Flexible Time Intervals Research Articles

Optimization of curtailment intervals of wind turbines through assessment of measured loads during start-up and shutdown events

There is an increasing amount of curtailment of wind parks due to grid overload or negative market prices. Operators need to also account for the effect of curtailment on component loads, wear, and lifetime. This paper investigates curtailment leading to turbine shutdown. A novel methodology to optimize the length of curtailment intervals based on load neutrality is introduced. Measurement data from the structural health monitoring system of an onshore and an offshore wind turbine are used to quantify effects of curtailment on loads and wear of tower, blade, and pitch system. Shutdown and start-up events increase loads on the tower and pitch system. Blades benefit from lower edgewise loads and reduced erosion. Results show that load neutrality for the tower is reached on average after 63 min of idling for the onshore and after 200 min for the offshore wind turbine evaluated (not generalizable). An optimal curtailment strategy shall use flexible time intervals but ensure load neutrality on yearly average. This can serve as a simple and valuable guideline for wind farm operators to evaluate the criticality of curtailment times in terms of asset fatigue life. Future research shall investigate curtailment strategies that also include deration of power output.

Short-term aviation maintenance technician scheduling based on dynamic task disassembly mechanism

This paper focuses on the aviation maintenance technician scheduling (AMTS) problem and formulates AMTS and AMTS-DTDM model with a practical dynamic task disassembly mechanism (DTDM) and arrange maintenance technicians across shifts in short-term maintenance situations (less than 24–48 h). In DTDM, four technician scheduling modes are devised to flexibly disassemble the overtime work or reassigned it to other maintenance technicians according to work efficiency and progress, which could shorten the maintenance time and save the total cost. Moreover, a flexible time interval is designed to adjust the boundary of task disassembly. To further study the effectiveness of DTDM in reducing maintenance time and total costs, this paper divides both the AMTS and AMTS-DTDM models into three sub-models (i.e., two single-objective models and one multi-objective model), respectively. After that, we design solution methods as well as the encoding schemes compatible with both the AMTS model and the AMTS-DTDM model for different problem scales. Finally, to verify the effectiveness of DTDM, four groups of experiments are set up and particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO) are applied to compare AMTS and AMTS-DTDM sub-models. The experimental results show that the AMTS-DTDM model can effectively shorten the maintenance time and reduce the total costs for different scaled problems. Furthermore, the flexible time interval can facilitate more options for airlines to adjust the two above objectives to a small extent. The increase in flexible time may weaken the advantages of the AMTS-DTDM model while improving the rationality of the ultimate technician scheduling scheme.

Depression Diagnosis and Forecast based on Mobile Phone Sensor Data.

Previous studies have shown the correlation be-tween sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to access and less time-consuming. In particular, passive mobile phone data can be collected on a flexible time interval, thus detecting moment-by-moment psychological changes and helping achieve earlier interventions. Moreover, while previous studies mainly focused on depression diagnosis using mobile phone data, depression forecasting has not received sufficient attention. In this work, we extract four types of passive features from mobile phone data, including phone call, phone usage, user activity, and GPS features. We implement a long short-term memory (LSTM) network in a subject-independent 10-fold cross-validation setup to model both a diagnostic and a forecasting tasks. Experimental results show that the forecasting task achieves comparable results with the diagnostic task, which indicates the possibility of forecasting depression from mobile phone sensor data. Our model achieves an accuracy of 77.0 % for major depression forecasting (binary), an accuracy of 53.7 % for depression severity forecasting (5 classes), and a best RMSE score of 4.094 (PHQ-9, range from 0 to 27).

Dynamic planning of a two-dose vaccination campaign with uncertain supplies

The ongoing COVID-19 pandemic has led public health authorities to face the unprecedented challenge of planning a global vaccination campaign, which for most protocols entails the administration of two doses, separated by a bounded but flexible time interval. The partial immunity already offered by the first dose and the high levels of uncertainty in the vaccine supplies have been characteristic of most of the vaccination campaigns implemented worldwide and made the planning of such interventions extremely complex. Motivated by this compelling challenge, we propose a stochastic optimization framework for optimally scheduling a two-dose vaccination campaign in the presence of uncertain supplies, taking into account constraints on the interval between the two doses and on the capacity of the healthcare system. The proposed framework seeks to maximize the vaccination coverage, considering the different levels of immunization obtained with partial (one dose only) and complete vaccination (two doses). We cast the optimization problem as a convex second-order cone program, which can be efficiently solved through numerical techniques. We demonstrate the potential of our framework on a case study calibrated on the COVID-19 vaccination campaign in Italy. The proposed method shows good performance when unrolled in a sliding-horizon fashion, thereby offering a powerful tool to help public health authorities calibrate the vaccination campaign, pursuing a trade-off between efficacy and the risk associated with shortages in supply.

Critical Time Windows for Air Pollution Exposure and Birth Weight in a Multicity Canadian Pregnancy Cohort.

