Optimal Average Time Research Articles

Simultaneous measurement of temperature and C2H4 concentration in hydrocarbon flames using interference-immune differential absorption spectroscopy at 3.3 μm

In diagnostic applications based on tunable diode laser absorption spectroscopy, the measurement of target substances can be influenced by factors such as background thermal radiation in the combustion environment, extinction caused by solid or liquid particles, and other interfering absorptions. In this work, we developed a differential absorption diagnostic technique based on wavelength pairs, utilizing an interband cascade laser near 3.3 μm to simultaneously measure temperature and C2H4 concentration in hydrocarbon flames. Based on a detailed study of the C2H4 spectrum in this region and considering the optimal standard for spectral lines, two wavelength pairs were selected. The temperature is determined by the ratio of the absorption cross-sections of two wavelength pairs, and the C2H4 concentration is inferred based on the wavelength pair with higher differential absorption. In the initial stage, the system’s accuracy was verified in high-temperature static conditions (T = 300–800 K, P = 1 atm), and continuous time series measurements demonstrated the system’s stability. The limit of detection achieved by Allan-Werle variance analysis is 2.5 ppm at the optimal average time of 100 s. Subsequently, measurements were taken in a hydrocarbon flame. The obtained results indicate an average deviation of 1.021 % between the measured temperature in the flame and the reference value, with a standard deviation of 1.381 % for concentration measurement. All the measurements show that the system can be potentially applied to combustion diagnosis.

Sequential Joint Detection and Estimation: Optimal Average Stopping Time

Sequential Joint Detection and Estimation: Optimal Average Stopping Time

Simultaneous atmospheric CH4, CO2 and O2 detection using fiber-optic switch (FOS)-based time-division multiplexed NIR laser long optical path absorption spectroscopy

A near-infrared (NIR) distributed feedback (DFB) laser sensor based on long optical path absorption spectroscopy technique was developed for simultaneous measurement of atmospheric methane (CH4), carbon dioxide (CO2) and oxygen (O2). A fiber-optic switch (FOS) in conjunction with the time-division multiplexing (TDM) technique was adopted to enable different lasers scan over the target gas absorption lines in turn. The long optical path wavelength modulation spectroscopy (WMS) technique with second harmonic detection as well as a laser power fluctuation correction method was used to improve the sensor’s precision and accuracy. For the selected gas absorption lines, located at 6046.945 cm-1, 6361.245 cm-1 and 7877.647 cm-1, the detection limit (1σ) of 0.034 parts per million (ppm) for CH4, 11.921 ppm for CO2 and 0.14% for O2 was achieved according to the concentration and noise level. Allan deviation analysis indicates that the 1-s measurement precision was 0.030 ppm for CH4, 11.518 ppm for CO2 and 0.14% for O2, which could be improved to 6.5E-4 ppm, 0.255 ppm and 0.005% at an optimal average time of 800 s. A two-day continuous measurement of the CH4, CO2, and O2 concentration from ambient air was carried out, which verifies the stability and robustness of the developed multi-gas sensor.

Autonomous ratcheting by stochastic resetting.

We propose a generalization of the stochastic resetting mechanism for a Brownian particle diffusing in a one-dimensional periodic potential: randomly in time, the particle gets reset at the bottom of the potential well it was in. Numerical simulations show that in mirror asymmetric potentials, stochastic resetting rectifies the particle's dynamics, with a maximum drift speed for an optimal average resetting time. Accordingly, an unbiased Brownian tracer diffusing on an asymmetric substrate can rectify its motion by adopting an adaptive stop-and-go strategy. Our proposed ratchet mechanism can model the directed autonomous motion of molecular motors and micro-organisms.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis.

