bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
Authors: Yaw Chong Tak , Teoh Siew Li , Koh Siaw Paw , Yap Keem Siah , Chong Kok Hen , Low Foo Wah 
Published: August 2022

Abstract
Fault diagnosis is increasingly important given the worldwide demand on wind energy as one of the promising renewable energy sources. This systematic review aimed to summarize the fault diagnosis using Extreme Learning Machine (ELM) on wind energy. Firstly, two databases (i.e. Engineering Village (EV) and IEEE Explore were searched to identify relevant articles, using three important keywords, including Extreme Learning Machine/ELM, fault and wind. Of the 14 included studies, only eight studies mentioned the use of sensor to collect vibration signals as the fault data. Sensors were commonly installed at four places (gearbox, generator, bearing, or rotor) in the included studies. Only nine studies used either single or fusion feature extractions for the fault data. Two types of ELM (i.e. single/multi-layered or hybrid-ELM) were identified to diagnose fault. In general, studies showed the superiority of the application of ELM in producing accuracy results in fault diagnosis of WT, compared to other algorithms. Future studies should incorporate the use of real-world data, and improve on the reporting on the methodological components of the study, to better inform on the usefulness of ELM for fault diagnosis in real-world wind energy settings.
Servo Motor Controller using PID and Graphical User Interface on Raspberry Pi for Robotic Arm
Authors: Abdul Karim Muhammad Zakwan Bin , Thamrin Norashikin M. 
Published: August 2022

Abstract
The usage of the robotic arm in industries has been growing as the robotic arm can provide many advantages to the industries. However, the usage of the robotic arm has many challenges faced by the industries. One of the problems is the error of the angle position caused by the movement of the robotic arm’s joint which affects the precision of the desired position of the end-effector. Therefore, this paper aims to study the solution of the PID controller to increase the accuracy of the position of the end-effector in the X-Y plane using forward kinematic. It describes the mechanical system, design concept, and the prototype of a 6 DOF robotic arm. Initially, the angle error of the joint for 2D plane movement is captured manually using the trigonometric calculation. Then, to reduce this error, the coefficient of the PID controller, k

p
, k

i
, and k

d
are tuned automatically using Simulink and MATLAB. Then, the controller is programmed on a Raspberry Pi with a servo driver, PCA9685. There are six servo motors used as the joints of the robotic arm, namely MG996R. Each servo motor can be controlled by using a Graphical User Interface (GUI) scripted in Python Programming Language. The end position of the end-effector will be displayed on this GUI. It is found that the angle position error of the 6 DOF robotic arm for X-Y plane movement is reduced from 4.7° to 0° by using the PID controller.
IoT based Robotic Arm for Student Online Learning
Authors: Wan Faizura W. T. , Luqman M.Z.M. , Luqman Hakim A. H. , Mohd Hafiz O. , Zulkifli I. 
Published: August 2022

Abstract
Currently, robots can be used for additional purposes, people trying to perform difficult tasks. These robots also have the potential to become more powerful and use sensors not available to humans, giving supernatural abilities to guards that they may not have. Robotic arms are widely used for industry, hospitals, and education. The main function of the robotic arm is to control movement. The robotic arm is operated manually by humans. Since being attacked by this covid 19 epidemic, robotic learning has been difficult to implement where students are forced to go through online learning. Therefore, the research is allowing the students to implement robotic arms through online learning. The purpose of this study is to implement a robotic arm using the Internet of Things (IoT). The robotic arm has been developed with four Degrees of Freedom (DOF). The robotic arm can operate and be controlled by Blynk applications. By using the esp8266 Wi-Fi module, the robot arm can directly connect to the internet. In this study, the most important is to determine to move robotic arms using IoT. Thus, experiments for robotic arms using IoT were carried out.
QRS Detection in Electrocardiogram Signal of Exercise Physical Activity
Authors: Mohd Apandi Ziti Fariha , Ikeura Ryojun , Hayakawa Soichiro , Tsutsumi Shigeyoshi 
Published: August 2022

Abstract
Accurate estimation of heart beats from electrocardiogram (ECG) signals during exercise activity is a very challenging problem. Unlike standard ECG, the signals recorded during exercise activity can affect the accuracy of QRS detection due to the noises and artifacts arise from body movements activity. However, most of the studies on QRS detection often used clean and standard data for the evaluations and assumed to reflect the overall performance of detectors. In addition, there are not many methods that evaluated using real ECG signal in their studies especially during the exercise activity. Therefore, this study was undertaken to access and evaluated the performance of QRS detection algorithms on the real ECG signal data. Three well-known QRS detection algorithms were re-implemented in this study. The ECG signal recorded under realistic movement conditions and can serving as a realistic data to assess the performance of QRS detection is used. The performance of the algorithm in real ECG signal data in sitting, walking, and jogging was then presented. The results show the algorithms capable to detect the QRS in ECG signal of exercise activity.
Breast Cancer Prediction Using Artificial Neural Networks Back Propagation Method
Authors: Nasien Dewi , Enjeslina Veren , Hasmil Adiya M. , Baharum Zirawani 
Published: August 2022

