Journal of Advances in Artificial Life Robotics
Volume 3, Issue 3 December 2022
Research Article
1.A Multi-Material Joint System as a Three-Dimensional Spring-Damper Compliant
Mechanism Toward Functional Versatility
Pancho Dachkinov1, Anirudha Bhattacharjee2, Bishakh Bhattacharya2, Hiroaki Wagatsuma1,3
1Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-Ku, Kitakyushu,
808-0196, Japan
2Indian Institute of Technology Kanpur, Kanpur-208016, India
3RIKEN CBS, Japan
pp. 128–134
ABSTRACT
3D printed material designs have a large capability in the functional aspect.
A cross-spring compliant joint is a hot topic in the sense of multipurpose
applications in various engineering fields. A combination of flexible materials
embedded in the structural form performs a motion of the system according
to deformation of parts elastically, which may fit to a specific engineering
purpose. In the present study, a traditional cross-spring pivot (CSP) was
improved to provide effectively frictionless and wear free in-plane motion.
The behavior of the joint was analyzed based on a non-linear FEA computer
analysis to focus the properties with various loading conditions. Improved
compliant joints will open a new door for designs of high-precision actuators
and robotic manipulators.
ARTICLE INFO
Article History
Received 25 November 2021
Accepted 11 August 2022
Keywords
Cross-spring pivot
Compliant mechanisms
FEA simulation
Multi-material joint
JAALR3301
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Research Article
2.Design of Control System for Daylily Picking Robot Based on Binocular Vision
Jiaxin Li, Zhuning Liu, Min Zhao
College of Electronic Information and Automation, Tianjin University of
Science and Technology, 300222, China
pp. 135–138
ABSTRACT
Daylily is loved by people because of its unique nutritional value, but
because of its unique biological characteristics, the harvesting environment
of daylily is poor, and the long-term harvesting may cause certain injuries
to the workers' hands. This paper aims to develop a picking robot with
strong applicability by studying and summarizing the biological characteristics
of daylily, which can accurately identify the picking parts through binocular
recognition system and complete the intelligent picking process of daylily.
ARTICLE INFO
Article History
Received 22 November 2021
Accepted 27 October 2022
Keywords
Picking robot
Daylily
Agricultural robot design
Machine Vision
JAALR3302
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Research Article
3.Development of a Low Cost Intelligent Parcel Box with Enhanced Security
Ahmad Luqmanulhakim bin Mohd Rusli, Wan Norsyafizan Wan Muhamad, Suzi Seroja
Sarnin, Meor Mohd Azreen Meor Hamzah, Noriza Othman
School of Electrical Engineering, College of Engineering, University Teknologi
MARA, Malaysia
pp. 139–146
ABSTRACT
The covid-19 pandemic has accelerated the growth of online shopping and
e-commerce. While shopping and e-commerce have made it convenient for customers
to purchase products, issues such as failed/delayed delivery and missing
parcels need to be addressed to provide a seamless and secure shopping
experience for customers. In Malaysia, the implementation of Pos Laju Ezi
Box Kiosk as a delivery system still faces challenges such as upfront costs,
high cost of maintaining a management system and vulnerabilities in wireless
communication. To overcome the aforesaid problem, this paper proposes a
low-cost smart parcel box system with enhanced security. All of the designed
system's processes were controlled by an Arduino Mega 2560 in this system.
The system will start when couriers message the user via applications with
the tracking number for the package in order to obtain the password. When
the courier's message and the user-specified message match, the password
will be given for security reasons. Once the password has been entered,
couriers can insert the package into the parcel box. The system provides
a convenient and secure solution for the delivery and retrieval of packages,
reducing the risk of theft or damage to the packages..
ARTICLE INFO
Article History
Received 20 November 2021
Accepted 23 February 2023
Keywords
Smart parcel box
Arduino mega 2560
Infrared sensor (IR)
Internet of things
JAALR3303
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Research Article
4.A Research on Image Defogging Algorithm based on Image Enhancement
Fangyan Li, Haokang Wen, Chang Sheng Hamzah, Noriza Othman
College of Electronic Information and Automation, Tianjin University of
Science and Technology, 300222, China
pp. 147–150
ABSTRACT
In order to solve the problem of low contrast image and loss of image details
in the foggy weather, the image defogging technique is used to remove the
noise in the image and improve the image contrast, so as to recover a clear
and fog-free image. In this paper, we mainly introduce three image defogging
algorithms: global histogram equalisation, local histogram equalisation
and the Retinex algorithm. The advantages and shortcomings of each algorithm
are summarised through the study of the principles of each algorithm and
the comparative analysis of the experimental result.