Maternal prenatal exposure to air pollution has been associated with adverse birth outcomes. However, previous studies focused on a priori time intervals such as trimesters reported inconsistent associations. We investigated time-varying vulnerability of birth weight to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) using flexible time intervals. We analyzed 1,300 live, full-term births from Maternal-Infant Research on Environmental Chemicals, a Canadian prospective pregnancy cohort spanning 10 cities (2008-2011). Daily PM2.5 and NO2 concentrations were estimated from ground-level monitoring, satellite models, and land-use regression, and assigned to participants from pre-pregnancy through delivery. We developed a flexible two-stage modeling method-using a Bayesian Metropolis-Hastings algorithm and empirical density threshold-to identify time-dependent vulnerability to air pollution without specifying exposure periods a priori. This approach identified critical windows with varying lengths (2-363 days) and critical windows that fell within, or straddled, predetermined time periods (i.e., trimesters). We adjusted the models for detailed infant and maternal covariates. Critical windows associated with reduced birth weight were identified during mid- to late-pregnancy for both PM2.5 and NO2: -6 g (95% credible interval: -11, -1 g) and -5 g (-10, -0.1 g) per µg/m3 PM2.5 during gestational days 91-139 and 249-272, respectively; and -3 g (-5, -1 g) per ppb NO2 during days 55-145. We used a novel, flexible selection method to identify critical windows when maternal exposures to air pollution were associated with decrements in birth weight. Our results suggest that air pollution impacts on fetal development may not be adequately captured by trimester-based analyses.

Optimasi Business Process Improvement Berbantuan Metode FLASH dengan Integrasi API Trello

The emergence of the COVID-19 case has a major impact on all sectors. At this critical time, customer satisfaction can be done by optimizing the use of resources by implementing BPI. The BPI method will conduct a review regarding the resources owned and will be adjusted to the current conditions. BPI is closely related to changes in project management. One of the optimal methods used in project management is FLASH, where the project duration will be in the form of a more flexible time interval. In this research, the system also uses Trello as a project management application. The purpose of this research is to design a scheme for calculating the duration of the project, as well as mapping project management from the data tasks that are owned using the FLASH method. System testing on the fuzzy system algorithm is implemented on the AOA network. Based on the calculation on the test object carried out, the probability of completing the project on time is 76%. This amount is obtained from the average delay factor for each task. With these values, scheduling using the FLASH method obtained the fastest duration is 19 days and the latest is 30 days.

Dating medieval texts by classification with flexible time intervals

AbstractDetermining the date of writing is a practical problem often encountered in the study of ancient and medieval texts. The problem is compounded by differences in genre, register and style, but in particular by our frequent dependence on much later copies of lost originals. This article examines how a method of classification with flexible time intervals has been developed for a corpus of medieval Irish annals (c.700–c.1600). It is shown that the method can deal successfully with an unparsed, complex corpus containing contaminated data. A method of extending the model from good quality, uncontaminated data to more complex texts is also demonstrated by identifying temporal characteristics and typical entries in the high-quality source.

Integrated control policy of production and preventive maintenance for a deteriorating manufacturing system

Abstract This paper addresses the integrated control of production and preventive maintenance for a deteriorating single-machine manufacturing system. The machine is subjected to deterioration and random failures. Both corrective maintenance and preventive maintenance are performed to maintain the machine’s reliability. The previous literature in this field normally assumes the periodicity of preventive maintenance and obtains only its fixed time interval for execution. Maintenance level, which represents the time and cost invested in the maintenance activity, is usually ignored in the literature. Therefore, we propose an integrated control model that considers not only the flexible time interval of preventive maintenance but also its level. The problem is then formulated as an infinite time horizon dynamic programming problem of a semi-Markov decision process. The objective is to develop the optimal control policy in terms of production rate, hedging level, maintenance rate and maintenance level such that the total production cost is minimized. The successive value iteration algorithm is applied to obtain the near optimal control policy. We compared the obtained policy with previous control policies reported in the literature through several numerical examples. Additionally, sensitivity analysis is performed to illustrate the impact of several cost parameters on the control policy and to validate the structure of this policy. This paper contributes to fill the research gaps mentioned above and provides a more practical production and preventive maintenance control model for industrial practitioners.