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Routing of autonomous vehicles for system optimal flows and average travel time equilibrium over time

We envisage a transportation management center (TMC) for future autonomous vehicles that distributes the traffic over a cycle comprising many days in such a manner that the average travel time (AVT) of vehicles of the same origin–destination (OD) over the cycle is identical at equilibrium while the entire network achieves system optimal (SO) flows every day. Vehicles are assigned to a set of paths, with different proportions of time within the cycle on different paths, but with the same AVT over the cycle at equilibrium. Since AVT is the same for each user, it is inherently fair to every-one. Thus, not only do users gain by subscribing to this system but the overall system also, as SO flows are followed each day. We then model the case of mixed equilibrium (ME), wherein drivers are given the option to subscribe to the TMC path combinations and hence assist in achieving SO, while non-subscribers only minimize their own private costs and follow their user equilibrium (UE) paths, which typically have shorter times than the corresponding AVT. To counter this, a tolling scheme is imposed on non-subscribers, i.e., UE users, based on their value-of-time (VOT) distribution and the travel time difference between subscribers' AVT and non-subscribers' UE time at ME. In the end, the total cost of travel of non-subscribers, toll plus UE time, is equal to the corresponding AVT of subscribers. The efficiency of this system is shown through numerical studies. Finally, the impact of market penetration and different VOT distributions on AVT and toll are investigated through sensitivity analysis.

A Heuristic-Based Task Scheduling Policy for QoS Improvement in Cloud

Cloud computing is a big step in the parallel and distributed computing that offers pervasive access to the entire stack of computing resources located in the data center via the Internet in a virtualized manner. QoS is an important research direction in the cloud and is a collection of constraints that meets the service level agreement (SLA) between the users and service providers. These constraints are waiting time, completion time, response time, makespan, resource utilization, effective utilization of bandwidth, and load balancing. This work presents a distribution plan for transferring task loads to different virtual machines (VMs) with an efficient load balancing mechanism that is best suited in heterogeneous environments and improve the QoS parameter of users and cloud vendors simultaneously. In this paper, the expected uniform load of tasks that can be mapped to a particular VMs is calculated and after that, the optimal average completion time (OACT) of expected uniform tasks load to each VM is calculated.

A Fuzzy Production Inventory Model for Deteriorating Items with Shortages

In this paper we have developed a supply chain production inventory model for deteriorating items with shortage under Fuzzy environment. The formulae for the optimal average system cost, stock level, backlog level and production cycle time are derived when the deterioration rate is very small. In reality it is seen that we cannot define all parameters precisely due to imprecision or uncertainty in the environment. So, we have defined the inventory parameter deterioration rate as triangular fuzzy numbers. The signed distance method and graded mean integration method have been used for defuzzification. Numerical examples are taken to illustrate the procedure of finding the optimal total inventory cost, stock level and backlog level. Sensitivity analysis is carried out to demonstrate the effects of changing parameter values on the optimal solution of the system.

Fundamental Limits of Stochastic Shared-Cache Networks

The work establishes the exact performance limits of stochastic coded caching when users share a bounded number of cache states, and when the association between users and caches, is random. Under the premise that more balanced user-to-cache associations perform better than unbalanced ones, our work provides a statistical analysis of the average performance of such networks, identifying in closed form, the exact optimal average delivery time. To insightfully capture this delay, we derive easy-to-compute closed-form analytical bounds that prove tight in the limit of a large number Λ of cache states. In the scenario where delivery involves K users, we conclude that the multiplicative performance deterioration due to randomness - as compared to the well-known deterministic uniform case - can be unbounded and can scale as Θ([log Λ]/[log log Λ]) at K = Θ(Λ ), and that this scaling vanishes when K = Ω(Λ log Λ ). To alleviate this adverse effect of cache-load imbalance, we consider various load-balancing methods, and show that employing proximity-bounded load balancing with an ability to choose from h neighboring caches, the aforementioned scaling reduces to Θ([log(Λ/h)]/[log log(Λ/h)]) at K=Θ(Λ ), while when the proximity constraint is removed, the scaling is of a much slower order Θ(log log Λ ). The above analysis is extensively validated numerically.