ARTICLE INFO
Article History
Received 22 November 2021
Accepted 02 October 2022
Keywords
Digital image processing t
Image enhancement
Foggy degraded image
Dehazing algorithm
JAALR3304
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Research Article
5.Diagnostic Aid of Palm Image Relating to Asthma Based on Convolution Neural
Network
Kunyu Yu, Hiroshi Matsuki
System Information, Ashikaga University, Ashikaga, City, 3260822, Japan
pp. 151–154
ABSTRACT
In the long history of TCM, it has been said that the shape and number
of wrinkles, the depth of the wrinkles and the roughness of the matricular
area of the palm are closely related to medical conditions such as asthma
and allergic diseases. This study focuses on palmprint classification using
deep learning based on TCM. This paper made the palm image dataset according
to the characteristics of the wrinkles. In training, an inception V3 model
using the Tensor Flow framework and Google Net was used to correctly classify
the palmprint part of the thumbprint as negative or positive with a certain
probability. This result expected to facilitate diagnosis to asthma and
allergic diseases.
ARTICLE INFO
Article History
Received 11 November 2021
Accepted 14 March 2023
Keywords
Diagnostic aid system
Chinese medicine
Palmprint
Support for TCM
JAALR3305
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Research Article
6.Automatic Dry Waste Classification for Recycling Purposes
Muhammad Nuzul Naim Baharuddin1, Hassan Mehmood Khan1, Norrima Mokhtar1, Wan Amirul Wan Mahiyiddin1
Heshalini Rajagopal2, Tarmizi Adam3, Jafferi Jamaluddin4
1Department of Electrical Engineering, Faculty of Engineering, University
of Malaya, Malaysia
2Institute of Computer Science and Digital Innovation, UCSI University,
56000 Kuala Lumpur, Malaysia
3Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia
81310 Johor Bahru, Johor, Malaysia
4UM Power Energy Dedicated Advanced Centre, Universiti Malaya, 50603, Kuala
Lumpur Malaysia
pp. 155–162
ABSTRACT
In recent decades, the rapid growth of urbanization and industrialization
has resulted in a significant increase in solid waste, creating an urgent
issue that demands attention. The accumulation of solid waste poses a significant
challenge, as it can lead to environmental pollution. Recycling is a viable
solution that offers economic and environmental benefits. To address this
challenge, various intelligent waste management systems and methods are
necessary. This research paper explores the use of image processing techniques
to classify different types of recyclable dry waste. The study proposes
an automated vision-based recognition system that includes image acquisition,
feature extraction, and classification. The intelligent waste material
classification system extracts 11 features from each dry waste image. The
study employed four classifiers - Quadratic Support Vector Machine (Q-SVM),
Cubic Support Vector Machine (C-SVM), Fine K-Nearest Neighbor (Fine KNN),
and Weighted K-Nearest Neighbor (Weighted KNN) - to categorize the waste
into distinct classes, such as bottle, box, crumble, flat, cup, food container,
and tin. Among these, the C-SVM classifier performed impressively well,
achieving an accuracy of 83.3% and 81.43% during training and testing,
respectively. This classifier exhibited consistent performance and had
a shorter computation time, making it a highly effective method. Although
using the Speeded-Up Robust Features (SURF) method could enhance the classification
process, it may lead to longer response and computation times.
ARTICLE INFO
Article History
Received 11 November 2021
Accepted 28 March 2023
Keywords
Support vector machine
Recycling
Feature extraction
Classification
JAALR3306
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Research Article
7.A Comparative Analysis of Serial and Parallel Models in the Morphological
Component Analysis-Based Structure Pattern Extraction for Aerial Image
Edge Detections
Wataru Oshiumi, Ankur Dixit, Hiroaki Wagatsuma
Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196,
Japan
pp. 163–168
ABSTRACT
A refinement process by human experts is still needed for areas/feature
extractions of interest from aerial images in multiple map makers. The
construction/road edge extraction is one of them which is highly important
in the preprocessing stage for map-making, while it is difficult to isolate
man-made structures from a natural landscape involving different size objects.
