Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

2014 ◽  
Vol 493 ◽  
pp. 388-394 ◽  
Author(s):  
Eddy S. Koenhardono ◽  
Eko Budi Djatmiko ◽  
Adi Soeprijanto ◽  
Mohammad I. Irawan
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Fuel Oil ◽  
Training Data ◽  
Reduction Gear ◽  
Maximum Relative Error ◽  
Oil Consumption ◽  
Testing Data ◽  
Ship Speed

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speedVand service margin SM, with the corresponding output parameters comprise of engine speednE, engine break horse powerPB, propeller pitchP/D, and the fuel consumptionFC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main engines's performance for trimaran patrol ship is satisfactory.

scholarly journals Neural Network Approach for Predicting Ship Speed and Fuel Consumption

2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Lúcia Moreira ◽  
Roberto Vettor ◽  
Carlos Guedes Soares
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Computing System ◽  
Fuel Oil ◽  
Network Computing ◽  
Peak Period ◽  
Neural Network Approach ◽  
Data Set ◽  
Navigation Data ◽  
Ship Speed

In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

scholarly journals Analysis of Fairness Limit on Fuel Oil Consumption of TwoTail Test Based Excavator and Dump Truck A Case Study in PT XYZ

2020 ◽  
Vol 1 (1) ◽  
pp. 6-12
Author(s):  
Bella Puspa Octaviania ◽  
Supriyadi ◽  
Ambran Hartono
Keyword(s):  
Fuel Consumption ◽  
Statistical Approach ◽  
Fuel Oil ◽  
Dump Truck ◽  
Oil Consumption ◽  
Mining Operations

A lack of method to find out the fairness limit of fuel consumption in mining operations enables statistical approach with two-tail test be applied to observe the fairness limit of actual fuel oil consumption compared to the manual handbook of its equipment. Fuel consumption according to the manual handbook for EXCA LIEBHERR 9350 excavator is 207.23 liters/hour and EXCA HITACHI 2500 is 191.51 liters/hour, while CATERPILLAR 777D Dump Truck is 36-53 liters/hour consider as low, 53-73, 8 liters/hour medium, and 73.8-96.5 liters/hour as high. This statistical approach has been carried out after fulfilling the concept of mechanized earth-moving. As a result, the differences in fuel consumption of LIEBHERR 9350 and HITACHI 2500 are 3.72% and 3.26%, which are still in range of a reasonable fuel consumption limit, while CAT 777D operating on LIEBHERR 9350 and CAT 777D operating on HITACHI 2500, each shows a difference in fuel consumption. The differences are 29.65%, meaning that it has exceeded the reasonable limits of fuel consumption and 7.15%, meaning that it is still in range of a reasonable fuel consumption limit.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

2008 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Methods ◽  
False Positive ◽  
Impulse Noise ◽  
False Negative ◽  
Training Data ◽  
Maximum Accuracy ◽  
First Case ◽  
Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

scholarly journals Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)

2019 ◽  
Vol 7 (2) ◽  
pp. 14
Author(s):  
Ireicca Agustiorini Harsehanto ◽  
M. Didik R. Wahyudi
Keyword(s):  
Social Media ◽  
Big Five ◽  
Personality Characteristic ◽  
Training Data ◽  
Big Five Personality ◽  
Personality Theory ◽  
Test Results ◽  
Bayes Classifier ◽  
Testing Data

Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.

Nonlinear Droop Load Sharing to Minimize Gas Emissions and Fuel Consumption

2017 ◽  
Author(s):  
Michel Rejani Miyazaki ◽  
Asgeir Johan Sørensen
Keyword(s):  
Optimum Condition ◽  
Fuel Consumption ◽  
Load Sharing ◽  
Fuel Oil ◽  
Oil Consumption ◽  
Gas Emission ◽  
Nonlinear Load ◽  
Gas Emissions ◽  
Marine Systems ◽  
Fine Tune

In this paper, load sharing curves are generated for marine systems with multiple gensets, where the goal is to reduce both gas emissions and fuel consumption. Initially, the average emissions and fuel consumption for each engine are calculated based on the specific emission and Specific Fuel Oil Consumption (SFOC) curves of each generator set (genset). An optimal nonlinear load sharing subject to gas emission and fuel consumption minimization is found for each engine. One result is that identical gensets should not have the same droop curve on the optimum condition, since it would lead to equal load sharing among them and a suboptimal configuration. Cases with two identical engines, two different engines, and multiple different engines were studied. The results show that by modifying the usually linear droop curve of engines, it is possible to reduce the fuel consumption and the gas emission, and it is also possible to fine tune the solution such that the fuel consumption or gas emissions are reduced.

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

scholarly journals CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM

Kursor ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 135
Author(s):  
Mohammad Zoqi Sarwani
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Electronic Systems ◽  
Testing Dataset ◽  
Testing Data ◽  
Neural Network Algorithm ◽  
Last Stage ◽  
Text Preprocessing

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

scholarly journals Pengaruh medan elektromagnetik pada prestasi mesin motor bakar empat langkah dengan bahan bakar gas

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Achmad Rifqi In'Amullah ◽  
Nasrul Ilminnafik
Keyword(s):  
Fuel Consumption ◽  
Alternative Fuels ◽  
Copper Wire ◽  
Research Data ◽  
Fuel Oil ◽  
Oil Consumption ◽  
Gas Fuel ◽  
Hc Emissions ◽  
High Level ◽  
Engine Power

The high level of fuel oil consumption in Indonesia caused by increases number of vehicles. Fuel oil consumption has switched into gas fuel as one of the secure alternative fuels and obtained more little gas emissions if compared with fuel oil. LPG (Liquified Petroleum Gas) is one of the alternative fuel was environmentally friendly. This research is purposed for compared performance of four-step engine with premium fuel and LPG fuel with a variety of additional electromagnetic field 600, 800, and 1000 total of copper wire windings. Using LPG fuel can increase torque generated by engine, but the result of engine power to be lower. Based on research data 800 copper wire windings can increase the number of torque and generated power compared to LPG fuel standard. LPG fuel can save fuel consumption compared to premium fuel. The most optimum decrease in fuel consumption is generated by using 1000 copper wire windings. Using LPG fuel can also reduce CO, CO2, and HC emissions levels. The best CO, CO2, and HC emissions levels are obtained from 1000 copper wire windings.Keywords: torque, power, fuel consumption, emissions, and LPG.

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