Analysis of multi-component broadband absorbers based on wavelength modulation united absorption spectroscopy

Wavelength Modulation United Absorption Spectroscopy (WM-UAS), which is virtually an extension of Wavelength Modulation Spectroscopy (WMS), for measuring multi-component broadband absorbers is proposed and demonstrated in this paper. “Broadband absorbers” indicates matters that do not exhibit resolved absorption spectra within a certain wavelength range. WM-UAS requires the assistance of a kind of probe substance which exhibits a series of narrow absorption profiles to measure broadband absorbers. An improved WMS technique is first introduced, with which the limitation of low absorbance in conventional WMS is broken through. On this basis, the mathematical expression for WM-UAS is deduced step-by-step. Simulations and experiments are conducted so that the procedures as well as the validity of WM-UAS is presented. In the experiment, water vapor is selected as the probe substance while methanol vapor and ethanol vapor are simultaneously measured. According to Allan deviation analysis, the optimal average time of the system is 150.18 s and the corresponding detection limit is 19.38 ppm·m for ethanol and 14.85 ppm·m for methanol.

Measurement of broadband absorbers in the near-infrared region based on Wavelength Modulation United Absorption Spectroscopy.

Molecules without well-resolved absorption spectra within a certain spectral range can be called broadband absorbers. Inspired by Wavelength Modulation Spectroscopy (WMS), we came up the idea of Wavelength Modulation United Absorption Spectroscopy (WM-UAS) for measuring broadband absorbers. WM-UAS commonly involves two gases, one is the probe gas, another is the target broadband absorber. The only request for implementing WM-UAS is that, within the spectral envelope of the broadband absorber, there exists more than one well-resolved absorption peak of the probe gas. To validate the effectiveness of WM-UAS, methanol was experimentally measured with water vapor as the probe gas. According to Allan deviation analysis, the detection limit was estimated as 1.7ppm during the optimal average time of 5.4s. For standard methanol vapor with a concentration of 100.9ppm, the measurement error was about 1.3%@5.4s, and for high purity nitrogen, the measurement result was 3.56ppm@5.4s.

A TDLAS Sensor for Simultaneous Measurement of Temperature and C2H4 Concentration Using a Differential Absorption Scheme at High Temperature

The ethylene (C2H4) concentration and temperature are simultaneously measured in high temperature environment using a differential absorption scheme combined with a distributed feedback (DFB) diode laser near 1.620 μm. Based on optimal selection criterions, two wavelength pairs with absorption peaks at 6174.58 cm-1 and 6174.98 cm-1 are selected for the measurement. Temperature are obtained from the ratio of the absorption cross section of the two wavelength pairs, and C2H4 concentration is inferred from the differential absorption of one of the wavelength pairs. Measurements are performed in a high temperature cell (T = 300-900 K, P = 1 atm) to verify the accuracy of the system. The accuracies for the measurement of C2H4 concentration and temperature are 1.135% and 2.215%, respectively. Continuous time series measurements indicated that the system has a good stability and the limit of detection achieved by Allan-Werle variance analysis is 6.6 ppm at the optimal average time of 340 s. All the measurements show the scheme is helpful to improve the practicality of laser absorption spectroscopy in combustion diagnosis.

Integrasi Penjadwalan Produksi dan Preventive Maintenance untuk Meminimasi Makespan dengan Menggunakan Metode Heijunka dan Batch – Backward Scheduling (Studi Kasus PT. BMC)

PT BMC industry which produces automotive components. The problem, in the Brake Assembly Assembly Line, is that high engine downtime is caused by engine failure, resulting in production scheduling not going according to plan. The Drum Assembly Brake Line consists of BD III lines, producing Brake Drum and HUB III lines, producing Hubs. This study aims to determine the smallest and the same makespan value, for the BD III and HUB III lines using the integration of the Heijunka method and Batch Backward Scheduling with Preventive Maintenance (PM) Data processing begins by determining the sequence of jobs and completion time with Heijunka and Batch-Backward Scheduling method. Next, determine the MTTF and MTTR parameters to determine the optimal inspection interval and average repair time. In the last step, the scheduling results are integrated with the inspection and repair intervals if needed, to get the makespan value. The calculation results show that the optimal inspection interval and MTTR for the BD III line is 94 hours and 1.7 hours while the HUB III line is 116 hours with 1.2 hours. After integration of the Heijunka-PM method, the makespan is 511.8 hours and 510.3 hours for the BD III and HUB III lines. The integration of the Batch-Backward scheduling-PM method produces makespan of 538.8 hours and 540 hours for the BD III and HUB III lines. Based on the smallest makespan criteria, scheduling integration is chosen, using the Heijunka-PM method.

