bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting Stock Market Price of Gold, Silver, Crude Oil and Platinum by Using Double Exponential Smoothing, Holt’s Linear Trend and Random Walk
Authors: Shukor Shuhaidah Abdul , Sufahani Suliadi Firdaus , Khalid Kamil , Wahab Mohd Helmy Abd , Idrus Syed Zulkarnain Syed , Ahmad Asmala , Subramaniam Tamil Selvan 
Published: June 2021

Abstract
Forecasting is an approach that has been used widely for ages. However, forecasting stock market price is a challenging task to be completed as the data fluctuates rapidly along the time and can be influenced by a variety of factors. Hence, this study aims to forecast the stock market price of gold, silver, crude oil and platinum by using Double Exponential Smoothing, Holt’s Linear Trend and Random Walk. Then, measures the forecast model obtained by using Sum Square Error, Mean Square Error and Root Mean Square Error which later can be used in determining the best forecasting method. Based on the analysis done, the result shows that Holt’s Linear Trend is the better forecasting method compared to Double Exponential Smoothing and Random Walk.
Compact and Wide Bandwidth Microstrip Patch Antenna for 5G Millimeter Wave Technology: Design and Analysis
Authors: Noor Shehab Khan , Ramli Nurulazlina , Sahar Norsuzlin Mohd , Khalifa Taher 
Published: June 2021

Abstract
In this paper, a compact microstrip patch antenna for 5G Millimeter wave technology has been proposed. By developing wide bandwidth characteristics, long ranges of data transfer are covered with better quality of signal transmission in 5G communication system. Modification of antenna element; patch width structured has influenced the frequency bandwidth. The design, analysis and optimization of the proposed work along with parametric analysis were performed using CST Microwave Studio. A microstrip patch antenna has been designed with high bandwidth millimeter wave, compact dimension, consistent radiation patterns and relatively higher gain at 28 GHz (mm-wave) band. FR-4 substrate has been employed in this work with a dielectric constant value of 4.3, thickness value of 1.6 mm and loss tangent value of 0.025.The proposed antenna has achieved high bandwidth which is 14.674 GHz with a reflection coefficient of -40.14 dB, Voltage Standing Wave Ratio (VSWR) value of 1.1098 dB and maximum gain of 5.29 dB with high directivity of 7.465 dBi. The antenna results of this work proved to be useful in fulfilling the requirements of wider bandwidth and higher gain particularly at 28 GHz for 5G applications.
Interval frequency estimation by cross-correlation analysis
Authors: Kostiukov I A 
Published: June 2021

Abstract
This paper presents a substantiation of interval frequency estimation which can be carried out based on frequency dependence of calculated values of cross-correlation coefficient between analyzed signal and reference sine function with zero initial phase. In considered approach the interval of frequencies which contains the exact frequency of analyzed signal is established by finding the range of frequencies limited by the maximum value of the main lobe and the closest zero crossing of this lobe. The results of carried out simulations for interval frequency estimation of a multifrequency signal are also given.
Experimental investigation on thermal conductivity and viscosity of purified aged transformer oil based SiO2, Al2O3 and TiO2 nanofluid for Electric Multiple Unit Train
Authors: Deenesh N.K. , Mahmood A. , Kadir Ghazali , Hasnan Khalid , Nafarizal N. 
Published: June 2021

