Predicting Ship Maintenance and Repair Labor with Artificial Neural Networks

Predictive Analytics ◽

Data Set ◽

Repair Work ◽

Maintenance And Repair ◽

Independent Variables ◽

Ship Maintenance ◽

Ship maintenance and repair work cost estimation is often regarded as an “Art,” which may contribute to the financial success or distress of a shipyard. Regarded as experts by senior management, estimators are among the most valued resources, and nonetheless, human. Over time, estimators learn from mistakes, and get better with tenure at sharpening assessments. When estimators retire without having groomed an apprentice, shipyards may be at risk of losing a lot of know-how, all at once. These shipyards may well find very costly to experience, for a while, estimating skills stepping back on the learning curve. Yet, even shipyards relying on less advanced information technology may have unwittingly accumulated a lot of valuable data relevant to ship maintenance and repair works. These shipyards may overlook how easily accessible knowledge can be turned into a competitive advantage through predictive analytics. Not only can this data be literally mined, but machine learning algorithms, such as Artificial Neural Networks (ANN), can now process it for a speedy and preliminary estimate through faster and cheaper computing power. To be clear, the purpose is not to replace the human estimator but to help the expert quickly assess, when times are busy, whether to bid or not on a specific project opportunity. In the absence of The Master Estimator, an Apprentice may also look for a quick and cheap sanity check of the prepared estimate before submitting a bid. The study carried out in this article is based on all ship maintenance and repair data recorded at a single North American shipyard over the last 19 years since the current information systems were implemented. This raw data extract with all directly paid hours logged daily by workers on 1277 ship maintenance and repair projects was screened through advanced data cleansing. To enrich the cleansed data tables, additional independent variables were subsequently collected internally and externally to develop a training–testing data set. The final 657 projects represent 136 vessels regrouped in eight types, for which 28 other independent variables were all made available for training up to testing simple ANN models. The scope of this article is limited to the estimation of the direct labor required to complete ship maintenance and repair projects on a specific type of vessels for which workforce planning and tactical pricing was deemed the most relevant to keep the business afloat.

Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

Anuário do Instituto de Geociências - UFRJ ◽

10.11137/1982-3908_2021_44_35024 ◽

2021 ◽

Vol 44 ◽

Author(s):

Guilherme Loriato Potratz ◽

Smith Washington Arauco Canchumuni ◽

Jose David Bermudez Castro ◽

Júlia Potratz ◽

Marco Aurélio C. Pacheco

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Unsupervised Classification ◽

Hydrocarbon Exploration ◽

Data Set ◽

Log Data ◽

Hidden Patterns ◽

One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

General Melting Point Prediction Based on a Diverse Compound Data Set and Artificial Neural Networks.

ChemInform ◽

10.1002/chin.200532200 ◽

2005 ◽

Vol 36 (32) ◽

Author(s):

M. Karthikeyan ◽

Robert C. Glen ◽

Andreas Bender

Keyword(s):

Neural Networks ◽

Melting Point ◽

Data Set ◽

Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

Applied Sciences ◽

10.3390/app11156723 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6723

Author(s):

Ariana Raluca Hategan ◽

Romulus Puscas ◽

Gabriela Cristea ◽

Adriana Dehelean ◽

Francois Guyon ◽

...

Keyword(s):

Neural Networks ◽

Training Sample ◽

Training Data ◽

Elemental Content ◽

Neural Structure ◽

Food Authentication ◽

Data Set ◽

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

Prirodoobustrojstvo ◽

10.26897/1997-6011-2021-1-14-22 ◽

2021 ◽

pp. 14-22

Author(s):

G. N. KAMYSHOVA ◽

Keyword(s):

Neural Networks ◽

Real Time ◽

Predictive Control ◽

Intelligent Control ◽

Intelligent Control Systems ◽

Artificial Neural ◽

The One ◽

Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

Advanced Methodologies and Technologies in Modern Education Delivery - Advances in Educational Technologies and Instructional Design ◽

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

10.4018/978-1-5225-7365-4.ch059 ◽

2019 ◽

pp. 751-766

Author(s):

Sajid Umair ◽

Muhammad Majid Sharif

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Student Performance ◽

Semantic Analysis ◽

Vital Role ◽

Support Vector ◽

Data Set ◽

Important Research Topic ◽

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Training Data ◽

Machine Learning Model ◽

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Artificial Neural Networks in Drug Design 11: Influence of Learning Rate and Momentum Factor on the Predictive Ability

Scientia Pharmaceutica ◽

10.3797/scipharm.aut-00-05 ◽

2000 ◽

Vol 68 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

D. Kaiser ◽

C. Tmej ◽

P. Chiba ◽

K.-J. Schaper ◽

G. Ecker

Keyword(s):

Neural Networks ◽

Predictive Power ◽

Predictive Ability ◽

Learning Rate ◽

Data Set ◽

Lead Compounds ◽

Learning Rates ◽

Artificial Neural ◽

Nanomolar Range

A data set of 48 propafenone-type modulators of multidrug resistance was used to investigate the influence of learning rate and momentum factor on the predictive power of artificial neural networks of different architecture. Generally, small learning rates and medium sized momentum factors are preferred. Some of the networks showed higher cross validated Q2 values than the corresponding linear model (0.87 vs. 0.83). Screening of a 158 compound virtual library identified several new lead compounds with activities in the nanomolar range.

High Three-Dimensional Detection Accuracy in Piezoelectric-Based Touch Panel in Interactive Displays by Optimized Artificial Neural Networks

Sensors ◽

10.3390/s19040753 ◽

2019 ◽

Vol 19 (4) ◽

pp. 753 ◽

Cited By ~ 4

Author(s):

Shuo Gao ◽

Yanning Dai ◽

Vasileios Kitsos ◽

Bo Wan ◽

Xiaolei Qu

Keyword(s):

Neural Networks ◽

Computational Cost ◽

Smart Device ◽

Detection Accuracy ◽

Force Level ◽

High Detection ◽

Artificial Neural ◽

Interactive Displays

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots

10.1177/0954408915608755 ◽

2015 ◽

Vol 231 (3) ◽

pp. 470-479 ◽

Cited By ~ 2

Author(s):

Mustafa Soylak ◽

Tuğrul Oktay ◽

İlke Turkmen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Inverse Kinematic ◽

Plasma Cutting ◽

Three Dimensional Model ◽

Inverse Kinematic Problem ◽

Data Set ◽

In our article, inverse kinematic problem of a plasma cutting robot with three degree of freedom is solved using artificial neural networks. Artificial neural network was trained using joint angle values according to cartesian coordinates ( x, y, z) of end point of a robotic arm. The Levenberg–Marquardt training algorithm was applied to educate artificial neural network. To validate the designed neural network, it was tested using a new test data set which is not applied in training. A simulation was performed on a three-dimensional model of MSC.ADAMS software using angle values obtained from artificial neural network test. It was revealed from this simulation that trajectory of plasma cutting torch obtained using artificial neural network agreed well with desired trajectory.

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Sensors ◽

10.3390/s20030652 ◽

2020 ◽

Vol 20 (3) ◽

pp. 652 ◽

Cited By ~ 4

Author(s):

Sławomir Francik ◽

Sławomir Kurpaska

Keyword(s):

Neural Networks ◽

Mean Square ◽

Internal Temperature ◽

Data Set ◽

External Temperature ◽

Factors Influencing ◽

Testing Data ◽

Artificial Neural ◽

Hidden Layer

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).