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

BackgroundThis paper proposes a methodology for helping bridge the gap between the complex waveform information frequently available in an intensive care unit and the simple, lumped values favoured for rapid clinical diagnosis and management. This methodology employs a simple waveform contour analysis approach to compare aortic, femoral and central venous pressure waveforms on a beat-by-beat basis and extract lumped metrics pertaining to the pressure drop and pressure-pulse amplitude attenuation as blood passes through the various sections of systemic circulation.ResultsValidation encompasses a comparison between novel metrics and well-known, analogous clinical metrics such as mean arterial and venous pressures, across an animal model of induced sepsis. The novel metric Ofe → vc, the direct pressure offset between the femoral artery and vena cava, and the clinical metric, ΔMP, the difference between mean arterial and venous pressure, performed well. However, Ofe → vc reduced the optimal average time to sepsis detection after endotoxin infusion from 46.2 min for ΔMP to 11.6 min, for a slight increase in false positive rate from 1.8 to 6.2%. Thus, the novel Ofe → vc provided the best combination of specificity and sensitivity, assuming an equal weighting to both, of the metrics assessed.ConclusionsOverall, the potential of these novel metrics in the detection of diagnostic shifts in physiological behaviour, here driven by sepsis, is demonstrated.

MobiCoRE: Mobile Device Based Cloudlet Resource Enhancement for Optimal Task Response

Cloudlets are small self maintained clouds, with hotspot like deployment, to enhance the computational capabilities of the mobile devices. The limited resources of cloudlets can become heavily loaded during peak utilization. Consequently, per user available computational capacity decreases and at times mobile devices find no execution time benefit for using the cloudlet. Researchers have proposed augmenting the cloudlet resources using mobile devices; however, the proposed approaches do not consider the offered service to load ratio while using mobile device resources. In this paper, we propose easy to implement Mobile Device based Cloudlet Resource Enhancement (MobiCoRE) while ensuring that: (i) mobile device always have time benefit for its tasks submitted to the cloudlet and (ii) cloudlet induced mobile device load is a fraction of its own service requirement from the cloudlet. We map MobiCoRE on M/M/c/K queue and model the system using birth death markov chain. Given the arrival rate of $\lambda$ , $c$ cpu cores in cloudlet, maximum tasks in the cloudlet to be $K$ and $P_0=f(\lambda, c,K,\mu)$ be probability of having no user in cloudlet, we derive the condition $\frac{1}{P_0}= \frac{\lambda ^K}{c^{K-c}c!\mu ^K}1000$ for optimal average service time $\frac{1}{\mu}$ of cloudlet such that the mobile applications have maximum benefit for using cloudlet services. We show that the optimal average service time is independent of the applications service requirement. Evaluation shows that MobiCoRE can accommodate upto 50 percent extra users when operating at optimal service time and sharing mobile resources for remaining task, compared to completing the entire user applications in cloudlet. Similarly, up to 47 percent time benefit can be achieved for mobile devices by sharing only 16 percent computational resources with the cloudlet.

Impact Analysis of Start-Up Lost Time at Major Intersections on Sathorn Road Using a Synchro Optimization and a Microscopic SUMO Traffic Simulation

Start-up lost time is the time lost in the starting of the green time interval when a traffic signal phase changes from red to green and previously stopped vehicles in the curb line queue need time to accelerate to the desired speed. Actual traffic data analytics from newly installed loop coil detectors at all approaching upstream road segments of major intersections on Sathorn Road in Bangkok, Thailand, are used to confirm that the vehicle flow is obstructed considerably by the large start-up lost time. In this paper, the effect of a large start-up lost time is evaluated in terms of the travel time of passenger cars in a calibrated microscopic traffic simulation. The evaluation is based on the simulation of the urban mobility platform, while the traffic signal lights at major intersections are based on the standard Synchro optimization software. By the simulation, the average travel time per vehicle increases from 4% to 37% when the start-up lost time is varied from a baseline value of 1 s to the maximum value of 15 s, which potentially occurred in the actual traffic data collection in this paper. In addition, the optimal traffic signal green phase lengths increase from 2% to 42%, depending on the volume-to-capacity ratio. The similar increasing trend of optimal green time and average travel time per vehicle is observed using theoretical analysis based on M/M/1 and D/D/1 queues to support the results from Synchro. Findings of this paper are beneficial for understanding the impact of start-up lost time at signalized intersections.