Abstract
High voltage traction transformer of electric train demands reliable transformer oil with good heat transfer properties. Anyhow thermal stress due to high temperature initiates premature thermal ageing that deteriorates the properties of transformer oil even after multiple purification process. Influence of silicon dioxide (SiO 2), aluminium oxide (Al 2O 3) and titanium oxide (TiO 2) nanoparticles on treating purified aged transformer oil (PATO) has been addressed in this study. Oil samples were collected after validation process using colour condition standards. Each nanoparticle was suspended in the aged oil using magnetic stirrer and ultrasonic bath at 0.1% volume concentration. Stability analysis was conducted using photo capturing and zeta potential analyser method. Thermal conductivity and viscosity were investigated from temperature range of 30°C to 70°C. Based on the experimental results, PATO-based SiO 2 nanofluid was chosen as no sedimentation detected even after 144 hours with zeta potential value of 71.83mV. Thermal conductivity of PATO was enhanced up to 20.83%. Besides that, the suggested nanofluid obeys Newtonian fluid law with the viscosity enhancement up to 6.7%. On the other hand, it was found that the properties of PATO were improved with the suspension of SiO 2 nanoparticles, thus helps railway industry to optimize the life span of transformer oil.
Feasibility Study of Future Seaports
Authors: Ahamad N.B , Othman M. , Che Mid E. , Fahmi M I , Suboh S. M. , Guerrero JM 
Published: June 2021

AbstractPort is a focal point especially in the trade sector, where it is represented as hub for the import and export from one destination to another. Despite the fact that it gives positive impact to country specifically in economic sector but from the health sector, this activity leaves a negative impact on the environment and human health, especially those living near the port. Now, most of developed countries have made improvements by converting conventional engines (diesel engines) to electrical engines for major vehicles in ports. The same goes for shore-side ports, most of them using cold ironing (CI). However, all the modification is still raw and faced several challenges, especially in technical aspects such as voltage difference, frequency, supply / demand. It occurs due to the difference in standard voltage and frequency between shore-side and ship. Most of the ocean ship used 60 Hz. In addition, the main constraint faced is that the power supplied on the shore-side is not enough to meet the demand from customers. Thus, to make additions to the existing substation, it will require high initial costs. After evaluating the pros and cons, the new approach is taken to integrate this system into microgrid technology. Microgrid technology is a local cluster energy source with a control capability comprising Energy Distribution Resources (DER), which cover management request, storage, and loads. One of the advantages of a microgrid is that they can be connected or disconnected from the grid to operate autonomously.
An energy management system employing Direct Supply Strategy for the hybrid cogeneration application
Authors: Isa Normazlina Mat , Shariff Shafura , Wei Tan Chee 
Published: June 2021

Abstract
Cogeneration needs an efficient energy management system to ensure their components able to work with minimum cost and at the same time have the maximum efficient. Due to that, the planning on the energy operation is important part whereas the study on energy management system has been discussed widely. Hence, this article presents the Direct Supply Strategy (DiSS) with the application of Harmony Search Algorithms (HSA) to cater the energy management system optimization issue for hybrid FC-PV cogeneration. The component of hybrid FC-PV is including PEMFC, photovoltaic integrated with battery storage to meet a demand from the medical block in Hospital Temerloh. The recognized input parameters needed in the simulation is solved in MATLAB environment whereby the finding from the HSA is then benchmarked with the Genetic Algorithms in order to observe the efficiency of proposed HSA to solve the DiSS. In addition, the proposed operation mode of DiSS also presented. As a results, the total profits obtained from this strategy were estimated to be RM167 per day or RM6095.50 per year.
Optimal Allocation and Sizing of Multi DG Units Including Different Load Model Using Evolutionary Programming
Authors: Mohammed M. F. , Rahim S R A , Azmi A. , Wan Zanudin W. Z. , Hussain M H , Hashim N , Aliman O. 
Published: June 2021

Abstract
This paper presents the optimal allocation and sizing of multi distributed generation (DG) units including different load models using evolutionary programming (EP) in solving power system optimization problem. This paper also studies on the effect of multi DG placement in different load model. To optimize the power distribution system, multi DG units were used to reduce losses power distribution system. By using EP, the optimal allocation and sizing of multi-DG was determined in order to obtain maximum benefits from its installation. The propose technique was tested into IEEE 69-bus distribution system. The result shows the placement of DG can reduce power loss 89% to 98%. The placement of multi-DG unit has better performance compare to single DG.
Assessment of in-house build low cost solar panel simulator
Authors: Fauzi F , Tajudin M F N , Mohamed M F , Azmi A , Manaf N A A 
Published: June 2021

