scholarly journals CONCEPTUAL COST ESTIMATIONS USING NEURO-FUZZY AND MULTI-FACTOR EVALUATION METHODS FOR BUILDING PROJECTS

2017 ◽  
Vol 23 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Wei-Chih WANG ◽  
Tymur BILOZEROV ◽  
Ren-Jye DZENG ◽  
Fan-Yi HSIAO ◽  
Kun-Chi WANG
Keyword(s):  
Adaptive Learning ◽  
Cost Estimation ◽  
Unit Cost ◽  
Cost Component ◽  
Building Projects ◽  
Regression Methods ◽  
Proposed Model ◽  
Average Unit ◽  
Factor Evaluation ◽  
Mathematical Techniques

During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

2018 ◽  
Vol 8 (4) ◽  
pp. 348-357 ◽  
Author(s):  
Dwifitra Jumas ◽  
Faizul Azli Mohd-Rahim ◽  
Nurshuhada Zainon ◽  
Wayudi P. Utama
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
State Building ◽  
Building Projects ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
West Sumatra

Purpose The purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage. Design/methodology/approach This paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model. Findings The result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data. Research limitations/implications Because the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied. Originality/value The study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

Rough-Cut Cost Estimation in a Capacitated Environment

2010 ◽  
pp. 1935-1953
Author(s):  
Mark Eklin ◽  
Yohanan Arzi ◽  
Avraham Shtub
Keyword(s):  
Cost Estimation ◽  
Capacity Planning ◽  
Deterministic Model ◽  
Unit Cost ◽  
Greedy Heuristic ◽  
Shop Floor ◽  
Machine Loading ◽  
Proposed Model ◽  
Traditional Costing ◽  
Rough Cut

This article presents a deterministic model for rough-cut cost estimation in a capacitated madeto- order environment. We assume that a firm can execute each job either at its own shop or by outsourcing it. The model calculates the unit cost of each product while taking into account the shop floor rough-cut capacity planning, and by determining what to produce in the firm’s shop and what to outsource. In order to reduce run times, a greedy heuristic algorithm was developed. Comparison of the proposed model with a model that takes into account precedence between operations and with a traditional costing approach was conducted. The article gives insight on the affect of shop workload, machine loading, and outsourcing decisions on the product unit cost estimation.

scholarly journals Cost analysis of a mobile medical unit programme in Andhra Pradesh: a microcosting study protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e038191
Author(s):  
Aakash Ashok Raikwar ◽  
Vishal Dogra ◽  
Ashish Giri ◽  
Nitin Rathnam ◽  
Shailendra Kumar Hegde
Keyword(s):  
Cost Analysis ◽  
Primary Healthcare ◽  
Cost Estimation ◽  
Andhra Pradesh ◽  
Unit Cost ◽  
Healthcare Services ◽  
Capital Investments ◽  
Cost Estimates ◽  
Medical Unit ◽  
Average Unit

IntroductionOffering primary healthcare through mobile medical units is an innovative way to reach the rural and the vulnerable population. With 292 mobile medical units, the Andhra Pradesh mobile medical unit (APMMU) programme is one of the largest health outreach programmes in rural India. However, India lacks reliable cost estimates for the health services delivered through mobile medical platforms. This study aims to estimate the unit cost of providing primary care services through mobile medical units in rural and tribal areas of Andhra Pradesh.Method and analysisCost analysis of 12 mobile medical units will be undertaken. We will use the activity-based microcosting technique from the providers’ perspective. A bottom-up approach will be used for cost estimation. Standardised tools will be used to collect data on activities and resources, and on the costs. Capital investments and recurrent costs will be measured and evaluated. Average unit costs, along with 95% CIs, will be reported. Sensitivity analysis will assess the cost estimate uncertainties and other cost assumptions.Ethics and disseminationPiramal Swasthya Management Research Institute’s ethics committee approved the study. The findings of the study will be disseminated through conference presentations, publications in peer-reviewed journals and advocacy with the national and state governments. This study will provide first-hand comprehensive cost estimates of provisioning primary healthcare services using mobile medical units in India.

scholarly journals Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Deep Level ◽  
Image Resolution ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Natural Image ◽  
Historical Period ◽  
Proposed Model

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

scholarly journals Spatial regression methods to evaluate beekeeping production in the state of Rio de Janeiro

2013 ◽  
Vol 65 (2) ◽  
pp. 553-558
Author(s):  
W.S. Tassinari ◽  
M.C. Lorenzon ◽  
E.L. Peixoto
Keyword(s):  
Rio De Janeiro ◽  
High Performance ◽  
Spatial Regression ◽  
The State ◽  
Spatial Effects ◽  
Regression Methods ◽  
Honey Production ◽  
Production Factors ◽  
Proposed Model ◽  
Wild Vegetation