AbstractResearch on breast cancer has been widely conducted and previously studied with various methods or algorithms to categorize it into benign and malignant groups. In ANN algorithm, one method called back propagation network is utilized to solve complex problems related to identification, pattern recognition prediction, and so forth. The objective of the present study is to investigate the level of accuracy and performance by ANN back propagation in predicting breast cancer. Several stages for this study are formulating the problem, collecting and processing the Wisconsin breast cancer dataset from the Kaggle site. Designing and creating an ANN algorithm system to classify cancer into malignant and benign, then examining the system to perceive the prediction accuracy, and conclude it. The results of the numerical simulation indicate that the created system of MATLAB R2016a software obtained an accuracy of 96.929% with an error of 3.071% by a combination of training parameters with epoch 1000, learning rate 0.01, goal 0.001, and hidden layer 5.
Cyber Attack Detection Dataset: A Review
Authors: Mohd Yusof Nur Nadiah , Sulaiman Noor Suhana 
Published: August 2022

Abstract
As cyber attack become more complicated, it becomes more difficult to identify breaches successfully. The inability to identify intrusions might jeopardize security services’ confidence, compromising data confidentiality, integrity, and availability. Cyber attacks like, Ping of Death, Botnets, also IP spoofing, as well as Social Engineering attacks, are becoming more common. A number of Intrusion Detection System (IDS) approaches developed to encounter cyber security intrusion. In order to discover attack patterns, the IDS performance was evaluated by employing dataset of IDS made up of network traffic properties. Intrusion detection is a classification problem in which different Artificial Intelligence techniques have been utilized to classify between legitimate also malicious network traffic. The multiple IDS datasets used to evaluate the IDS model are listed in this publication. These are new attack categories and recent datasets containing network attack features. This paper presents several IDS dataset with many existing evaluation techniques in model of IDS. Hopefully the outcome can be used in designing efficient and effective systems employing the benchmark and new IDS datasets.
Semantic Segmentation of Traffic Landmarks Using Classical Computer Vision and U-Net Model
Authors: Boudissa Mehieddine , Kawanaka Hiroharu , Wakabayashi Tetsushi 
Published: August 2022

Abstract
Due to the deterioration of traffic landmarks such as (crosswalks, lanes, speed limits, …etc) in Mie prefecture-Japan, the sudden stop rate during road crossings reached 3.4% in the year 2020. It has been deemed necessary by the local authorities to implement a reliable maintenance process for these road signs. Such a process would rely on an automatic camera-based monitoring system in order to keep track of the deterioration status of road signs. The first step consists of achieving successful detection and segmentation of these landmarks. In this paper, we try to accomplish this initial goal by using a dataset of around 13000 high resolution images taken from Mie prefecture streets. In order to achieve our goal of landmark detection we relied on some classical computer vision techniques as well as a deep learning approach. We developed a method to separate landmarks from other objects in a given image by relying on distinctive features of these landmarks. The method in question was then used to build a semi-assisted labelling tool, which was used to annotate a set of around 182 images and used them to train a CNN model to perform semantic segmentation of the road signs. In the end we managed to achieve a successful segmentation with a dice score of 78.90% on the validation set, as well as a solid proof of concept for a CNN based approach to solve this particular problem
Tongue Colour Diagnosis System Using Convolutional Neural Network
Authors: Tan Yi Chen , Shahrizal Rusli M. , Kamarudin Nur Diyana , Ab Rahman Ab Al-Hadi , Izzat Ullah Sheikh Usman Ullah Sheikh , Peng Tan Michael Loong , Shapiai Mohd Ibrahim 
Published: August 2022

Abstract
Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this paper, a tongue diagnosis system based on Convolution Neural Network (CNN) for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colours (i.e. red or pink). Several pre-processing and data augmentation methods have been carried out and evaluated, which include salt-and-pepper noises, rotations and flips. The proposed system achieves accuracy of up to 88.98% from 5-fold cross validation. Compared to the reported baseline Support Vector Machine (SVM) method, the proposed method using CNN results in roughly 30% improvement in recognition accuracy.
Comparison of RNN-LSTM and Kalman Filter Based Time Series Human Motion Prediction
Authors: Yunus Andi Prademon , Shirai Nobu C , Morita Kento , Wakabayashi Tetsushi 
Published: August 2022