Abstract
It is well known that solar simulator is a very important equipment in solar panels research and development (R&D). Solar simulator is mainly used to assess the efficiency of solar panels. However, the cost to acquire such system is expensive and prone to malfunction when used repeatedly. The cost to repair solar simulator when malfunction is also expensive and time consuming. As an alternative, low cost solar simulator has been built independently by a group of researcher from Faculty of Electrical Engineering Technology (FTKE), University of Malaysia, Perlis (UniMAP) using 88 GU-10 halogen bulbs. This paper presents the assessment of the in-house build low cost solar panel simulator. The simulator was tested in three separate experiments to plot the current-voltage (IV) curve. The IV curves obtained from these experiments were analysed to determine the irradiance input (in Wm −2) of the solar simulator. Results have shown that the in-house build solar simulator has an input of 350 ∼ 400 Wm −2 if solar panels are suspended 20 cm above the halogen bulbs.
Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle
Authors: Ahmad Firdaus A. Z. , Azmi S. A. , Kamarudin K. , Hwai L.J. , Ali H. , Azalan M. S. Z. , Kasa Zamri Che Mat 
Published: June 2021

Abstract
Nowadays electric vehicle has increasingly gained much popularity indicated by growing global share market targeted at 30% by 2030 after recording 7.2million global stock in 2019. Compared to Internal Combustion Engine (ICE) counterpart, Battery Electric Vehicles (BEV) produce zero tailpipe emission which greatly reducing carbon footprints. Induction motor has been widely used and its control technology has evolved from scalar type volt/hertz to recent predictive control technology. This allows induction motor’s application to expand from being the workhorse of industry to become prime mover in electric vehicle, where high performance is expected. Among vector control scheme, Direct Torque Control (DTC) has gained interest over Field Oriented Control (FOC) with simpler structure, better robustness and dynamics performance yet suffer from high torque and flux ripple. In electric vehicle applications, high ripple at low speed is highly undesirable, potentially causing torsional vibration. High performance control requires speed sensor integration, which often increase complexity in the design. The work aims to review the best control technology for induction motor in electric vehicle application through performance parameter evaluation such as improvement on dynamic response, torque and flux ripple reduction, and component optimization. Several arise issues in motor control and possible methods to circumvent are highlighted in this work. In conclusion, model predictive torque control (MPTC) is the most promising scheme for electric vehicle with excellent dynamic response, good low speed performance, and 50% torque ripple reduction compared to conventional DTC and potential integration with sliding mode observer for sensorless solution.
On some logical relation properties for rough neutrosophic set
Authors: Mohd Zainal S A , Ab Ghani A T , Abdullah L 
Published: June 2021

Abstract
In this paper, we mainly investigate the relation theory and topological structures of rough neutrosophic sets which constructed by combining rough sets and neutrosophic sets. Firstly, we introduce the concept of algebra which is operations and identity relation. Then, discuss the properties of inverse, reflexive, symmetric as well as transitive relation of rough neutrosophic set. Further, regular sets form a Boolean algebra is introduced from the interior and closure of interior in rough neutrosophic sets. The results establish can help to gain more insight in rough neutrosophic sets.
Route Beaconing (RouteBea) Process in GreedLea Routing Protocol for Internet of Vehicle (IoV) Network Environment
Authors: Omar N , Yaakob N , Saidahmed M E E , Husin Z 
Published: June 2021

