scholarly journals Project Delivery of Goods With Limited Resources and Minimum Time Using Fuzzy Logic Method

2021 ◽  
Vol 12 (6) ◽  
pp. 1729-1740
Author(s):  
Paryati Et al.
Keyword(s):  
Input Data ◽  
Probabilistic Approach ◽  
Transformation Model ◽  
Fourth Generation ◽  
Start Time ◽  
Time Duration ◽  
Parallel Method ◽  
New Techniques ◽  
Object Oriented Design ◽  
Fuzzy Transformation

               In solving the problem of fuzzy application for scheduling delivery of goods with a limited power source and minimal time, the problems that are still not considered in the RCPS problem modeling are the uncertainty characteristics of the parameters of the timing of activities in the delivery project. Even though this can be solved by using the PERT (Project Evaluation and Review Technoloque) method with a probabilistic approach, this technique still ignores the limitations of the supply of resources. Actually, a probabilistic approach can be used, if previously provided data about the experience in completing similar projects. But if the project is a new project or the techniques and methodologies used to complete a new project, such as new techniques and methodologies in software engineering, among others: object-oriented design and programming, computer-aided software design, user interface management systems, fourth generation languages, etc., then the probabilistic approach is not suitable.                    In this situation, the decision maker must be able to estimate the cost and time duration, of all activities in the project based on existing experience, related to the level of knowledge they have, about new techniques and methodologies to be applied, and the level of human resource expertise. which are available.     This method of estimating project costs and time, which is more precise, uses representations in the form of fuzzy numbers, namely fuzzy sets in the set of real numbers that are normal, convex, and closed intervals. The delivery time is modeled as a fuzzy number of LR types IKiri, IKanan, α, β, with three values ​​of α-cuts E = 0.3, L = 0.7, and I = 1.0. The fuzzy transformation model is based on three pairs of inferior and superior values ​​from α-cuts. The priority for scheduling delivery of goods is based on the smallest early start time EST value. Resources are solved by serial and parallel models. The smallest makespan value is used to determine the best solution. Goods delivery settlement uses fuzzy operations, namely arithmetic operations and relation operations.                    Analysis of the output oftware based on testing with test scenarios (table ^ .50) for some input data shows that the parallel method is better than the serial method. This is indicated by the large difference in makespan value generated from the two methods. Based on input data from a software development project, the serial method gives makespan values ​​in the range between 675.0 and 867.0, while the parallel method gives makespan values ​​in the range between 116.0 and 259.0. The analysis of the output software in a fuzzy Gantt Chart representation shows that an activity can be scheduled with varying degrees of optimism. The degree of optimistic activity scheduling can be graded in linguistic terminology between two extreme pessimistic and optimistic values, namely very pessimistic, pessimistic, slightly pessimistic, slightly optimistic, optimistic, very optomistic.

ASSESSMENT OF PARAMETER UNCERTAINTY IN RIGID MUSCULOSKELETAL SIMULATION USING A PROBABILISTIC APPROACH

2015 ◽  
Vol 18 (03) ◽  
pp. 1550013 ◽  
Author(s):  
Tien Tuan Dao ◽  
Marie-Christine Ho Ba Tho
Keyword(s):  
Experimental Data ◽  
Numerical Simulations ◽  
Parameter Uncertainty ◽  
Input Data ◽  
Muscle Force ◽  
Muscle Tension ◽  
Probabilistic Approach ◽  
Rectus Femoris ◽  
Joint Moment ◽  
Musculoskeletal Simulation

Experimental investigation coupled with numerical simulations is commonly used for solving multi-physical problems. In the field of biomechanics, in which the aim is to understand the mechanics of living system, the main difficulties are to provide experimental data reflecting the multi-physical behavior of the system of interest. These experimental data are used as input data for numerical simulations to quantify output responses through physical and/or biological laws expressed by constitutive mathematical equations. However, uncertainties on the experimentally available data exist from factors such as human variability and differences in protocols parameters and techniques. Thus, the true values of these data could never be experimentally measured. The objective of this study was to develop a modeling workflow to assess and account for the parameter uncertainty in rigid musculoskeletal simulation. A generic musculoskeletal model was used. Data uncertainties of the right thigh mass, physiological cross-sectional area (pCSA) and muscle tension coefficient of the rectus femoris were accounted to estimate their effect on the joint moment and muscle force computing, respectively. A guideline was developed to fuse data from multiple sources into a sample variation space leading to establish input data distribution. Uncertainty propagation was performed using Monte Carlo and most probable point methods. A high degree of sensitivity of 0.98 was noted for the effect of thigh mass uncertainty on the hip joint moment using inverse dynamics method. A strong deviation of rectus femoris muscle force (around 260 N) was found under effect of pCSA and muscle tension coefficient on the force estimation using static optimization method. Accounting parameter uncertainty into rigid musculoskeletal simulation plays an essential role in the evaluation of the confidence in the model outputs. Thus, simulation outcome may be computed and represented in a more reliable manner with a global range of plausible values.

