scholarly journals Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4815
Author(s):  
Kamal M. Othman ◽  
Ahmad B. Rad
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Virtual Environment ◽  
Knowledge System ◽  
Topological Map ◽  
Subsumption Architecture ◽  
Navigation Strategy ◽  
Learning Capabilities ◽  
The Individual

In this paper, we present a navigation strategy exclusively designed for social robots with limited sensors for applications in homes. The overall system integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities. The component of the system includes several modules, such as doorway detection and room localization via convolutional neural network (CNN), avoiding obstacles via reinforcement learning, passing the doorway via Canny edge’s detection, building an abstract map called a Directional Semantic Topological Map (DST-Map) within the knowledge system, and other predefined layers within the subsumption architecture. The individual modules and the overall system are evaluated in a virtual environment using Webots simulator.

An Integrated Approach for Traffic Scene Understanding from Monocular Cameras

2021 ◽  
Author(s):  
Malte Oeljeklaus
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scene Perception ◽  
Integrated Approach ◽  
Image Features ◽  
Road Users ◽  
Environment Model ◽  
Computational Performance ◽  
Spatial Reconstruction ◽  
The Individual

This thesis investigates methods for traffic scene perception with monocular cameras for a basic environment model in the context of automated vehicles. The developed approach is designed with special attention to the computational limitations present in practical systems. For this purpose, three different scene representations are investigated. These consist of the prevalent road topology as the global scene context, the drivable road area and the detection and spatial reconstruction of other road users. An approach is developed that allows for the simultaneous perception of all environment representations based on a multi-task convolutional neural network. The obtained results demonstrate the efficiency of the multi-task approach. In particular, the effects of shareable image features for the perception of the individual scene representations were found to improve the computational performance. Contents Nomenclature VII 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work and Fundamental Background 8 2.1 Advances in CNN...

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

scholarly journals Severity Estimation of Depression Using Convolutional Neural Network

2021 ◽  
Author(s):  
Attila Zoltán Jenei ◽  
Gábor Kiss
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Correlation Structure ◽  
Acoustic Features ◽  
Convolutional Network ◽  
Severity Estimation ◽  
Severity Of Depression ◽  
The Individual

In the present study, we attempt to estimate the severity of depression using a Convolutional Neural Network (CNN). The method is special because an auto- and cross-correlation structure has been crafted rather than using an actual image for the input of the network. The importance to investigate the possibility of this research is that depression has become one of the leading mental disorders in the world. With its appearance, it can significantly reduce an individual's quality of life even at an early stage, and in severe cases, it may threaten with suicide. It is therefore important that the disorder be recognized as early as possible. Furthermore, it is also important to determine the disorder severity of the individual, so that a treatment order can be established. During the examination, speech acoustic features were obtained from recordings. Among the features, MFCC coefficients and formant frequencies were used based on preliminary studies. From its subsets, correlation structure was created. We applied this quadratic structure to the input of a convolutional network. Two models were crafted: single and double input versions. Altogether, the lowest RMSE value (10.797) was achieved using the two features, which has a moderate strength correlation of 0.61 (between estimated and original).

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