Abstract
Practically, vehicles tend to travel in long distances. As a result, a vehicle might attach to different network scenarios and topologies. This unique behavior in IoV brings the attention for a robust routing protocol design. For example, a vehicle that runs the same routing protocol, while it moves from one geographical area to another, it experiences different network topology requirements, and thus, the performance of the routing protocol contrasts. Consequently, the performances of the network drop. Considering a huge number of vehicles join in these networks with their high mobility, there are still having problem due to the viability of applications via different network topology. Traffic management problems come up as number of vehicle has been growing at an exponential rate. In order to make life easier, emergency response to road accidents, speed limits, and pollution checks should be considered to be observed. The common applied to this problem are observing of vehicle’s speed via CCTV cameras, speed trackers and periodic pollution checks. However, these approaches be inclined to fail as a large number of vehicles need to be observed. Therefore, GreedLea routing protocol has been develop to overcome the problem of monitoring the traffic condition and traffic congestion. In GreedLea routing protocol, path interval is provided by the host to other vehicle to update the condition of traffic and routes in certain area. The details about GreedLea routing protocol has been described in following section. The performance of the GreedLea routing in different speed and distance has been analyze and presented in result and discussion section. In the result, it shows that the performance of GreedLea increase in the packet delivery ratio (PDR) which is packet loss is less than 0.1% and reducing protocol overhead by approximately 20-60% as the vehicle’s speed increase for beaconing intervals in the range of 1-3 seconds.
An overview of object detection from building point cloud data
Authors: Mansor H , Shukor S A A , Wong R 
Published: June 2021

Abstract
3D laser scanner, also known as LiDAR (Light Detection and Ranging), is a device that able to collect dense representation of its surroundings. Its data in point cloud form is commonly used to monitor complex environments like the highways, infrastructures and buildings. The rapid development of 3D laser scanner nowadays has assisted the process of managing complicated and huge areas, especially in building and facility management. As the advancement in architectural and civil engineering increases, building spaces change frequently, as well as renovations work which consists of several items like structures (walls, ceilings, floors) and building fixtures (doors, windows). This has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, this data needs to be handled efficiently. Object recognition and classification is one of the most important process in point cloud data since it provides a full detail of building information. Object recognition is used to recognize multiple objects in point cloud data and classification process is used to classify the objects into a class based on the criteria of the objects. These processes reduce the noise and size of point cloud data to be processed. This paper provides an overview on data processing approaches, which focused on the process of object detection and classification, especially for buildings, as part of Building Information Management (BIM) and the possibility of future research in BIM modelling.
Inkjet-Printed Flexible Temperature Sensor using Hybrid Reduced Graphene Oxide – Silver Nanoparticles(rGO/AgNPs) Conductive Ink and Silver Nanoparticles Ink
Authors: Liew Q.J , Abdul Rashid N. , Lee H.W , Hawari H.F. , Md Khir M.H. 
Published: June 2021

Abstract
This paper presents a new temperature sensor, inkjet-printed with the in-house developed hybrid rGO/Ag nanoparticles ink (rGO/AgNPs ink). Its performance is studied, and the results indicate that its sensitivity is better than the commercial sensor. The meander—shaped electrodes were fabricated using drop-on-demand inkjet printer (Fujifilm Dimatix 2850 printer) on polyethylene terephthalate (PET) substrate. Compared to the sensitivity 0.0543 ?/oc of the sensor developed with commercial ink, the in-house developed sensor shows higher sensitivity 0.1086 ?/oc. Besides, the printed sensors exhibit its resistance increasing linearly with temperature from 30°C to 100°C. The bending tests results also prove that the characteristics of the sensors do not vary significantly, indicating excellent mechanical stability and flexibility. Therefore, the flexible inkjet-printed temperature sensor with the in-house hybrid rGO/AgNPs ink is recommended for the large-scale productions and implementations.
Real-time Dynamic Traffic Light ControlSystem with Emergency Vehicle Priority
Authors: Ariffin Wan Nur Suryani Firuz Wan , Keat Cheah Seng , Suriyan Terrance Prasath A/Ludhaya , Nore Nurfadzilah Aqilah Mohamad , MohamadIrfanSyaaramiMohdLazim  , HasneezaLizaZakaria  , Hashim Nur Baya Mohd , Zain Aini Syuhada Md 
Published: June 2021

