International Journal of Integrated Engineering
Not a member yet
2309 research outputs found
Sort by
Design Chart of Trough Width Value for Rectangular Shaped Tunnel
Get PDFThe rectangular tunnel has proven as the best technique for depth clearance and utilizing the space of cross-section compared with the circular tunnel. In designing the rectangular tunnel, trough width parameters found from the literature mostly were designed for circular shape tunnels only. The main objective of this paper is to develop a design chart of trough width value for rectangular tunnels through a parametric study comprising a variation of tunnel size ratios using numerical analyses. PLAXIS 2D, a finite element modelling software, was used to generate rectangular tunnel models with shield support under different conditions. Developing a design chart could increase the intention of constructing a rectangular-shaped tunnel for tunnel engineers and increase the impact on green engineering. Design charts for rectangular tunnels held at depths of 4 m, 5 m and 6 m were developed and validated with field data from the literature for the potential application of trough width value that matches clayey ground conditions. Soft soil conditions may not be applicable to be applied on these charts
Influence of Temperature and Blending Ratio on Product Yield for Co-gasification of Torrefied Palm Kernel Shell (TPKS) and Low-Density Polyethylene (LDPE)
Get PDFThis study investigated the product yields produced from the co-gasification of torrefied palm kernel shell (TPKS) and low-density polyethylene (LDPE). Prior co-gasification, PKS was undergo pre-treatment process at different temperature. The optimum parameter for torrefaction was found at 250 oC for 60 min reaction time with 4.89 wt.% moisture content and 10.48 wt.% fixed carbon. Thus, the result indicated that TPKS a suitable fuel feedstock for further thermal conversion. Then, TPKS and LDPE were gasified at different temperature and blending ratio for 60 min reaction time. The results showed that, temperature plays an important role in co-gasification. Higher gasification temperature increases the carbon conversion which improves gasification rate. By varying temperature from 600 to 1000 oC, the gas yield increased considerably from 25.88 to 45.94 wt.%, whilst tar yield decreased sharply from 49.61 to 35.03 wt.%. However, as temperature increased from 800 to 1000 oC, tar yield increased from 26.58 to 35.03 wt.%. Meanwhile, char yield decreased from 24.50 wt.% to 19.02 wt.% over the temperature range of 600 to 1000 oC. For the effect of blending ratio, through blending of TPKS and LDPE, the gas and char yield increase, while tar decrease with increase torrefied TPKS ratio. Furthermore, it was observed that the product yields obtained from the co-gasification of TPKS and LDPE at 50:50 blending ratios produce the highest gas yield with low char and tar yield than another blending ratio. Therefore, based on the effect of temperature and blending ratio on product yield shows that the optimum parameter to produce maximum gas yield with minimum tar and char yield are at 50:50 (TPKS:LDPE) blending ratio at 800oC for 60 minutes reaction time. The gas analysis exhibited the H2 composition was increased drastically with increase reaction time for TPKS:LDPE compared than UnPKS:LDPE. The high production of H2 is in accordance with the high quantity of carbon content in TPKS compared to UnPKS. As a result, the pretreatment of PKS enhanced the H2 production during co-gasification of TPKS and LDPE
Analysis of EEG Signals Between Motor Imaginary Tasks and Rest Condition for Biometric Application
Get PDFBiometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while Spectral Entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Decision Tree and Naive Bayes, were employed for the classification task. The Welch method combined with the Support Vector Machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the development of robust and reliable biometric systems in cybersecurity
Correlation Effect on Different Temperature-Humidity Range of Highly Thermal GNP/AG/ SA Conductive Ink
Get PDF
