Journal of Advances in Artificial Life Robotics
Volume 2, Issue 2, Septmber 2021
Research Article
1.A Study of Boiler Water Level System with Fuzzy Control Method
Tianyi Zhang1, Fengzhi Dai1,2, Peng Lu1
1Tianjin University of Science and Technology, China
2Tianjin Tianke Intelligent and Manufacture Technology CO., LTD, China
pp. 53–57
ABSTRACT
In this paper, the performance characteristics of the boiler water level
system are analyzed, and a fuzzy control method is used to control it based
on the three-stroke water supply system. This fuzzy control method is to
reason out the appropriate fuzzy control rules, design fuzzy controller,
and applied to the control system, so that the system for self-adjustment
of PID parameters, constitute a fuzzy PID control system. On this basis,
this paper analyzes the performance, advantages and characteristics of
two control systems: the traditional PID control system and the fuzzy PID
control system, and simulates the parameters of the input variables for
comparison and analysis.
ARTICLE INFO
Article History
Received 25 October 2020
Accepted 07 May 2021
Keywords
Fuzzy PID
Three-pulse
MATLAB
PID control
JAALR2201
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Research Article
2.Research on Feature Point Measurement Technology Based on Stereo Vision
Jiwu Wang, Xin Pei
School of Mechanical and Electronic Engineering, Beijing Jiaotong University,
Beijing, Haidian District, China
pp. 58–62
ABSTRACT
In view of the high similarity of feature points in low texture environment,
this paper proposes an interactive method of manual selection of feature
points based on stereo vision under the condition that automatic modeling
cannot meet the requirement of 3D environment. Firstly, the model of binocular
stereo vision camera with parallel optical axis is designed, then the camera
calibration is carried out, and the 3D ranging system of interactive manual
selection of feature point pairs is developed. In order to verify the effectiveness
of the system, this paper uses corridor floor tiles with fewer texture
features to carry out experimental tests. By verifying the three-dimensional
coordinates of the measured feature point pairs, and comparing with the
actual measured values, it is found that the measurement error is less
than 1%.
ARTICLE INFO
Article History
Received 02 December 2020
Accepted 08 June 2021
Keywords
Stereo vision
Camera calibration
Distance measurement
JAALR2202
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Research Article
3.A Brief Overview of Autonomous Path Planning Algorithms for UAV Applications;
reflections from a survey
Anees ul Husnain1,2, Norrima Binti Mokhtar1, Noraisyah Binti Mohamed Shah1, Mahidzal Bin Dahari1
1Department of Electrical Engineering, University of Malaya, Kuala Lumpur,
Malaysia
2Applied Controls and Robotics Research Laboratory, University of Malaya,
Kuala Lumpur, Malaysia
pp. 63–69
ABSTRACT
The past two decades of research on UAVs has revealed that about seventy
percent of it had been published in the previous four years. To serve the
exponentially increasing role of UAVs in multi-disciplinary research, the
choice for most suitable path planning algorithms is presented in this
work. The extent of autonomy in path planning for a UAV primarily depends
upon the capabilities of its algorithm. Hence, a comprehensive survey study
was proposed and conducted. This article presents a summary of the survey
and suggests most suitable path planning algorithms for a UAV application.
A collective consciousness was also developed while going through the process
and presented on how the research work on intelligent robots should be
categorized to cater future needs.
ARTICLE INFO
Article History
Received 27 October 2020
Accepted 18 June 2021
Keywords
Autonomous UAV
Survey
UAV path planning
JAALR2203
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Research Article
4.A Study of YOLO Algorithm for Multi-target Detection
Haokang Wen1, Fengzhi Dai1,2
1Tianjin University of Science and Technology, China
2Tianjin Tianke Intelligent and Manufacture Technology CO., LTD, China
pp. 70–73
ABSTRACT
With the development of deep learning, target detection has become one
of the research directions of many scholars. As one of the more mature
algorithms, the single-stage YOLO algorithms have been widely used in real
life. Combining the development history of the YOLO algorithm, this article
focuses on the main framework and main content of the current latest YOLOv5
algorithm, and uses the YOLOv5s model to identify and detect multi-target.
The test results show that YOLOv5s algorithm has good detection effect
and wide application meaning in real life.
ARTICLE INFO
Article History
Received 25 October 2020
Accepted 29 July 2021
Keywords
Target detection
YOLOv5
Deep learning
Computer vision technology
JAALR2204
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Research Article
5.A Design and Implementation of Family Potted Plant Maintenance System
Songyun Shi, Yizhun Peng, Xinpeng Yang, Yuze Si, Junjie Tai
College of Electronic Information and Automation, Tianjin University of
Science and Technology, Tianjin, 300222, China
pp. 74–77
ABSTRACT
This device is designed for the phenomenon that people often ignore the
potted plants at home and lead to the death of potted plants. The intelligent
flowerpot can make potted plants survive and grow better without supervision.
