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Prediction of vessel propulsion power using machine learning on AIS data, ship performance measurements and weather data

Prediction of vessel propulsion power using machine learning...

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international Maritime and Port Technology and Development conference, and the 2nd international conference on Maritime Autonomous Surface Ships, MTEC/ICMASS 2019

作者： Liang, Q. Tvete, H.A. Brinks, H.W. Group Technology and Research Maritime Digital Class and Services DNV GL Veritasveien 1 Høvik1363 Norway

The aim of this paper is to utilize AIS (Automatic Identification System) data, ship propulsion power measurements and weather data and apply different machine learning (traditional and deep learning) methods to develop improved models to predict ship propulsion power. The performance between different traditional machine learning methods and deep learning with different architectures were compared and discussed. Two scenarios were explored: 1) Training a machine learning model on one container ship significantly improved the predictions of power demand as compared to a physics-based model, with an R2 score of 0.78 compared to 0.48. 2) A machine learning model was also trained on several container ships. This scenario, where the test data with other container ships was not included in the training dataset, also showed better prediction with machine learning than physics-based model, with an improvement in R2 score from 0.69 to 0.85. However, the use of the trained machine learning model on other ship types showed varying results and especially when the vessel size differed significantly from vessels in the training data, data-driven models showed limitations. For vessels of similar size, however, machine learning for other ship types showed improvements. © Published under licence by IOP Publishing Ltd.

关键词： Ships

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Deep learning-based Emotion Spatial Regression in Speech Recognition for Human-computer Interaction 2

Deep Learning-based Emotion Spatial Regression in Speech Rec...

引用

2nd IEEE international conference on Knowledge Innovation and Invention, ICKII 2019

作者： Yeh, Jui-Feng Tsai, Jian-Cheng Wu, Bo-Wei Kuang, Tai-You National Chia-Yi University Department of Computer Science and Information Engineering Chiayi city Taiwan

ISBN: (纸本)9781728101101

This paper propose a method which uses the CNN-LSTM speech recognition as a basis to convert the input of user's speech signal into text, and combine the CKIP system to break sentence or phrases into words and calculate Valence-Arousal values by the method of vector-based in the last. We calculate Valence-Arousal values with vector-based method and three lexicons which is emotional, degree and negative lexicon respectively for words and phrases. If the input is not exist in three lexicons, we will use the OOV(Out-of-Vocabulary) function to solve the problem. The OOV function not only solve the problem but also is helpful for Valence-Arousal values calculation. Because the sentence is consists of many modifiers and words, the system will have calculation deviation. In order to improve the accuracy of values, we use the way of detecting modifiers again and implement in OOV function to reach the goal. Finally, the system will output the Valence-Arousal values and present on the chart. © 2019 IEEE.

关键词： Speech recognition

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Transforming Convolutional Neural Network to an Interpretable Classifier 26

Transforming Convolutional Neural Network to an Interpretabl...

引用

26th international conference on Systems, signals and Image processing (IWSSIP)

作者： Tamajka, Martin Benesova, Wanda Kompanek, Matej Slovak Univ Technol Bratislava Fac Informat & Informat Technol Inst Comp Engn & Appl Informat Ilkovicova 2 Bratislava 84216 Slovakia

ISBN: (纸本)9781728132273

For machine learning based methods to be accepted in practice, these methods should be either directly or indirectly interpretable or should at least provide a reliable estimate of a confidence degree about their prediction. This is of the importance primarily in critical domains like medicine where the decision should be clearly justified. Although deep neural networks achieve remarkable performance in almost any computer vision task, due to their complexity, their predictions remain difficult to interpret and explain. An interpretable classifier does not only provide the prediction but also a kind of evidence or explanation. In this paper, we present an effective way of transforming a sufficiently well trained convolutional neural network (98.50% accuracy on MNIST dataset, which contains small images of digits) to an interpretable classifier. We use hidden activations of training observations as their descriptors. Next, for every unseen observation, we use these descriptors to identify three most similar training samples based on cosine distance of their hidden activations. Using this approach, we are able not only to predict the class of an unknown observation, but also to justify the prediction by providing three most similar training observations in exchange to a slightly decreased accuracy (98.12%).

