scholarly journals Ground motion predictive modelling based on genetic algorithms

2011 ◽  
Vol 11 (10) ◽  
pp. 2781-2789 ◽  
Author(s):  
S. Yilmaz
Keyword(s):  
Genetic Algorithms ◽  
Epicentral Distance ◽  
Model Development ◽  
Predictive Modelling ◽  
South West ◽  
Proposed Model ◽  
Average Shear ◽  
Earthquake Research ◽  
Earthquake Research Institute

Abstract. This study aims to utilise genetic algorithms for the estimation of peak ground accelerations (PGA). A case study is carried out for the earthquake data from south-west Turkey. The input parameters used for the development of attenuation relationship are magnitude, depth of earthquake, epicentral distance, average shear wave velocity and slope height of the site. Earthquake database compiled by the Earthquake Research Institute of Turkey was used for model development. An important contribution to this study is the slope/hill data included into the dataset. Developed empirical model has a good correlation (R = 0.78 and 0.75 for the training and overall datasets) between measured and estimated PGA values. The proposed model is also compared with local empirical predictive models and its results are found to be reasonable. The slope-hill effect found to be an important parameter for the estimation of PGA.

scholarly journals Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Tao Zhang ◽  
Gang Ren ◽  
Yang Yang
Keyword(s):  
Genetic Algorithms ◽  
Network Design ◽  
Weighted Sum ◽  
Hybrid Metaheuristic ◽  
Route Network ◽  
Proposed Model ◽  
Transit Route ◽  
Solution Methodology ◽  
Unsatisfied Demand

This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology.

Using productivity concept to predict material demand

2021 ◽  
Vol 14 ◽  
Author(s):  
Hui-Hsin Huang
Keyword(s):  
Characteristic Function ◽  
Probability Function ◽  
Probability Distributions ◽  
Demand Forecasting ◽  
Model Development ◽  
General Function ◽  
Input Material ◽  
Proposed Model ◽  
The Impact

Background: The issue of material demands prediction has been researched in industrial study and materials/ manufacturing technology many years ago. The previous researches based on stochastic model to discuss the quantities prediction of material demand. Some of them focus on multi-suppliers with characteristic function. Some use the information of past ordering quantities and ordering recency time. In these previous models, there is less study to discuss the impact of cost on material demand forecasting. Thus, this paper considers the productivities concept to make cost balance when forecasting material demand. The different probability distributions are demonstrated to portray the input (material demand) and output(cost). Methods: A case study with its empirical data is released to derive the probability function of cost and estimate the parameters of the proposed model. Results: The proposed model can extend to different distributions depending on different kind of cost or different type of industries and is more widely application. Conclusion: To consider manufacture's productivity, this model can help manager to control their cost and make a balance when ordering their materials. The model development of cost release a general function which makes it possible to extend different distributions depending on different kind of cost or different type of industries.

Sugar factory audits, debottlenecking and efficiency improvement

2020 ◽  
pp. 607-612
Author(s):  
Bernard Coûteaux
Keyword(s):  
Evaluation Method ◽  
Predictive Modelling ◽  
Cane Sugar ◽  
Sugar Factory ◽  
Low Energy Consumption ◽  
Process Simulations ◽  
Capital Efficiency ◽  
Potential Improvement ◽  
Primary Focus

This paper elaborates on the key solutions offered by De Smet Engineers & Contractors (DSEC) to optimize the efficiency of cane sugar producing and processing facilities. In order to meet customer needs, DSEC offers proprietary predictive models built using the latest versions of specialized software. These models allow factory managers to envision the whole picture of increased operational and capital efficiency before it becomes reality. An integrated energy model and the CAPEX/OPEX evaluation method are discussed as ways to estimate and optimize costs, both for new greenfield projects and revamping of existing factories. The models demonstrate that factory capacities can be successfully increased using equipment that is already available. Special attention is paid to crystallization and centrifugation process simulations and the potential improvement of the global energy balance. One case study shows the transformation of a beet sugar factory into a refinery to process raw cane sugar after beet crop season and the second case shows the integration of a refinery into a cane sugar factory. The primary focus of the article is optimization of the technological process through predictive modelling. DSEC’s suggested solutions, which lead to great improvements in a plant’s efficiency and its ability to obtain very low energy consumption, are discussed.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

scholarly journals Multi-Layer Blockchain-Based Security Architecture for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 772 ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Model Development ◽  
Base Stations ◽  
Smart Devices ◽  
Security Model ◽  
Cluster Heads ◽  
Authentication Mechanism ◽  
Proposed Model ◽  
Authentication And Authorization

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

122 A Mechanistic Model of Growth and Amino Acid Deposition in the Pregnant Sow: Model Development, Evaluation, and Application

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 55-56
Author(s):  
Christian D Ramirez-Camba ◽  
Crystal L Levesque
Keyword(s):  
Weight Gain ◽  
Amino Acid ◽  
Mechanistic Model ◽  
Model Development ◽  
Allantoic Fluid ◽  
Late Gestation ◽  
Maternal Body ◽  
Proposed Model ◽  
Development Evaluation ◽  
Model Weight

Abstract A mechanistic model was developed with the objective to characterize weight gain and essential amino acid (EAA) deposition in the different tissue pools that make up the pregnant sow: placenta, allantoic fluid, amniotic fluid, fetus, uterus, mammary gland, and maternal body were considered. The data used in this modelling approach were obtained from published scientific articles reporting weights, crude protein (CP), and EAA composition in the previously mentioned tissues; studies reporting not less than 5 datapoints across gestation were considered. A total of 12 scientific articles published between 1977 and 2020 were selected for the development of the model and the model was validated using 11 separate scientific papers. The model consists of three connected sub-models: protein deposition (Pd) model, weight gain model, and EAA deposition model. Weight gain, Pd, and EAA deposition curves were developed with nonparametric statistics using splines regression. The validation of the model showed a strong agreement between observed and predicted growth (r2 = 0.92, root mean square error = 3%). The proposed model also offered descriptive insights into the weight gain and Pd during gestation. The model suggests that the definition of time-dependent Pd is more accurately described as an increase in fluid deposition during mid-gestation coinciding with a reduction in Pd. In addition, due to differences in CP composition between pregnancy-related tissues and maternal body, Pd by itself may not be the best measurement criteria for the estimation of EAA requirement in pregnant sows. The proposed model also captures the negative maternal Pd that occurs in late gestation and indicates that litter size influences maternal tissue mobilization more than parity. The model predicts that the EAA requirements in early and mid-gestation are 75, 55 and 50% lower for primiparous sows than parity 2, 3 and 4+ sows, respectively, which suggest the potential benefits of parity segregated feeding.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

scholarly journals History, Colonialism, and Archival Methods in Socio-Hydrological Scholarship: A Case Study of the Boerasirie Conservancy in British Guiana

World ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 205-215
Author(s):  
Joshua Mullenite
Keyword(s):  
Environmental History ◽  
Cross Section ◽  
Science And Technology Studies ◽  
Model Development ◽  
Human Geography ◽  
Hydrological Models ◽  
British Guiana ◽  
Current Role

In this article, I review a cross-section of research in socio-hydrology from across disciplines in order to better understand the current role of historical-archival analysis in the development of socio-hydrological scholarship. I argue that despite its widespread use in environmental history, science and technology studies, anthropology, and human geography, archival methods are currently underutilized in socio-hydrological scholarship more broadly, particularly in the development of socio-hydrological models. Drawing on archival research conducted in relation to the socio-hydrology of coastal Guyana, I demonstrate the ways in which such scholarship can be readily incorporated into model development.

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