scholarly journals Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

2019 ◽  
Vol 10 (1) ◽  
pp. 253 ◽  
Author(s):  
Donghoon Shin ◽  
Hyun-geun Kim ◽  
Kang-moon Park ◽  
Kyongsu Yi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Threat Assessment ◽  
Collision Probability ◽  
Driver Model ◽  
Sequential Data ◽  
Automated Driving ◽  
Human Driver ◽  
Acceleration And Deceleration ◽  
The Individual

This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision.

scholarly journals SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Felisia Handayani ◽  
Metty Mustikasari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Confusion Matrix ◽  
Data Representation ◽  
Sequential Data ◽  
Communication Tool ◽  
Electric Cars

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles

2018 ◽  
Author(s):  
Rui Li ◽  
Weitian Wang ◽  
Yi Chen ◽  
Srivatsan Srinivasan ◽  
Venkat N. Krovi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Human Vision ◽  
Automated Driving ◽  
Learning Framework ◽  
Research Platform ◽  
Automatic Parking ◽  
Fully Automatic

Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings.

scholarly journals INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 387
Author(s):  
Shuyu Li ◽  
Yunsick Sung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Generative Models ◽  
Variable Length ◽  
Sequential Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Music Generation ◽  
Automatic Music Generation ◽  
Frequency Vectors

Deep learning has made significant progress in the field of automatic music generation. At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. First, the length of the music must be determined artificially prior to generation. Second, although the convolutional neural network (CNN) is unexpectedly superior to the recurrent neural network (RNN), CNN still has several disadvantages. This paper proposes a conditional generative adversarial network approach using an inception model (INCO-GAN), which enables the generation of complete variable-length music automatically. By adding a time distribution layer that considers sequential data, CNN considers the time relationship in a manner similar to RNN. In addition, the inception model obtains richer features, which improves the quality of the generated music. In experiments conducted, the music generated by the proposed method and that by human composers were compared. High cosine similarity of up to 0.987 was achieved between the frequency vectors, indicating that the music generated by the proposed method is very similar to that created by a human composer.

scholarly journals Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN)

2021 ◽  
Vol 8 (5) ◽  
pp. 907
Author(s):  
Muhammad Yuslan Abu Bakar ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Spatial Information ◽  
Contextual Information ◽  
Computing Time ◽  
Sequential Data ◽  
Text Data

<p class="Abstrak"><span lang="IN">Hadis merupakan sumber hukum dan pedoman kedua bagi umat Islam setelah Al-Qur’an dan banyak sekali hadis yang telah diriwayatkan oleh para ahli hadis selama ini. Penelitian ini membangun sebuah sistem yang dapat melakukan klasifikasi teks hadis Bukhari terjemahan berbahasa Indonesia. Topik ini diangkat untuk memenuhi kebutuhan umat Islam dalam mengetahui apa saja informasi mengenai anjuran dan larangan yang terdapat dalam suatu hadis. Klasifikasi teks memiliki tantangannya tersendiri terkait dengan jumlah fitur yang sangat banyak (dimensi sangat besar) sehingga waktu komputasi menjadi besar dan mengakibatkan sulitnya mendapatkan hasil yang optimal. Pada penelitian ini, digunakan salah satu metode hibrid dalam dunia <em>deep learning</em> dengan menggabungkan Convolutional Neural Network dan Recurrent Neural Network, yaitu Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network dipilih sebagai metode seleksi dan reduksi data dikarenakan dapat menangkap informasi spasial yang saling berhubungan dan berkorelasi. Sementara Recurrent Neural Network digunakan sebagai metode klasifikasi dengan mengusung kemampuan utamanya yaitu dapat menangkap informasi kontekstual yang sangat panjang khususnya pada data sekuens seperti data teks dengan mengandalkan ‘memori’ yang dimilikinya. Hasil penelitian menyajikan beberapa hasil klasifikasi menggunakan <em>deep learning</em>, dimana hasil akurasi terbaik diberikan oleh Convolutional Recurrent Neural Network (CRNN), yakni sebesar 80.79%.</span></p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Judul2"><span lang="IN"> </span></p><p class="Abstract"><em><span lang="IN">Hadith is a source of law and guidance for Muslims after the Qur'an and many hadith have been narrated by hadith experts so far. This research builds a system that can classify Bukhari hadith in Indonesian translations. This topic was raised to meet the needs of Muslims in knowing what information about the suggestions and prohibitions that exist in a hadith. Text classification has its own challenges related to several features whose dimensions are very large so that it increases computing time and causes difficulties in getting optimal results. This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network was chosen as a method of selecting and reducing data that can be determined as spatial information that is interrelated and correlated. While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%.</span></em></p><p class="Abstrak"><strong><em><br /></em></strong></p>