关键词： convolutional neural network deep learning explainability interpretability

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Optimizing deep convolutional neural network for facial expression recognitions 2nd

Optimizing deep convolutional neural network for facial expr...

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2nd international conference on Data Management, Analytics and Innovation, ICDMAI 2018

作者： Chavan, Umesh Kulkarni, Dinesh Walchand College of Engineering SangliMaharashtra India

ISBN: (纸本)9789811314018

Facial expression recognition (FER) systems have attracted much research interest in the area of machine learning. We designed a large, deep convolutional neural network to classify 40,000 images in the dataset into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN with graphic processing units (GPUs). Optimizing Deep CNN is to reduce training time for system. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Graphics processing unit

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Solar cell defect recognition based on orthogonal learning strategy 2

Solar cell defect recognition based on orthogonal learning s...

引用

2nd international conference on Digital Medicine and Image processing, DMIP 2019

作者： Song, Xiaoyu Qi, Qianqian Chen, Zhili Shenyang Jianzhu University Hunnan District Shenyang City Liaoning Province China

ISBN: (纸本)9781450376983

The quality of silicon wafers is an important factor restricting the efficiency and service life of photovoltaic power generation. In order to inspect the quality of silicon wafers, a defect recognition method based on orthogonal learning strategy is proposed where support vector machine is combined with binary tree for multiclass classification. Firstly, the adaptive threshold is set to remove the raster lines in the original image, and Fourier reconstruction image is used to enhance the defect. After that, we extract the image features. With the help of orthogonal learning strategy, an orthogonal array of feature data is established to implement the initial defect classification, and the classification results are analyzed by factor analysis. The extracted features are sorted according to their influence degree, and the improved support vector machine is used to classify the feature data accumulated one by one. Finally, genetic algorithm and grid search are introduced to optimize the parameters. The recognition accuracy of the designed classifier is up to 96.6%. The experimental results indicate the effectiveness of the proposed method. © 2019 Association for Computing machinery.

关键词： Support vector machines

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Multi-task learning based end-to-end speaker recognition 2

Multi-task learning based end-to-end speaker recognition

引用

2nd international conference on signal processing and machine learning, SPML 2019

作者： Pan, Yuxuan Zhang, Wei-Qiang Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China School of Electronic and Information Technology Sun Yat-sen University Guangzhou Guangdong China

ISBN: (纸本)9781450372213

Recently, there has been an increasing interest in end-to-end speaker recognition that directly take raw speech waveform as input without any hand-crafted features such as FBANK and MFCC. SincNet is a recently developed novel convolutional neural network (CNN) architecture in which the filters in the first convolutional layer are set to band-pass filters (sinc functions). Experiments show that SincNet achieves a significant decrease in frame error rate (FER) than traditional CNNs and DNNs. In this paper we demonstrate how to improve the performance of SincNet using Multi-Task learning (MTL). In the proposed Sinc-Net architecture, besides the main task (speaker recognition), a phoneme recognition task is employed as an auxiliary task. The network uses sinc layers and convolutional layers as shared layers to improve the extensiveness of the network, and the outputs of shared layers are fed into two different sets of full-connected layers for classification. Our experiments, conducted on TIMIT corpora, show that the proposed architecture SincNet-MTL performs better than standard SincNet architecture in both classification error rates (CER) and convergence rate. © 2019 Association for Computing machinery.

关键词： Bandpass filters

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Research on Autonomous Maneuvering Decision of UCAV Based on Approximate Dynamic Programming 2

Research on Autonomous Maneuvering Decision of UCAV Based on...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Hu, Zhencai Gao, Peng Wang, Fei Harbin Inst Technol Shenzhen Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Unmanned aircraft systems can perform some more dangerous and difficult missions which manned aircraft systems cannot perform. For tasks with high complexity, such as air combat, maneuvering decision mechanism is required to sense the combat environment and make the optimal strategy in real time. This paper formulates one-to-one air combat maneuvering problem in 3D environment, and proposes an approximate dynamic programming approach to make optimal maneuvering decisions automatically. The aircraft searches for combat strategies based-on Reinforcement Leaning, while sensing the environment, taking available maneuvering actions and receiving feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to rapid changing tactical situations, and learns effective strategies to fight against the opponent aircraft.