Abstract
Traffic light control systems are widely used to monitor and control vehicles’ flow to provide a smooth and orderly flow of traffic by controlling which lane should be granted to pass through the intersection point without compromising on safety standards. However, a conventional traffic light system handles neither dynamic traffic flow nor the prioritization of emergency vehicles (EVs). Consequently leads to congestion as the conventional traffic light grants underutilized road lanes to have the equal cycle length as a congested lane and possibly causes an accident and delay when multiple EVs are approaching the intersection. This paper proposes an intelligent system that dynamically adjusts the cycle length for each of the lanes at an intersection based on the vehicular density and grants the different types of EVs to pass through the intersection point by assigning different priorities. The system consists of three modules, the traffic light control module, the emergency Radio-frequency identification (RFID) moduleandtheinternetmodule. Thetrafficlightcontrolmoduledetectsvehiculardensityusing ultrasonicsensorsandassignsadynamicsetofcyclelengthbasedontheindividuallanedensity condition. TheemergencyRFIDsareinstalledon differenttypesofEVsbasedonthepreset priority weights. The internet module allows the dynamic traffic light system to be controlled by authorized personnel in a real-time application.
Evaluating the Effectiveness of Course Assessments Method in Determining Student’s Competency Level in Programming
Authors: Salleh Faridah Hani Mohamed , Drus Sulfeeza Md , Gunasekaran Saraswathy Shamini , Dewi Deshinta Arrova 
Published: June 2021

Abstract
Students who choose to study in Computer Science in Malaysia at the Degree level, basically have a diverse background of academic achievement at the final level of their secondary school. Although there are minimum requirements set for admission to Computer Science courses, students tend to demonstrate different achievements after being at university. It is possible that the method of evaluation of educational institutions is not in line with the actual skills mastered by the students. Therefore, this study takes all the test data that has been collected before and assesses which type of test is actually most effective to determine the level of student’s competency in programming. This study proves that compared to other type of assessments, students’ achievement in the final examination, additional lab assessment with challenging questions and test correlated positively with the student’s programming competency. Our experiments also prove that if the final exam score is less than 60%, it is very likely that the student should be classified as incompetent. This suggests the final exam marks should carry significant weightage so that students do not just rely on carried marks to get a high grade.
A Blind Recognition Algorithm of Scrambler after Convolutional Encoder
Authors: Wang Zhongfang , Zhai Liuqun , Fu Jingwen 
Published: June 2021

Abstract
In a wireless communication system, the data is usually scrambled after channel encoding. However, existing research on blind recognition often ignores the pseudo-random scrambling of channel encoding data, which is not in line with the actual situation. To solve this problem, this paper proposes an algorithm to recognize the scrambler parameters with convolutional codes. First, we transform this problem into a cognition problem of cancelling scrambling sequence by using the properties of the polynomial of scrambler. Then, we put forward a fast judgment method after the cancellation of scrambling. The proposed method is based on the conditional entropy and can effectively identify the scrambler after the convolutional encoder. The method has a better performance of resisting error comparing to the traditional method and saves computing resources. Simulation results show that the parameters of the scrambler can still be determined effectively when the bit error rate is 6% in the case of sufficient data.
Data Augmentation Using DCGAN for Improved Fault Detection of High Voltage Shunt Reactor
Authors: Zhu Ming , Zhang Zongxi , Mei Jie , Zhou Kejian , Chen Pengan , Qi Yongka , Huang Qinqing 
Published: June 2021

Abstract
High voltage shunt reactor is an important equipment of power transmission systems. The accurate assessment of their operating status and the timely and correct diagnosis of faults and defects concern the operation safety of the entire grid. Health assessment of high voltage shunt reactors based on vibration signal, which can be used to characterize the hidden troubles of it, is a topic widely studied in deep learning and fault diagnosis. A large number of samples are needed to train the deep learning model, but it is not easy to acquire enough fault samples in the actual scene. In this paper, we utilize a Deep Convolutional Generative Adversarial Networks (DCGAN) to generate synthetic fault samples and enlarge the fault dataset to train the Convolution Neural Network (CNN) fault detection model. Results reveal that the performance through the CNN model can be improved by 3% with the synthetic samples generated by DCGAN, which is better than that of traditional Synthetic Minority Oversampling Technique (SMOTE) algorithm.