A Simplified Waveform Energetics Approach to Interpreting Arterial and Venous Pressure

Abstract This paper develops a novel methodology for extracting simple, beat-to-beat, lumped metrics of systemic circulation performance by comparing the catheter pressure waveforms from the femoral artery and vena cava. Their diagnostic potential is compared to the similar, clinically used metric ΔMP, the drop in mean blood pressure between the aorta and vena cava, across an experimental cohort encompassing the progression of sepsis and several clinical interventions known to alter circulatory state. Both Ofe→vc, the model derived pressure attenuation between the femoral artery and vena cava, and the clinical metric, ΔMP, performed well. However, Ofe→vc reduced the optimal average time to sepsis detection from endotoxin infusion from 46.2 minutes for ΔMP to 11.6 minutes, for a slight increase in false positive rate from 1.8% to 6.2%. Thus, the potential diagnostic benefits of this novel approach are demonstrated, and a case is made for further investigation.

Investigation of the incoming wind vector for improved wind turbine yaw-adjustment under different atmospheric and wind farm conditions

Regardless of the evolution of wind energy harvesting, the way in which turbines obtain in-situ meteorological information remains the same - i.e. using traditional wind vanes and cup anemometers installed at the turbine's nacelle, right behind the blades. As a result, misalignment with the mean wind vector is common and energy losses up to 4.6% can be experienced as well as increases in loading and structural fatigue. A solution for the near-blade monitoring is to install wind LIDAR devices on the turbines' nacelle. This technique is currently under development as an alternative to traditional in-situ wind anemometry because it can measure the wind vector at substantial distances upwind. But at what upwind distance should they interrogate the atmosphere? and, what is the optimal average time in which to learn about the incoming flow conditions? This work simulates wind fields approaching isolated wind turbines and wind turbine arrays within large wind farms using Large Eddy Simulations. The goal is to investigate the existence of an optimal upstream scanning distance and average time for wind turbines to measure the incoming wind conditions under different ambient atmospheric conditions. Results reveal no significant differences when measuring the incoming wind vector at different upstream distances, regardless of the atmospheric stratification. Within this framework a 30 min readjustment period is observed to perform the best.

Particle Swarm Optimization Technique for Equalization of EV Load with Variable Wind Power Generation

Objectives: An Electric Vehicle (EV) charging station supplies electrical energy for the charging of Electric Vehicles. As the plug-in hybrid electric vehicle is expanding, there is a growing need for widely distributed publicly accessible charging stations. This paper defines Optimization method for equalize/match charging schedule of Electric vehicle with dynamic wind power availability. Methods/Statistical Analysis: Optimal charging cost and average running time are the major considerable constraints at equalization of dynamic wind power with EV load. Depending upon the remaining parking time, the EVs are aggregated to reduce the size of the problem. The proposed model innovatively incorporates the degree of equalization between EV charging load and Wind power into the objective function. Estimation of EV parking time affects the charging schedule, so it is a considerable factor for optimization. Findings: Particle Swarm Optimization (PSO) technique can optimize/reduce the scheduling problems and it can equalize dynamic behavior of wind power generation with respect to EV loads. Applications/Improvements: Computational efficiency and the average running time show the validation of the proposed technique.

Simultaneous Boundary Partitioning and Cameras Synchronization for Optimal Video Surveillance

This paper proposes a real-time distributed algorithm for a team of smart cameras to self-organize and perform video surveillance of an open boundary. In particular, our algorithm simultaneously partitions the boundary among the cameras, and synchronizes the motion of the cameras to optimize the surveillance performance. We focus on the detection of smart intruders, who are aware of the cameras configuration at each time instant, and who schedule their motion to avoid detection for as long as possible. We consider both the worst-case and the average detection times of smart intruders. Our algorithm achieves minimum worst-case detection time, and, under some reasonable assumptions, constant-factor optimal average detection time.