scholarly journals MODELS OF GENERATION OF INPUT DATA OF TRAINING OF NEURAL NETWORK MODULES FOR DIAGNOSTIC OF DISEASES IN UROLOGY

2019 ◽  
pp. 116-122
Author(s):  
Mykola Ivanovych Fedorenko
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Input Data ◽  
Probabilistic Approach ◽  
Training Data ◽  
Digital Data ◽  
Training Methods ◽  
Training Procedure ◽  
Network Modules ◽  
Parameter Values

The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of uroflowmetric parameters, in particular, graphs of uroflowrograms of the required volume, used as input data for NNM training. Objective: to develop a mathematical model for the formation of uroflowmetric parameters using a probabilistic approach based on a uniform "white noise". To develop an effective algorithm for the procedure for generating new parameter values and tools for its implementation. Methods used: NNM training methods, mathematical modeling methods, digital signal processing methods, tools for generating and processing random numerical sequences, digital data filtering methods. The following results were obtained: when creating and implementing a mathematical model for generating a large amount of training data, the requirements of randomness are taken into account when obtaining new values of uroflowmetric parameters. And at the same time, the obtained noise values are filtered to values of a given range, which are percentage-wise comparable to the amplitude value of the uroflowmetric parameter. Conclusions. The scientific novelty of the results is as follows: the NNM training method for recognizing diseases in urology has been improved by developing a mathematical model to generate uroflowmetric parameters for NNM training. The presented model allows you to create the necessary amount of data for training neural network modules in the course of experimental research on the recognition of diseases. The generation of uroflowmetric parameters is based on adding noise to the parameter values. This allows you to change the input data of the NNM training in a given range. This ensures the creation of the required input volume of the NNM training procedure. In the future, this contributes to the testing process of trained neural network modules with reliable information on the diagnosis of diseases in urology.

scholarly journals Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

2020 ◽  
Vol 12 (8) ◽  
pp. 3439 ◽  
Author(s):  
Ye Yang ◽  
Zhongfu Tan ◽  
Yilong Ren
Keyword(s):  
Linear Regression ◽  
Electric Vehicles ◽  
Influential Factors ◽  
Start Time ◽  
Time Duration ◽  
Battery Modeling ◽  
Battery Electric Vehicles ◽  
Fast Charging ◽  
Battery State Of Charge ◽  
Time Origin

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

A New Technique for Estimating the Distribution of a Stochastic Project Makespan

2012 ◽  
pp. 33-47
Author(s):  
Yuval Cohen ◽  
Ofer Zwikael
Keyword(s):  
Project Scheduling ◽  
Time Distribution ◽  
Start Time ◽  
Time Duration ◽  
Project Duration ◽  
Critical Success ◽  
Activity Networks ◽  
Computation Scheme ◽  
Simulation Results ◽  
A New Technique

A critical success dimension in projects is the ability to complete a project within an estimated duration. In that regard, effective project scheduling techniques in an uncertain environment is of interest in many organizations. In this paper, the authors use an analytic approach to analyze the behavior of time duration distributions of projects in stochastic activity networks, and propose a simple computation scheme for approximating their distribution. The findings offer an understanding of the large gap between PERT and simulation results, and the deviation of projects from their intended schedules. In addition to providing theoretical framework, the proposed approach also recommends a simple practical pragmatic technique that computes the time distribution of project duration. This is a simple and handy tool for the project manager that may replace simulation. As a byproduct, the earliest start time distribution for each activity is also estimated.