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

scholarly journals Financial Analysis of 18kW Solar Photovoltaic Baidi Microgrid at Baidi, Tanahun, Nepal

2020 ◽  
Vol 15 (1) ◽  
pp. 229-236
Author(s):  
Sanjaya Neupane ◽  
Ajay Kumar Jha ◽  
Anirudh Prasad Sah
Keyword(s):  
Alternative Energy ◽  
Agricultural Development ◽  
Net Present Value ◽  
Financial Analysis ◽  
Unit Cost ◽  
Pilot Project ◽  
Solar Photovoltaic ◽  
Cost Of Electricity ◽  
Average Unit ◽  
Micro Grid

 This study presents financial evaluation of 18 kW solar photovoltaic powered Baidi Micro Grid implemented by Alternative Energy Promotion Center (AEPC) in Dubung village, Rising Gaupalika, Tanahun district of Nepal. The grid is built and is operational under Baidi Micro Grid Pvt. Ltd, a Special Purpose Vehicle (SPV) established under “Pro-Poor Public Private Partnership (5P)” concept supported by United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) & International Fund for Agricultural Development (IFAD). It is pilot project under 5P concept in Nepal. People from Dubung and Mauribas village as well as Saral Urja Nepal Pvt Ltd (SUN) jointly owns the SPV strengthening not only technical, managerial and financial support but also the community participation and engagement in all decision making process. The total cost of the project is NPR 13,395,000.00 at 2015 AD. The grant for the project was of value NPR 11,295,000.00 from AEPC, IFAD and UNESCAP and remaining was equity of SUN. The net present value of NPR -10,978,605.76 is obtained at 3% discount rate due to unavoidable replacement cost of batteries, charge controllers, inverters and high initial investment without the consideration of the grant amount. Whereas, with 84.32% utilization of available grant, the NPV worth of NPR 384,394.22 is obtained for the project. In breakeven analysis, a breakeven point of the project is obtained at 81.87% utilization of the grant. Without grant, project like Baidi Micro Grid will not sustain. In addition, average unit cost of electricity is found to be NPR 37.08 but it varied from NPR 16.67 to NPR 80.81. Household consuming more electricity has to pay less unit cost of electricity whereas household consuming less electricity had to pay higher unit cost of electricity.

scholarly journals Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Li ◽  
Yujuan Si ◽  
Tao Xu ◽  
Saibiao Jiang
Keyword(s):  
Cardiovascular Disease ◽  
Adaptive Learning ◽  
Portable Devices ◽  
Ecg Signals ◽  
Automatic Feature Extraction ◽  
Average Accuracy ◽  
Proposed Model ◽  
Home Health Monitoring ◽  
Predictive Rate ◽  
Electrocardiogram Ecg

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

M-Learning Generations and Interview Study Results of a Mobile Context-Aware Learning Schedule Framework

2011 ◽  
pp. 28-55 ◽  
Author(s):  
Jane Yin-Kim Yau ◽  
Mike Joy
Keyword(s):  
Mobile Learning ◽  
Adaptive Learning ◽  
Interview Study ◽  
Learning Preferences ◽  
Context Aware ◽  
Postgraduate Students ◽  
Learning Contexts ◽  
Proposed Model ◽  
Study Results ◽  
Mobile Context

Mobile learning applications can be categorized into four generations – ‘non-adaptive’, ‘learning-preferences’-based adaptive, ‘learning-contexts’-based adaptive and ‘learning-contexts’-aware adaptive. The research on our Mobile Context-aware and Adaptive Learning Schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using these as the basis for selecting appropriate learning materials for students. The authors have adopted a pedagogical approach for evaluating this framework – an exploratory interview study with potential users consisting of 37 university students. The authors targeted primarily undergraduate computing students, as well as students within other departments and postgraduate students, so that a deep analysis of a wider variety of users’ thoughts regarding the framework can be gained. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Their data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario, and a proposed model for describing mobile learning preferences dimensions.

Direct Calculation of Average and Marginal Costs From the Productive Structure of an Energy System

1995 ◽  
Vol 117 (3) ◽  
pp. 171-178 ◽  
Author(s):  
A. Lazzaretto ◽  
A. Macor
Keyword(s):  
Unit Cost ◽  
Direct Calculation ◽  
Optimization Methods ◽  
Energy System ◽  
Marginal Costs ◽  
Unit Costs ◽  
Average Unit ◽  
Productive Structure ◽  
Definition Of ◽  
Direct Inspection

Most of the thermoeconomic accounting and optimization methods for energy systems are based upon a definition of the productive purpose for each component. On the basis of this definition, a productive structure of the system can be defined in which the interactions among the components are described by their fuel product. The aim of this work is to calculate marginal and average unit costs of the exergy flows starting from their definitions by a direct inspection of the productive structure. As a main result, it is noticed that the only differences between marginal and average unit cost equations are located in the capital cost terms of input-output cost balance equations of the components.

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