关键词： Air Combat Maneuvering Decision Approximate Dynamic Programming Reinforcement learning

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Security Comparison of machine learning Models Facing Different Attack Targets 2nd

Security Comparison of Machine Learning Models Facing Differ...

引用

2nd Annual international conference on Science of Cyber Security (SciSec)

作者： Liu, Zhaofeng Jia, Zhen Lu, Wenlian Fudan Univ Sch Math Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Ctr Math Sci Shanghai 200433 Peoples R China Shanghai Key Lab Contemporary Appl Math Shanghai 200433 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

ISBN: (纸本)9783030346362;9783030346379

machine learning has exhibited great performance in several practical application domains such as computer vision, natural language processing, automatic pilot and so on. As it becomes more and more widely used in practice, its security issues attracted more and more attentions. Previous research shows that machine learning models are very vulnerable when facing different kinds of adversarial attacks. Therefore, we need to evaluate the security of different machine learning models under different attacks. In this paper, we aim to provide a security comparison method for different machine learning models. We firstly classify the adversarial attacks into three classes by their attack targets, respectively attack on test data, attack on train data and attack on model parameters, and give subclasses under different assumptions. Then we consider support vector machine (SVM), neural networks with one hidden layer (NN), and convolution neural networks (CNN) as examples and launch different kinds of attacks on them for evaluating and comparing model securities. Additionally, our experiments illustrate the effects of concealing actions launched by the adversary. © Springer Nature Switzerland AG 2019.

关键词： machine learning Adversarial attack Security evaluation Support vector machine Neural network

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Arunalu: learning Ecosystem to Overcome Sinhala Reading Weakness due to Dyslexia

Arunalu: Learning Ecosystem to Overcome Sinhala Reading Weak...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Lakchani Sandathara Shalini Tissera Ransi Sathsarani Harini Hapuarachchi Samantha Thelijjagoda Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Dyslexia is an impairment in ability in reading. People having Dyslexia has difficulties in identifying specific letters and words and identifying speech sounds and decoding the letters which leads to difficulties in comprehension, spelling and writing. Dyslexia may severely affects language development and impacts reading and other language based improvement and functioning. “ARUNALU: learning ecosystem to overcome reading disabilities in Sinhala language due to Dyslexia” has been proposed as a multi-sensory mobile solution, in native language of Sri Lanka (Sinhala) and with effective screening and intervention methodologies recommended by health professionals. Objective is to deliver, a phonological support to enhance reading skills of dyslexic children by providing a machine learning based automated screening and intervention mobile solution. Through these reading environments, there's a reward system in intervention process to encourage the user, and also users and respective parties can analyze user's progress. The proposed system is mainly based on Voice recognition, Natural Language processing, machine learning and Deep learning concepts collaborating with reading and gaming environments. Core Objective of the proposed system is to come up with a better and effective screening and intervention methodologies for early identification of Dyslexia and provide phonological training to overcome Sinhala reading difficulties due to Dyslexia in a user friendly manner.

关键词： Training Pediatrics Ecosystems Speech recognition Writing Natural language processing Object recognition

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A Technique For Determining The Early Detection For Cervical Cancer 5

A Technique For Determining The Early Detection For Cervical...

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Deswal, Anjali Dhawan, Sanjeev Singh, Kulvinder Kurukshetra Univ Univ Inst Engn & Technol UIET Dept Comp Sci & Engn Kurukshetra 136119 Haryana India

ISBN: (纸本)9781728139883

Cervical cancer is one of the most common types of cancer among women. More importantly, cervical cancer is because by Human papillomavirus (HPV) and having regular Pap smear tests can detect HPV infection and pre-cancerous cells. Treatment of HPV can stop cervical cancer before it develops into cancer cells. Since the patterns of development of cervical cancer provide multiple opportunities for detection and treatment. This paper is divided into six sections. Section 1 and 2 discuss cervical cancer and various existing screening for cervical cancer. Section 3 provides a brief overview of the work done so far in the field of cervical cancer. Section 4 discusses the proposed mechanism and the results are described in section 5. Finally, section 6 discusses the conclusion drawn from the whole work. In this paper, an attempt has been made to propose an early detection of cervical cancer system based on some existing parameters such as smoking, the number of sexual partner and age of the person. This work has been implemented in Eclipse and analysed using Weka.

关键词： Cervical cancer Early detection machine learning Weka

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