Bursts of Information: Coordinating Interneurons Encode Multiple Parameters of a Periodic Motor Pattern

2006 ◽  
Vol 95 (2) ◽  
pp. 850-861 ◽  
Author(s):  
Brian Mulloney ◽  
Patricia I. Harness ◽  
Wendy M. Hall
Keyword(s):  
Motor Neurons ◽  
Motor Pattern ◽  
Intrinsic Excitability ◽  
Power Stroke ◽  
Start Time ◽  
Time Duration ◽  
Return Stroke ◽  
Multiple Parameters ◽  
Similar Information ◽  
Phase Progression

The limbs on different segments of the crayfish abdomen that drive forward swimming are directly controlled by modular pattern-generating circuits. These circuits are linked together by axons of identified coordinating interneurons. We described the distributions of these neurons in each abdominal ganglion and monitored their firing during expression of the swimming motor pattern. We analyzed the timing, the numbers of spikes, and the duration of each burst of spikes in these coordinating neurons. To see what information these neurons encoded, we correlated these parameters with the timing, durations, and strengths of bursts of spikes in motor axons from the same modules. During the power-stroke phase of each output cycle, the anterior-projecting neurons fired bursts of spikes that encoded information about the start-time, duration, and strength of each burst of spikes in power-stroke motor neurons from the same module. When the period and intensity of the motor output fluctuated, the bursts of spikes in these neurons tracked these fluctuations accurately. Each additional spike in these neurons signified an increase in the strength of the power-stroke burst. The posterior-projecting neurons that fired during the return-stroke phase encoded similar information about the return-stroke motor neurons. Although homologous neurons from different ganglia were qualitatively similar, neurons from posterior ganglia fired significantly more spikes per burst than those from more anterior ganglia, a segmental gradient that correlates with the normal posterior-to-anterior phase progression of limb movements. We propose that this gradient and a similar gradient in the durations of bursts in power-stroke motor neurons might reflect a hitherto-undetected difference in the excitation or intrinsic excitability of swimmeret modules in different segments.

scholarly journals Optimization of water distribution for open-channel irrigation networks

2013 ◽  
Vol 16 (2) ◽  
pp. 341-353 ◽  
Author(s):  
Sothea Hong ◽  
Pierre-Olivier Malaterre ◽  
Gilles Belaud ◽  
Cyril Dejean
Keyword(s):  
Water Distribution ◽  
Open Channel ◽  
Optimization Approach ◽  
Start Time ◽  
Time Duration ◽  
Channel Network ◽  
Water Losses ◽  
Numerical Resolution ◽  
Irrigation Networks ◽  
Demand Patterns

Water distribution for open-channel irrigation networks is more and more complex due to increasing constraints on water resources and changing demand patterns, whereas the performance of such systems is expected to increase. In this regard, an optimization approach is developed in order to schedule a fair scenario of water distribution among different users, where water demand is formulated in term of start time, duration and flow rate. This study investigates how to optimize the water distribution over a finite scheduling horizon while respecting the constraints linked to the system. The optimization approach forces the scheduled start time and the volume to be closer to the demanded ones, to minimize water losses and to reduce manpower. The constraints take into account the flow routing processes, the physical infrastructure, the available water resource, and the gate keeper timetable. The numerical resolution is done by using an optimization software IBM-Ilog Cplex. The method is then illustrated with the scheduling of off-take withdrawals for a typical traditional open-channel network: a lateral canal of the Gignac canal, in southern France.

scholarly journals Method of Source Identification following an Accidental Release at an Unknown Location Using a Lagrangian Atmospheric Dispersion Model

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1305
Author(s):  
Spyros Andronopoulos ◽  
Ivan V. Kovalets
Keyword(s):  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Source Location ◽  
Location Estimation ◽  
Source Inversion ◽  
Inversion Algorithm ◽  
Start Time ◽  
Time Duration ◽  
True Value ◽  
Atmospheric Dispersion Model

A computationally efficient source inversion algorithm was developed and applied with the Lagrangian atmospheric dispersion model DIPCOT. In the process of source location estimation by minimizing a correlation-based cost function, the algorithm uses only the values of the time-integrated concentrations at the monitoring stations instead of all of the individual measurements in the full concentration-time series, resulting in a significant reduction in the number of integrations of the backward transport equations. Following the source location estimation the release start time, duration and emission rate are assessed. The developed algorithm was verified for the conditions of the ETEX-I (European Tracer Experiment—1st release). Using time-integrated measurements from all available stations, the distance between the estimated and true source location was 108 km. The estimated start time of the release was only about 1 h different from the true value, within the possible accuracy of estimate of this parameter. The estimated release duration was 21 h (the true value was 12 h). The estimated release rate was 4.28 g/s (the true value was 7.95 g/s). The estimated released mass almost perfectly fitted the true released mass (323.6 vs. 343.4 kg). It thus could be concluded that the developed algorithm is suitable for further integration in real-time decision support systems.

scholarly journals To explore the impact of following the pre-operative fasting guidelines by ASA (American society of anesthesiologists), in improving patient outcome.