It is a smart home product based on Internet of things technology. STM32
single chip microcomputer is used to collect the data of temperature sensor,
humidity sensor, soil humidity sensor, harmful gas sensor, photosensitive
sensor and other sensors, and it is used with intelligent tracking system
composed of four DC motors, automatic irrigation system and mobile phone
app; Through machine learning, potted plants can adapt to a variety of
potted plants, so as to achieve the purpose of potted cultivation, beautification
and improvement of living environment. Aiming at the disadvantages of artificial
cultivation and potted plants in traditional family life, the maintenance
of scientific intelligence is realized. We designed this smart flowerpot.
This flowerpot not only solves the problem of life, but also adds green
to the homes of those who have no time or ability to raise flowers, even
the disabled.
ARTICLE INFO
Article History
Received 16 November 2020
Accepted 10 August 2021
Keywords
Internet of things technology
MCU
Embedded
Smart home
Ecology
WiFi
Machine learning
JAALR2205
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Research Article
6.Forest Management using Internet of Things in the Fushan Botanical Garden
in Taiwan
Shuo-Tsung Chen1, Chih-Chiang Hua2, Ching-Chun Chuang3
1Bachelor Program in Interdisciplinary, National Yunlin University of Science
and Technology;123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan,
R.O.C
2Department of Electrical Engineering, National Yunlin University of Science
and Technology;123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan,
R.O.C.
3Department of Computer Science and Information Engineering, National Formosa
University; No. 63, Wunhua Rd., Huwei Toshnship, Yunlin 632, Taiwan, R.O.C
pp. 78–82
ABSTRACT
In recent years, the technology of the Internet of Things (IoT) has developed
rapidly and has been successfully used in different fields. Moreover, the
application context of the IoT will be extended more widely. This work
applies the IoT technology to forestry management, including: 1. Transmission
of sensing data about forest information using wireless network communication
technology of Low Power Wide Area Network (LPWAN) such as LoRa and NB-IoT;
2. Apply different sensing technologies to survey resource of forest and
monitor the microclimate changes in forest. In order to verify the proposed
LPWAN communication technology, sensors, and sensor deployment, we built
LoRa and NB-IoT communication equipment (including repeat equipment) and
various sensors to transmit the real-time sensing data in the Fushan Botanical
Garden with the most diverse and complex terrain in Taiwan. The returned
data also proves the successful operation of various communication devices
and sensors.
ARTICLE INFO
Article History
Received 30 October 2020
Accepted 01 September 2021
Keywords
Internet of things (IoT)
Low power wide area network (LPWAN)
LoRa
NB-IoT
JAALR2206
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Research Article
7.Robust attitude control of micro-satellite based on Generation Adversarial
Networks fault detection and Cerebellar Model Articulation Controller fault
tolerant control
Ho-Nien Shou
Dept. of Aviation & Communication Electronics, Air Force Institute
of Technology, Gangshan, Kaohsiung, Taiwan (R.O.C.)
pp. 83–86
ABSTRACT
This paper proposes a new robust attitude control architecture for microsatellites.
Based on deep learning fault detection method, Cerebellar Model Articulation
Controller (CMAC) is used as fault-tolerant control. Using the image recognition
function of Generation Adversarial Networks (GAN), the microsatellite actuator
fault wavelet spectrum is used as the basis of training generator and discriminator
for real-time fault diagnosis and classification. When the system fault
diagnosis determines that the fault occurs, the cerebellar neural network
participates in the fault-tolerant control. Using the Gan learning ability
of generating confrontation network, the problems of insufficient sample
data and insufficient sample labeling are solved respectively. As a kind
of local learning network, CMAC has the advantages of strong generalization
ability, fast convergence speed and simple hardware and software implementation.
The simulation results show that, compared with the traditional methods,
the fault detection and fault-tolerant control of GAN method combined with
CMAC has higher accuracy and robustness.
ARTICLE INFO
Article History
Received 30 October 2020
Accepted 22 June 2021
Keywords
Deep learning
Fault-detection
Fault-tolerant control
Cerebellar model articulation controller
Generation adversarial networks
Wavelet
JAALR2207
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Research Article
8.Design a Mobile Robot with Image Recognition Function based on LabVIEW
and KNRm
Kuo-Hsien Hsia1, Bo-Jung Yang2, Jr-Hung Guo1, Chang-Sheng Xiao3
1Bachelor’s Program in Intelligent Robotics, National Yunlin University
of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin
64002, Taiwan, R.O.C
2Graduate School of Engineering Science and Technology, National Yunlin
University of Science and Technology, 123 University Road, Section 3, Douliou,
Yunlin 64002, Taiwan, R.O.C.