scholarly journals Forecasting Ionospheric foF2 Based on Deep Learning Method

2021 ◽  
Vol 13 (19) ◽  
pp. 3849
Author(s):  
Xiaojun Li ◽  
Chen Zhou ◽  
Qiong Tang ◽  
Jun Zhao ◽  
Fubin Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Sequential Data ◽  
Activity Maximum ◽  
Forecasting Accuracy ◽  
Ionosphere Model ◽  
Forecasting Performance

In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them.

scholarly journals Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

2021 ◽  
Vol 2 ◽  
Author(s):  
Danshi Wang ◽  
Min Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Optical Communication ◽  
Optical Communications ◽  
Channel Modeling ◽  
Modeling Method ◽  
Learning Performance ◽  
Sequential Data

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

scholarly journals Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems

2019 ◽  
Vol 9 (17) ◽  
pp. 3470
Author(s):  
Nguyen Minh-Tuan ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Sequential Data ◽  
Short Term ◽  
Data Simulation ◽  
Assignment Problems ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Fully Connected

Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.

Joint Decision Making and Cooperative Driver-Vehicle Interaction during Critical Driving Situations

i-com ◽  
2016 ◽  
Vol 15 (3) ◽  
Author(s):  
Eugen Altendorf ◽  
Gina Weßel ◽  
Marcel Baltzer ◽  
Yigiterkut Canpolat ◽  
Frank Flemisch
Keyword(s):  
Decision Making ◽  
Complex Task ◽  
Automated Driving ◽  
Joint Decision ◽  
Vehicle System ◽  
Human Driver ◽  
Automation Systems ◽  
Driving Task ◽  
Common Goals ◽  
The Individual

AbstractIn automated driving, the human driver and an automation form a joint human-machine system. In this system, each partner has her own individual cognitive as well as perceptual processes, which enable them to perform the complex task of driving. On different layers of the driving task, both, drivers and automation systems, assess the situation and derive action decisions. Although the processes can be divided between human and machine, and are sometimes very elaborate, the outcome should be a joint one because it affects the entire driver-vehicle system. In this paper, the individual processes for decision-making are defined and a framework for joint decision-making is proposed. Joint decision-making relies on common goals and norms of the two subsystems, human and automation, and evolves with experience.

scholarly journals Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

2021 ◽  
Author(s):  
Jiyoung Byun ◽  
Yong Jeong
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Heterogeneous Data ◽  
Disease Classification ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Individual Features ◽  
The Individual

ABSTRACTDeep learning frameworks for disease classification using neuroimaging and non-imaging information require the capability of capturing individual features as well as associative information among subjects. Graphs represent the interactions among nodes, which contain the individual features, through the edges in order to incorporate the inter-relatedness among heterogeneous data. Previous graph-based approaches for disease classification have focused on the similarities among subjects by establishing customized functions or solely based on imaging features. The purpose of this paper is to propose a novel graph-based deep learning architecture for classifying Alzheimer’s disease (AD) by combining the resting-state functional magnetic resonance imaging and demographic measures without defining any study-specific function. We used the neuroimaging data from the ADNI and OASIS databases to test the robustness of our proposed model. We combined imaging-based and non-imaging information of individuals by categorizing them into distinctive nodes to construct a subject–demographic bipartite graph. The approximate personalized propagation of neural predictions, a recently developed graph neural network model, was used to classify the AD continuum from cognitively unimpaired individuals. The results showed that our model successfully captures the heterogeneous relations among subjects and improves the quality of classification when compared with other classical and deep learning models, thus outperforming the other models.

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