The study evaluates how the resistivity and properties of the material change in response to environmental factors such as temperature and humidity, and how these changes impact its performance in various applications. In order to develop a highly thermal graphene hybridization conductive ink, a new formulation of conductive ink was formulated using graphene nanoplatelet (GNP), silver flake (Ag), and silver acetate (SA) as conductive fillers mixed with organic solvents. The batch of chemicals was converted into a powder by undergoing sonication and stirring to create a powdery state. The powder was then treated with organic solvents, specifically 1-butanol and terpineol, and mixed using a thinky mixer to form a paste. The GNP/Ag/SA hybrid conductive ink paste was then printed on copper substrates using a mesh stencil and was cured at 250°C for 1 hour. Cyclic testing had been conducted using a cyclic bending test machine and a cyclic torsion test machine in a heat chamber with different temperature-humidity. The new formulation then was characterized base on the electrical and mechanical behaviour. After the torsion and bending tests, the GNP/Ag/SA hybrid conductive ink formulation reliability was evaluated. GNP/Ag/SA hybrid conductive ink room temperature baseline and GNP/Ag/SA hybrid conductive ink after given different temperature-humidity were compared in terms of electrical and mechanical properties. Both cyclic bending and torsion testing results showed an increasing value of resistance and resistivity with every progress of bending and torsion cycle, which displays a clear trend. The results indicate that the average resistance values at all sample points either stay constant or decrease with the increasing temperature. This observation suggests that the ink\u27s electrical conductivity remains rather stable as the temperature increases. Thus, even with rising temperatures, the ink\u27s electrical conductivity remains stable, indicating the ink\u27s capacity to preserve its integrity and structural qualities within a defined temperature range. Future research should focus on improving the adhesion, stability and reliability of stretchable conductive inks under various temperature and humidity conditions
Shortest Pathfinding in a Standard Rectangular Maze using A* Search Algorithm
Get PDFMazes are defined as a complicated system that consisted of paths or passages that causes confusion. A large-sized maze can be difficult or impossible to be solved by hand due to high time consumption. This paper presents a solution based on the A* algorithm to find the shortest route. A* algorithm is a searching algorithm that is used to find the shortest path between the initial and the final state. Two heuristic approaches are used and compared using the Manhattan and Euclidean distance. In this paper, A* algorithm is used to solve several sizes and several types of rectangular maze and is evaluated based on the average time in solving a maze for one hundred times under various scenarios. Performance evalution includes mazes with wider paths and multiple routes. Limitations arise when dealing with mazes lacking start or end points. The performance comparison favours Manhattan distance due to its efficiency over Euclidean distance. Further exploration is recommended, including the evaluation of both heuristics on larger, more complex mazes. Additionally, the study identifies the A* algorithm’s need for modification to handle mazes without defined starting or ending points. This research underscores the suitability of the Manhattan distance heuristic and suggests potential extensions of the A* algorithm to dynamic, changing, and multi-agent maze environments.
Effect of Fire-Retardant Ratios in Kenaf Fiber for Fire Resistance Properties and Acoustical Performance
Get PDFNowadays, awareness towards environmental issues increases significantly where people have learned the important of protecting environment for a better future. Thus, this study investigated the embedded kenaf fiber as a natural material with different of fire-retardant loading content towards the fire resistance properties and acoustical performance for insulator application in future building component. Based on the study conducted toward the prepared size 200 mm x 2000 mm x 40 mm of fixed kenaf composition with various fire retardant by 5 wt. % and increasing gradually to 25 wt. %, this study found that the highest value of thermal conductivity which is 0.4472 W/mK and temperature different of 240 °C for S1. Also, this study found the higher percentage of non-burn fiber which is 98.103 % for S6 and the highest peak of 0.9104 coefficient at 1259 Hz and at 1600 Hz with coefficient of 0.9091 for S3. This study showed that kenaf fiber embedded with different of fire-retardant loading content have a potential to replace the current insulator used in industry
SST Based Medium Voltage Extreme Fast Charger for Electric Vehicles Using Fuzzy-PI and ANN Controllers