Development of passive samplers for in situ measurement of pyrethroid insecticides in surface water

Pyrethroid insecticides are widely used in urban environments, and their occurrence has been recently associated with aquatic toxicity in urban surface streams. Synthetic pyrethroids are strongly hydrophobic compounds, highlighting the importance of the freely dissolved concentration (Cfree), rather than the total chemical concentration, for better prediction of potential effects in aquatic ecosystems. The goal of this study was to develop a simple, robust and field-applicable passive sampling methodology that may be used for in situ monitoring of trace levels of pyrethroids in surface water. Among a range of polymer films, polyethylene film (PE) was found to be the most efficient at absorbing pyrethroids from water. To circumvent the long equilibrium time, 13C-permethrin and bifenthrin-d5 were preloaded on the PE sampler as performance reference compounds (PRC). Desorption of isotope-labeled PRCs was found to be isotropic to the absorption of target analytes. The optimized method was first tested in large circulating tanks simulating various environmental conditions. The derived Cfree values were consistently smaller than the total aqueous concentration in salt water or water containing humic acids. The PE samplers were further deployed at multiple field sites for 7 d in Southern California and analysis demonstrated good monitoring reproducibility and sensitivity under ambient environmental conditions. The developed passive sampler approach is ideal for application for in situ sampling under field conditions, and the use of PRCs allows sampling with short and flexible time intervals.

A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO) to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

Mobile Real-Time Data Acquisition System for Application in Preventive Medicine

In this article, the development of a system for online monitoring of a subject's physiological parameters and subjective workload regardless of location has been presented, which allows for studies on occupational health. In the sector of occupational health, modern acquisition systems are needed. Such systems can be used by the subject during usual daily routines without being influenced by the presence of an examiner. Moreover, the system's influence on the subject should be reduced to a minimum to receive reliable data from the examination. The acquisition system is based on a mobile handheld (or smart phone), which allows both management of the communication process and input of several dialog data (e.g., questionnaires). A sensor electronics module permits the acquisition of different physiological parameters and their online transmission to the handheld via Bluetooth. The mobile handheld and the sensor electronics module constitute a wireless personal area network. The handheld allows the first analysis, the synchronization of the data, and the continuous data transfer to a communication server by the integrated mobile radio standards of the handheld. The communication server stores the incoming data of several subjects in an application-dependent database and allows access from all over the world via a Web-based management system. The developed system permits one examiner to monitor the physiological parameters and the subjective workload of several subjects in different locations at the same time. Thereby the subjects can move almost freely in any area covered by the mobile network. The mobile handheld allows the popping-up of the questionnaires at flexible time intervals. This electronic input of the dialog data, in comparison to the manual documentation on papers, is more comfortable to the subject as well as to the examiner for an analysis. A Web-based management application facilitates a continuous remote monitoring of the physiological and the subjective data of the subject.

The Cluster Design for the Postmarketing Stability Surveillance Program

Postmarketing stability surveillance is necessary in order to (re)confirm the stability profile of a marketed pharmaceutical formulation throughout its lifecycle. In this paper the so-called ‘selected date’ or ‘cluster approach’ is described as an efficient alternative to the conventional ‘fixed-interval approach’ which is normally followed. The principle of the cluster approach is that samples of all batches of a formulation in a stability study are tested simultaneously at flexible time intervals. A statistical routine performed on the analytical results provides the opportunity to either confirm or reject the registered shelf life of the product tested. The cluster approach provides significant efficiency improvements, without losing the essential information about the stability of a product.

Managing a mixed-registration-type appointment system in outpatient clinics

Introduction: Improving outpatient resource utilization significantly enhances the efficiency of healthcare organizations. Substantial number of walk-in patients (average of 72% in our study) to outpatient services is a universal characteristic of Taiwan's healthcare organizations. Consequently, scheduling becomes extremely complicated and important. Selecting the right scheduling alternative, a healthcare organization can markedly improve operating efficiency of outpatient resources. Objective: This research applied simulation methodology to analyze several scheduling solutions and found that setting the appropriate arrival time interval for preregistered patients significantly impacts queuing problems in outpatient services. Method: Using established simulation models, the effects of various scheduling policies on patients' throughput time and waiting times were revealed. Under alternative model A, the first 20 numbers are reserved for scheduled patients; after that, only even numbers are offered for scheduled ones. Odd numbers after 20 are left for walk-ins. Under alternative model B, front numbers were assigned to scheduled patients successively. The later numbers were left for walk-ins. Alternative model C assigned scheduled patients with even numbers and walk-ins with odd numbers in sequence. Finally, alternative model D was designed to examine the optimal scheduled time interval by conducting the model with different scheduled time intervals such as 3, 5, 7, 9, and 11 min. Result: The alternative sequence (alternative model C—assigning even numbers for scheduled patients and odd numbers for walk-in patients, or vice versa) significantly has the least throughput time (average: 34.9 min vs 55.2, 56.2, and 46.2 min) and waiting times (average: 14.7 min vs 34.9, 35.8, and 25.8 min) for walk-in patients compared with other registration strategies. Scheduling the appointments with flexible time interval (alternative model D) has the least throughput time (average: 24.2 min vs 28.4, 28.2, and 37.2 min) and waiting times (average: 8.0 min vs 12.5, 12.3, and 20.5 min) for scheduled patients compared with other registration strategies. Conclusion: The findings of this research could be applied possibly to any outpatient clinic with mixed-registration-type (walk-in and scheduled), particularly which accounts for high percentage of walk-in patients.