In the present study, we focused on an actual procedure in the decomposition
of the Morphological Component Analysis known as MCA to extract specific
patterns as serial and parallel models. In our computer experiments, dictionaries
of Curvelet and Local Discrete Cosine Transform known as LDCT were introduced
for the MCA decomposition and then two models demonstrated a non-negligible
difference in the feature extraction performance. This result may contribute
to the extension of future possibilities of structural data analyses especially
for buildings and roads from shapes in nature.
ARTICLE INFO
Article History
Received 25 November 2022
Accepted 31 March 2023
Keywords
Image processing
Edge detection
MCA
Curvelet
Local discrete transform
JAALR3307
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Research Article
8.Measuring Aggressiveness of Human Subject by Using BPAQ Survey and Brain
Signals
Wan Khairunizam1, Kai Xu tung1, M. Lugieswaran1, Wan Azani Mustafa1, Hashimah Ali1, Hafiz Halin1, Zuradzman M. R2, Shahriman A. B2, Norrima Mokhtar3
1FKTE, Universiti Malaysia Perlis, 02600 Perlis, Malaysia,
2FKM, Universiti Malaysia Perlis,02600 Perlis, Malaysia
3Department of Electrical Engineering, Faculty of Engineering, University
of Malaya, Malaysia
pp. 169–173
ABSTRACT
Aggression is a human behaviour that has the potential to injure another
person physically or emotionally. The aggression states of the human could
be identify by using BPQ, however, some of the subjects are not honest
while answering the survey. This effects the precision of the survey. In
this studies, experimental works have been conducted to investigate the
aggression states of the 10 subjects. The experimental protocol is proposed
for inducing aggression while brain signals are recorded by using an electroencephalogram
(EEG). The subjects are experience 4 states which are relaxing state before
starting the experiment, aggression state when playing a video game in
muted, aggression state when playing video game in a maximum volume, and
post gaming state which is similar with the relaxing state but the time
is after playing the video game. The subjects are asked to play a video
game “Subway Surfers” while the brain signals are recorded. The original
data may contain noises and a Butterworth filter is used to screen it.
Then, the signals are cut into pieces called the window to extract significant
features of the brain signals. The experimental protocols and signal processing
techniques proposed can generate the aggression pattern.
ARTICLE INFO
Article History
Received 18 November 2021
Accepted 06 April 2023
Keywords
Aggressiveness
Electroencephalogram
Pattern
Experiment protocol
JAALR3308
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Research Article
9.A Dynamic Nurse Scheduling Approach Using Reinforcement Learning to Address
Sudden Absences of an Unknown Nurse
Masato Nagayoshi1, Hisashi Tamaki2
1Niigata College of Nursing, 240 shinnan-cho, Joetsu, Niigata 943-0147,
Japan
2Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan
pp. 174–178
ABSTRACT
Creating work shift schedules for nurses can be a complex task, as it involves
satisfying various requirements that can be difficult to reconcile. Although
several studies have investigated the nurse scheduling problem, creating
practical work schedules with numerous constraints and evaluation values
can still be challenging. To address this issue, we have proposed a method
for work revision that utilizes reinforcement learning to improve a constructive
nurse scheduling system. In this article, we extend the proposed method
to accommodate dynamic nurse scheduling, wherein work schedules are revised
or rescheduled in response to sudden absences. Specifically, we demonstrate
the effectiveness of our approach in creating feasible work schedules for
an unknown nurse who may be absent at any given time, through computational
experiments.
ARTICLE INFO
Article History
Received 15 November 2022
Accepted 14 April 2023
Keywords
Reinforcement learning
Dynamic nurse scheduling
Sudden absences
JAALR3309
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Research Article
10.Using OpenCV for Augmented Reality Applications
Gabdullina Dinara, Zykov Evgeniy, Kugurakova Vlada
Kazan Federal University, Kazan, Russian Federation, 420008, Rep. Tatarstan,
Kazan, St. Kremlin, 35, Russia
pp. 179–184
ABSTRACT
This article describes the use of OpenCV for use in augmented reality applications.
The implementation of the object recognition functionality is described
using the basic algorithms: FAST, BRIEF, as well as by applying a mask
and defining contours. Attention is also paid to the practical application
of this technology on the example of an augmented reality application to
assist a specialist in mounting radio-electronic components on a printed
circuit board. Experiments were carried out to measure the recognition
time depending on the degree of illumination. An analysis of the filters
used for image preprocessing has been carried out..
ARTICLE INFO
Article History
Received 24 November 2022
Accepted 18 April 2023
Keywords
Augmented reality
Object recognition
Computer vision
Optical control
Production automation
JAALR3310
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