2021 ◽  
Vol 28 (7) ◽  
pp. 1018-1021
Author(s):  
Maqsood Ahmed Siddiqui ◽  
◽  
Ashok Perchani ◽  
Hamid Raza ◽  
Ahmeduddin Soomro ◽  
...  
Keyword(s):  
Tertiary Care ◽  
Cross Sectional Study ◽  
Care Hospital ◽  
Start Time ◽  
Time Duration ◽  
Cross Sectional ◽  
Emergency Procedures ◽  
The Mean ◽  
The Impact ◽  
Mean Time

Objective: To analyze the occurrence and consequences of a prolonged preoperative-fasting. Study Design: Cross-sectional study. Setting: Large tertiary care hospital in Pakistan. Period: October 2018 to October 2019. Material & Methods: We collected data for all the procedures that occurred during the study period and divided the cases into emergency procedures, add-on procedures and elective cases. We excluded the first cases of the day and excluded the emergency and add-on cases. We studied the patients NPO duration and their scheduled start time of the procedure and compared with their actual start time of the procedure. Results: The study population was n= 434 cases. Of these 434 cases n= 164 cases were performed on time and n= 270 cases were delayed by 60 minutes or more from their scheduled time. The most frequent reason was a previous cases running longer than expected in 59.25% of the cases, the second most common reason was a change in sequence and order of the procedures which was the case for delay in 14.44% of the cases. The overall mean time of NPO for the patients was found to be 770.1 +/- 130.6 minutes, for the delayed cases the mean time duration of NPO was 812.3 +/- 105.3 minutes. The mean time of case delay was 155.2 +/- 102.7 minutes for overall cases and for the significantly delayed case the mean time period of delay was 190.2 +/- 92.1 cases. Conclusion: Our results showed that 60% of the cases have a prolonged NPO status due to delays in start of their surgical procedure as compared to the scheduled times.

scholarly journals Multi-hazard risk assessment for roads: probabilistic versus deterministic approaches

2020 ◽  
Vol 20 (11) ◽  
pp. 3135-3160
Author(s):  
Stefan Oberndorfer ◽  
Philip Sander ◽  
Sven Fuchs
Keyword(s):  
Risk Assessment ◽  
Road Safety ◽  
Input Data ◽  
Probabilistic Approach ◽  
Transport Infrastructure ◽  
Data Uncertainty ◽  
European Alps ◽  
Hazard Risk ◽  
The Impact

Abstract. Mountain hazard risk analysis for transport infrastructure is regularly based on deterministic approaches. Standard risk assessment approaches for roads need a variety of variables and data for risk computation, however without considering potential uncertainty in the input data. Consequently, input data needed for risk assessment are normally processed as discrete mean values without scatter or as an individual deterministic value from expert judgement if no statistical data are available. To overcome this gap, we used a probabilistic approach to analyse the effect of input data uncertainty on the results, taking a mountain road in the Eastern European Alps as a case study. The uncertainty of the input data are expressed with potential bandwidths using two different distribution functions. The risk assessment included risk for persons, property risk and risk for non-operational availability exposed to a multi-hazard environment (torrent processes, snow avalanches and rockfall). The study focuses on the epistemic uncertainty of the risk terms (exposure situations, vulnerability factors and monetary values), ignoring potential sources of variation in the hazard analysis. As a result, reliable quantiles of the calculated probability density distributions attributed to the aggregated road risk due to the impact of multiple mountain hazards were compared to the deterministic outcome from the standard guidelines on road safety. The results based on our case study demonstrate that with common deterministic approaches risk might be underestimated in comparison to a probabilistic risk modelling setup, mainly due to epistemic uncertainties of the input data. The study provides added value to further develop standardized road safety guidelines and may therefore be of particular importance for road authorities and political decision-makers.

Meta-Heuristic Approach for Software Project Risk Schedule Analysis

2018 ◽  
pp. 136-149
Author(s):  
Isha Sharma ◽  
Deepshikha Chhabra
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Resource Constraints ◽  
Biological Evolution ◽  
Software Project ◽  
Start Time ◽  
Time Duration ◽  
Project Risk ◽  
Limited Availability ◽  
Multiple Resource

This chapter illustrates a technique to shorten the time duration using structured method. This is done by considering multiple resource constraints apart from time for the software project. The resource constraints are due to limited availability of resources (hardware, software, people, etc.). The difficulty is to locate minimal duration schedule. This is done by assigning the start time for each activity with the clear representation of precedence among them and resources available. There are various optimization approaches available but authors have selected a genetic algorithm. This method emulates the concept of biological evolution that is based on natural selection. This chapter concludes that additional research is needed in this area to provide better outcomes.

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