Deep reinforcement learning has been successful in solving common autonomous driving task such as lane keeping by simply using pixel data from front view camera as input. However, raw pixel data contains very high-dimensional observation that affects the learning quality of the agent due to the complexity imposed by a ‘realistic’ urban environment. As a result, we investigate how compressing the raw pixel data from high-dimensional state to low-dimensional latent space using a variational autoen...
The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The one-against-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two con...
Internet of Things (IoT) and fuzzy logic are very useful in increasing the efficiency and effectiveness of a system, this study applies both to the street lighting systems. The prototype of a street lighting control and monitoring system has been completed. The status of lights that are on or off and the value of the light intensity can be monitored by using IoT. The intensity of the light is fuzzy controlled by utilizing the presence of vehicles and pedestrians around the lights. The prototype ...
The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an ...
This article presents the sliding control method combined with the self-adjusting neural network to compensate for noise to improve the control system's quality for the Two-Wheel Self-Balancing robot. Firstly, the dynamic equations of the Two-Wheel Self-Balancing robot built by Euler - Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the Two-Wheel Self-Balancing r...
Last. Sobir Sobir(IPB: Bogor Agricultural University)H-Index: 6
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Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanato...
#1Phat Nguyen Huu(HUST: Hanoi University of Science and Technology)H-Index: 5
#1Phat Huu Nguyen(HUST: Hanoi University of Science and Technology)H-Index: 1
Last. Cuong Vu Quoc(HUST: Hanoi University of Science and Technology)
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Nowadays, there are many smart parking lots using plate detection system, which is set in the entrances to control in/out vehicle. It is noticeable that those systems have drawbacks such as: working in a fixed environment, necessity of manual labor and requirement of checkpoints set in entrances. To overcome those problems, this paper proposes a car numbers detection system using a cutting-edge technology of machine learning. A Convolutional Neural Network (CNN)-Warped Planar Object Detection (W...
Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automati...
Hidden null M null arkov models (HMMs) are one of machine learning algorithms which have been widely used and demonstrated their efficiency in many conventional applications. This paper proposes a modified posterior decoding algorithm to solve hidden Markov models decoding problem based on MapReduce paradigm and spark’s resilient distributed dataset (RDDs) concept, for large-scale data processing. The objective of this work is to improve the performances of HMM to deal with big data challenges. ...
In this work, we will present the rectifications that we brought on Recursive Entity Modeling Method into lasts articles. Then, we will establish the Recursive Entity Modeling Method global conceptual diagram in order to conceive a space name and database for this method in future works. Therefore, in the first paragraph, we will present an overview of the Recursive Entity Modeling Method conceptual model, then, in a second paragraph, we will present Recursive Entity Modeling Method revisited el...