3Department of Electrical Engineering, National Yunlin University of Science
and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002,
Taiwan, R.O.C.
pp. 87–92
ABSTRACT
The main purpose of this paper is to use the image recognition function
of LabVIEW to construct a mobile robot with various functions, and make
it applicable to the industry having web monitoring applications. The core
of the robot is the KNRm controller which is suitable for beginners, and
can be connected to DC servo motor, RC servo motor, infrared, ultrasonic
and camera to achieve various functions of the robot. The structure of
the robot uses metal parts sold by Studica company, which can be in accordance
with the desired function to assemble the robot. Since the company is a
designated equipment sponsor company for World Skills competitions, it
can also be in line with international standards. Finally, PID control
and sensors are added to make the robot movement and position more accurately.
ARTICLE INFO
Article History
Received 12 November 2020
Accepted 18 September 2021
Keywords
Image recognition
Mobile robot
Web monitoring
KNRm
JAALR2208
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Research Article
9.Isolated Pixel Filtering-Based Image Inpainting Methods for Drawing Robots
Chun-Chieh Wang
Department of Automation Engineering and Institute of Mechatronoptic Systems,
Chienkuo Technology University, Taiwan
pp. 93-97
ABSTRACT
The purpose of this thesis is to show that isolated pixel filtering-based
image inpainting methods for drawing robots. For watercolor painting, HSI
color space is used to improve the effect of color simplification such
that the recognition of image processing results is enhanced, firstly.
Second, that less-affected isolated point color is replaced with the surrounding
color via isolated pixel filtering methods. Third, we use image inpainting
technology to reduce the distortion caused by the isolated pixel filtering.
Besides, we adjusted the path planning as well as reduced isolated points
to dramatically reduce drawing time. For sketch painting, through the image
resolution adjustment as well as the shortening of the spacing of the drawing
lines, the robot can draw more detailed pictures in the same size of drawing
space. To allow LabVIEW to directly issue commands to control the drawing
robot, the communication function has been added. The measured results
confirm that the application of the technology in this paper can shorten
the drawing time by about 57% to 59% on the drawing robot system.
ARTICLE INFO
Article History
Received 06 November 2020
Accepted 20 September 2021
Keywords
Isolated pixel filtering
Image inpainting
Watercolor
Sketch
Drawing robots
JAALR2209
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Research Article
10.Towards Multiple Perspectives of Cross-National Culture UsingSelf-Organizing Map (SOM)
Li-Min Chuang1, Yu-Po Lee2, Shu-Tsung Chao3
1The Department of International Business, Chang Jung Christian University,
No.1, Changda Rd., Gueiren District, Tainan City, 711301, Taiwan
2The Ph.D. Program in Business and Operations Management, College of Management,
Chang Jung Christian University, No.1, Changda Rd., Gueiren District, Tainan
City, 711301, Taiwan
3The Institute of Business and operation Management, Chang Jung Christian
University, No.1, Changda Rd., Gueiren District, Tainan City, 711301, Taiwan
pp. 98-103
ABSTRACT
This study integrates the previous cross-cultural literature and aims to
construct an analysis model of cross-national culture with multiple dimensions
from three important cultural dimension theoretical models commonly used
in cross-cultural studies: Hofstede, Global Leadership and Organizational
Effectiveness (GLOBE) and World Values Survey (WVS). Traditional statistical
analysis seems to be unable to solve the problem of the integration of
relevant scales and units in different dimensions of cultural analysis.
Therefore, this study uses a self-organizing map (SOM) as an analysis method
to integrate 17 cultural variables from this multicultural dimension for
cluster analysis and explains the cultural types in 26 countries based
on the results. This study explores the differences and similarities of
different countries in different cultural dimension analyses and provides
a comparative model of multicultural analysis. This study takes samples
from three cross-cultural analysis databases as data sources and employs
the self-organizing map for analysis based on a neural network algorithm
that can be used for type discrimination, map analysis, process monitoring,
and error analysis. The results identify the cross-cultural groups of 26
countries and reveal their key cultural similarities and differences. We
also elaborate upon the findings of these cultural characteristics and
multi-cultural dimensions. The signification of this study is presented
as a reference for subsequent studies of transnational and cross-cultural
analysis and its applications.
ARTICLE INFO
Article History
Received 30 October 2020
Accepted 01 September 2021
Keywords
Cross-culture
Self-organizing map (SOM)
Hofstede GLOBE WVS
JAALR2210
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