AbstractMachine-human and machine-machine interaction is inevitable and needs to be considered apart from human-machine interaction. For the safety of machine interaction, the system is required to prevent any unexpected accident. This paper proposed a system that can forecast human motion to know the movement direction for one second. This research was conducted by using an RGB camera as a reliable alternative. The feature extraction process has been done by OpenPose to obtain the coordinate of human body parts in the frame. Then the coordinate data is converted to the movement data containing the distance and direction in the key points to the next frame for the input in the prediction method. This research aims for the human motion prediction using these methods to compare the performance on the human motion data and realize the prediction of human motion. Mainly, Kalman Filter shows more positive results than RNN-LSTM as the prediction method. Movement like hand gestures is more effortless than motions like hand gestures and steps to the left side. The validity of the system based on the RGB camera with unstable data has been confirmed based on the results.
Deforestation, plantation-related land cover dynamics and oil palm age-structure change during 1990–2020 in Riau Province, Indonesia
Authors: Numata Izaya , Elmore Andrew J , Cochrane Mark A , Wang Cangjiao , Zhao Jing , Zhang Xin 
Published: August 2022

Abstract
The expansion of plantations, such as oil palm, in Indonesia has caused large-scale deforestation. Loss of tropical forest, in particular peatland forest, is a major ecological and environmental threat as well as a source of atmospheric carbon emissions. Understanding the spatio-temporal dynamics of plantation expansion may illuminate pathways to reduce deforestation while maintaining high yields in existing plantations. Beyond mapping forest conversion to plantations, it is also important to understand post-conversion plantation success and crop age. In the case of oil palm, the typical productive lifespan is 25–30 years before replanting or conversion to other land use becomes necessary. Knowledge about the extent of oil palm in different productive growth stages is important for yield estimation and improving management strategies. This study characterizes the land-cover and land-use changes inherent to oil palm plantation expansion and age-structured oil palm dynamics across Riau, the province with the greatest production of oil palm in Indonesia, using a 30 year time-series of Landsat satellite imagery. From 1990 to 2020, Riau lost 4.63 M ha of forest, while oil palm extent grew six-fold, reaching an estimated 3.52 M ha in 2020. Rapid expansion of oil palm plantations in Riau resulted in the predominance of younger age classes (<10 yr-old) and rapidly increasing yields during 2010–2020. Conversion dynamics changed over time such that, after 2014, the <10 yr age class declined by 14%, whereas the 10–20 yr-old (peak yield stage) and ⩾20 yr-old (decline stage) age classes increased by 11% and 3%, respectively. In 28 years of observation (1992–2020), 41% of oil palm planted between 1990 and 1992 underwent at least one cycle of replanting in Riau.
Formation and crossover of multiple helical dipole chains
Authors: Siemens Ansgar , Schmelcher Peter 
Published: August 2022

Abstract
We investigate the classical equilibrium properties and metamorphosis of the ground state of interacting dipoles with fixed locations on a helix. The dipoles are shown to align themselves along separate intertwined dipole chains forming single, double, and higher-order helical chains. The number of dipole chains, and their properties such as chirality and length scale on which the chains wind around each other, can be tuned by the geometrical parameters. We demonstrate that all possible configurations form a self-similar bifurcation diagram which can be linked to the Stern–Brocot tree and the underlying Farey sequence. We describe the mechanism responsible for this behavior and subsequently discuss corresponding implications and possible applications.
Lie bi-algebras on the non-commutative torus
Authors: Landi Giovanni , Rajeev S G 
Published: August 2022

Abstract
We use the canonical trace on the non-commutative torus

T

θ

2

to construct a Lie bi-algebra splitting of the algebra of its smooth functions. In the special case of rational parameter

θ
=

M

N

(the algebra is then generated by clock and shift matrices) this Lie bi-algebra is

G
L

̲

(

N

)

=

U

̲

(

N

)

⊕

B

̲

(

N

)

, corresponding to unitary and upper triangular matrices. The Lie bi-algebra has a remnant in the classical limit N → ∞: the elements of

U

̲

(

N

)

tend to real functions while

B

̲

(

N

)

tends to a space of complex analytic functions. The limit results into an infinite dimensional Lie bi-algebra for the smooth functions on the commutative torus.
Entropy growth during free expansion of an ideal gas
Authors: Chakraborti Subhadip , Dhar Abhishek , Goldstein Sheldon , Kundu Anupam , Lebowitz Joel L 
Published: August 2022