Get PDFThe development of power electronics devices and the integration of intelligent non-linear loads into the existing grid with the combination of various renewable sources along with batteries are increasing. For achieving the fast response in charging stations for electric vehicles, an efficient control strategies should be introduce when the renewable sources like PV is integrated with the grid along with battery. This paper proposes the utilization of Solid State Transformers replacing the conventional transformers for achieving the bidirectional power flow in addition to attain the better controlling over the system. To achieve the fast response in the charging station to the Electric vehicles traditional PI controller is replaced with the Fuzzy-PI and Artificial Neural Networks controllers and comparative analysis of these two are also done. The framework of the charging station is done by considering three different levels of voltages in view of achieving practical layout for the project. In order to demonstrate the proposed methods simulations are done using Matlab/Simulin
Variable Frequency Drive Optimization with Adaptive Neuro Fuzzy Inference System
Get PDFThe output power of drive should be controlled to avoid stress on the advanced components in the input power system of the 1-∅ AC source; this is powered to the three-phase (3-∅) variable frequency drives (VFDs). To deal these issues, an integrated artificial neural network (ANN) and fuzzy logic control (FCL) named as adaptive neuro fuzzy inference system (ANFIS)-based VFD optimization is proposed to mitigate the stresses above the various parts of VFD such as input side, terminal block, direct current (DC) capacitor bus, current harmonics, torque ripple and speed of the induction motor (IM). In addition, the proposed ANFIS with the supervisory learning approach is utilized to regulate the speed with mitigated rise time and settling time of the VFD system. The proposed ANFIS model is simulated in MATLAB/Simulink environment and compared with several conventional VFD optimization The extensive simulated performance shows that the proposed ANFIS-based VFD has achieved better results than conventional VFD optimization techniques
Automated X-Ray Inspection (AXI) on Surface Mount Technology Resistor (SMT-Res) Defects Detection Using GAN-YOLOv8n Model
Get PDFProduct quality is a crucial factor in the electronic manufacturing sector, with inspection playing a significant role. However, human inspectors\u27 accuracy fluctuates due to factors like fatigue, turnover, experience, and inconsistent fault categorization. These inconsistencies result in longer inspection times and operator-specific quality variations. To address this challenge, AI automation within Industry 4.0 framework is increasingly adopted to replace human involvement and reduce annual costs in quality control and testing. This paper proposes an Automated X-ray Inspection (AXI) system that develops a defects detector framework for detecting and classifying Surface Mount Technology-Resistor (SMT-Res) on PCBs, using a private dataset from Intel Technologies. The YOLOv7 and YOLOv8 models are examined as detectors, and experimental results highlight the exceptional performance of the GAN-YOLOv8n model. With just 3.01M parameters, it achieves 92.9% mean average precision (mAP) at Intersection over Union (IoU) 0.5 and 71.5% mAP at IoU 0.95, demonstrating excellent accuracy and efficiency. The proposed AXI system, leveraging AI automation, offers a promising solution for improving inspection processes, reducing costs, and enhancing product quality in electronic manufacturin
Inverse Hall-Petch Nature of Fe-Ni-Cr-Co-Cu High Entropy Alloy at Atomic Scale
Get PDFHigh entropy alloy is a class material composed of several principal elements with equiatomic composition. The entropic of this material with near equiatomic configuration composition can theoretically decrease the strength-ductility trade-off, which differs their application from their classical alloy counterpart. The strength of nanocrystalline materials, known as the Hall-Petch phenomenon, tends to increase. Still, when it hits the critical grain size, the softening behavior of decreasing strength is observed and known as the inverse Hall-Petch phenomenon. Although there are numerous reports on the inverse Hall-Petch effect, both numerically and experimentally, the deformation mechanism of these materials is not clearly described, especially at the atomic scale. This study aims to tackle the issue by reporting the molecular dynamics simulation of nanocrystalline Fe-Ni-Cr-Co-Cu high entropy alloy with an average grain size of 7.3 nm3, 6.8 nm3, and 6.4 nm3. The study shows that the tensile deformation is mainly attributed to the grain boundary sliding due to the high grain boundary volume within the system. However, in the case of compressive deformation, at the earlier stage, the deformation is mainly focused on the grain boundary deformation, although the new cluster of grain has infrequently happened due to the compression. Both tensile and compressive strength exbibit the inverse Hall-Petch nature of Fe-Ni-Cr-Co-Cu high entropy alloy