, this paper investigates the correlations between heart rate, pupil dilation, and cognitive workload during such exercise and found that a spike in cognitive load during a critical moment, such as an engine failure, augments a pilot’s heart rate and pupil dilation. Results show that a critical moment within a takeoff procedure increases a pilot’s cognitive load. Next, we used a stacked-LSTM model to predict cognitive workload 5 seconds into the future. The model was able to produce accurate predictions.
文摘Objectives:The study aimed to the multicenter application of a nursing workload measurement scale in the internal medicine and surgery adults hospitalization units.Methods:The study design was a multicenter,observational,and descriptive study.A multicenter application of the MIDENF®nursing workload measurement scale was carried out,which consists of 21 items,and covers the four nursing functions(patient care items,teaching,manager,and researcher),in units of hospitalization of adults of internal medicine and surgery of four different hospitals.Each item contains one or more of the nursing interventions of Nursing Interventions Classification(NIC)and has an assigned time,after comparing the real time it takes to perform each intervention with the North American Nursing Diagnosis Association(NANDA)standardized time.The study was carried out during nine months of the year 2020,measuring two days each month in the three work shifts(morning,evening,and night)to all patients admitted on the days of measurement in the indicated units.Results:The descriptive and inferential analysis of 11,756 completed scales,5,695 in general surgery and 6,061 in internal medicine,showed a greater care load for the two units during the morning shift(227,034 min in general surgery,261,835 min in internal medicine),especially in the items of“self-care”,“medication”,“common invasive procedures”,“fluid therapy”,and“patient and family support”,while the managerial function was similar during the three work shifts in the two units studied,getting values between 57,348 and 62,901min.In the analysis by shift and unit,statistical significance was obtained both for the total workload and the four nursing functions(P<0.001).Conclusions:It is shown that the use of validated scales with the standardized language of nursing functions,adapted to the units,provides objective information to adjust the nursing staff to the real situation of care in any hospital and unit where it is applied,improving quality and patient safety.
文摘We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models - benchmark application and workload models are described. We give the design and implementation of a synthetic workload generator that accepts the benchmark and workload model specifications generated by the characterization and modeling of workloads of cloud computing applications. We propose the Georgia Tech Cloud Workload Specification Language (GT-CWSL) that provides a structured way for specification of application workloads. The GT-CWSL combines the specifications of benchmark and workload models to create workload specifications that are used by a synthetic workload generator to generate synthetic workloads for performance evaluation of cloud computing applications.
文摘The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.
文摘A consensus has been reached that the tanks need to be integrated into the informatization battlefield. With the development of technology,the tank crew has being gradually decreased, so the research on two-soldier crew tank has become a hotspot. The workload of tank crew under the conditions of informatization is analyzed based on the combat mission of tank and the typical combat scenarios, and the impact of new technologies on workload is evaluated. The crew members in tank can be reduced from three to two, but it is necessary to substantially improve the automation of target search and the reliability of each subsystem and component.
基金The authors sincerely thank the deputy of research and technology affiliated to Shiraz University of Medical Sciences for financial support from this study,in the form of a research project by Mr.Hossein Molaeifar approved by the University on No.10652.
文摘Exposure to sound,heat,and increased physical workload can change physiological parameters.This study was conducted to evaluate the effect of concomitant exposure to sound,heat,and physical workload changes on physiological parameters in controlled laboratory conditions.This experimental crosssectional study was conducted in 35 male university students with a mean age of 25.75 years and a mean BMI of 22.69 kg/m2.Systolic and diastolic blood pressure and heart rate were measured after 15 min rest in the laboratory,5 and 10 min after starting the experiment,and then after 20 min in controlled laboratory conditions in five combination modes.The combination modes were(Sound:65 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%),(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%),(Sound:65 dB,WBGT:32°C,Speed:1.7,Slope:10%),and(Sound:95 dB,WBGT:32°C,Speed:3.4,Slope:14%).Mixed model analysis and paired t-test were applied for analysis.The results showed that the mean physiological parameters(Systolic and diastolic blood pressure and heart rate)increased when different combination modes worsened(Sound from 65 to 95 dB,WBGT from 22°C to 32°C,speed from 1.7 to 3.4,and slope from 10%to 14%,and when sound:95 dB,WBGT:32°C,Speed:3.4,and Slope:14%).Moreover,the mean changes of systolic and diastolic blood pressure were significant in all conditions when compared with the reference condition(Sound:65 dB,WBGT:22°C,Speed:1.7,and Slope:10%).The mean heart rate changes were also significant except for exposure to the second condition(Sound:65 dB,WBGT:22°C,Speed:3.4,Slope:14%)and the third condition(Sound:95 dB,WBGT:22°C,Speed:1.7,Slope:10%).Exposure to hazardous levels of sound,heat,and workload has adverse effects on physiological parameters.Concomitant exposure to all three hazards has a synergistic effect and increases the adverse effect.
基金supported by NSF Award IIS-0713506,and NIH Grant DA021817。
文摘We have applied functional near-infrared spectroscopy(fNIRS)to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex.We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks.To quantify the potential of fNIRS for mental workload discrimination,we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin(HbO2)and deoxyhemoglobin(HbR)concentration changes associated with the working memory tasks.We have found classification success rates in the range of 44%-72%,which are significantly higher than the corresponding chance level(for random data)of 19.1%.This work shows the potential of fNIRS for mental workload classification,especially when more parameters(rather than just the amplitude of concentration changes used here)and more sophisticated classification algorithms(rather than the simple 3-nearest neighbor algorithm used here)are considered and optimized for this application.
基金supported by the National Natural Science Foundation of China(Nos.71801002,71701003)the Humanities and Social Science Fund of the Ministry of Education of China(No.18YJC630023)+1 种基金the Natural Science Foundation of Anhui Province(No.1808085QG228)the Postdoctoral Program of Liaoning Province.
文摘Mental workload is considered to be strongly linked to human performance,and the ability to measure it accurately is key for balancing human health and work.In this study,brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload.In addition,a finite impulse response(FIR)filter,independent component analysis(ICA),and multiple artifact rejection algorithms(MARAs)were used to filter event-related potentials(ERPs).Then,the data consisting of ERPs,subjective ratings of mental workload,and task performance,were analyzed through the use of variance and Spearman’s correlation during a simulated computer task.We found that participants responded faster and performed better in the easy task condition,followed by the medium and high-difficulty conditions,which verifies the validity of the ERP filtering.Moreover,larger P2 and P3 waveforms were evoked as the task difficulty increased,and a higher task difficulty elicited a more enhanced N300.Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings,and a positive relationship between the P3 amplitude and accuracy.The results presented in this paper demonstrate that a combination of FIR,ICA,and MARA methods can filter ERPs in the non-invasive real-time measurement of workload.Additionally,frontocentral P2,N3,and parietal P3 components showed differences between genders.The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future.
基金This work is supported by the CETC Joint Advanced Research Foundation(No.6141B08020101)Major Special Science and Technology Project of Hainan Province(No.ZDKJ2019008).
文摘Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.