Abstract
To illustrate Boltzmann’s construction of an entropy function that is defined for a microstate of a macroscopic system, we present here the simple example of the free expansion of a one dimensional gas of non-interacting point particles. The construction requires one to define macrostates, corresponding to macroscopic variables. We define a macrostate M by specifying the fraction of particles in rectangular boxes ΔxΔv of the single particle position-velocity space {x, v}. We verify that when the number of particles is large the Boltzmann entropy, S
B(t), of a typical microstate of a nonequilibrium ensemble coincides with the Gibbs entropy of the coarse-grained time-evolved one-particle distribution associated with this ensemble. S
B(t) approaches its maximum possible value for the dynamical evolution of the given initial state. The rate of approach depends on the size of Δv in the definition of the macrostate, going to zero at any fixed time t when Δv → 0. Surprisingly the different curves S
B(t) collapse when time is scaled with Δv as: t ∼ τ/Δv. We find an explicit expression for S
B(τ) in the limit Δv → 0. We also consider a different, more hydrodynamical, definition of macrostates for which S
B(t) is monotone increasing, unlike the previous one which has small decaying oscillations near its maximum value. Our system is non-ergodic, non-chaotic and non-interacting; our results thus illustrate that these concepts are not as relevant as sometimes claimed, for observing macroscopic irreversibility and entropy increase. Rather, the notions of initial conditions, typicality, large numbers and coarse-graining are the important factors. We demonstrate these ideas through extensive simulations as well as analytic results.
Factors affecting safety leadership of construction project owners in Indonesia
Authors: Indrayana D V , Pribadi K S , Marzuki P F , Iridiastadi H 
Published: August 2022

Abstract
Construction is an industry with a high possibility of risk. The construction industry accounts for the highest number of work accidents globally, with more than 60,000 fatal accidents per year. A construction safety culture through mature safety leadership can be the fundamental factor to improve safety performance. Safety leadership is the process of interaction between leaders and followers to achieve organizational safety goals. In many cases of work accidents in construction projects in Indonesia, the leadership of the project owner is rarely discussed. It is more often determined that the responsible stakeholder is the contractor. Leadership by the project owner is the main factor for generating new and effective safety management measures. This study aims to determine the factors that influence the Safety Leadership of construction project owners in Indonesia. This study uses the Delphi round technique to analyze experts’ judgments to avoid biases. This study results in obtaining several factors and indicators that are considered to have an influence on the safety leadership of construction project owners in Indonesia.
The effect of land subsidence on hydrodynamic conditions in Pekalongan coast
Authors: Martin R J , Kurniawan A , Nugroho E O , Wijaya F S , Muhammad I F , Yola  , Fauzi N 
Published: August 2022

Abstract
Due to land subsidence at Pekalongan, some areas at Pekalongan coast submerged at a rate up to 10 cm/year. This study was conducted to examine the effect of land subsidence on the hydrodynamic conditions in Pekalongan through a numerical model simulation using Delft3D. There are 2 scenarios for the numerical model, scenario 1 in 2016 and scenario 2 in 2021 after the occurrence of land subsidence at a rate of 10 cm/year. Model calibration was carried out on water level elevation and current velocity by comparing the simulation data and field data based on the variation of the Chezy number. The calibrated Chezy number parameter was 60 m0.5/s. The sensitivity test is carried out by changing the input parameters such as water level elevation, river discharge, and wave height. The sensitivity test results shows that the most sensitive parameter is wave height. The comparison of the simulation results of the two scenarios shows that the current velocity decreased by about 0.05 m/s to 0.1 m/s and the wave height increased by about 0.05 meters to 0.15 meters after land subsidence occurred. Land subsidence causes the waters in the study area to become deeper, and the results of the study show that current velocity tends to decrease and wave height tends to increase because of the land subsidence.
Mechanical Characteristics Of Concrete With The Addition Of Integral Waterproof Using Local Aggregate East Kalimantan
Authors: Jamal Mardewi , Jazir Alkas M , Haryanto Budi , Rasyid Ahmad 
Published: August 2022

Abstract
Nowadays, there are various ways to deal with leaks in concrete. One way that is often used to deal with leaks is to provide a waterproofing or waterproofing layer on the surface of the floor or wall. Along with technological developments, research on the quality of concrete waterproofing continues to be improved. One of them is the use of Concrete Integral waterproofing, which is a mixture of concrete that is added with integral waterproofing to minimize water penetration into the concrete, thereby increasing the concrete’s impermeability and protecting the concrete against leakage. The purpose of this study was to determine the effect of adding Integral waterproofing to the compressive and tensile strength of concrete and to determine the optimum level of addition of Integral waterproofing. The aggregates used are Palu coarse aggregate and Kalimantan local fine aggregate to utilize the natural resources in East Kalimantan. Addition of Integral Waterproofing into the concrete mixture with a percentage of 0%, 0.5%, 1% and 2% of the cement weight. Concrete testing was carried out at the age of 28 days by testing its mechanical properties, namely compressive strength and tensile strength. Based on research results, for concrete that uses Mahakam fine aggregate, the highest compressive strength value at 2% addition is 34.73 MPa, and the highest tensile strength value at 2% addition is 2.38 MPa. And for concrete that uses Sambera fine aggregate, the highest compressive strength value at 2% addition is 30.91 MPa, and the highest tensile strength value at 1.5% addition is 2.34 Mpa.