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Analysis of Optimizer Effects on CNN Model for Defect Identification in Welding Results of A 106 Grade B Carbon Steel Pipe

Analysis of Optimizer Effects on CNN Model for Defect Identi...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Al Amin, Mochammad Karim Wahyudi, Mohammad Thoriq Yaqin, Muhammad Ainul Adhitya, Ryan Yudha Anggara, Dika Alliyrahmanda, Aygasta Firstya Munir, Moh Miftachul Septian, Eka Cahya Welding Engineering Study Program Shipbuilding Institute Polytechnic of Surabaya Surabaya Indonesia Automation Engineering Study Program Shipbuilding Institute Polytechnic of Surabaya Surabaya Indonesia PT. Kintomo Engineering Group Surabaya Indonesia

ISBN: (纸本)9798350381290

Welding is one of the sectors that play a crucial role in supporting the rapid development of infrastructure. However, in the process, many defects occur that sometimes escape inspection and will lead to damage in building construction. The purpose of conducting this research is to investigate the influence of optimization methods on Convolutional Neural Network (CNN) models in detecting the quality of welding results based on three conditions: Normal, Excessive Reinforcement, and Undercut. The area to be detected is a portion of the welding result with a circular welding path around a full A106 Grade B carbon steel pipe. The image capture process is carried out using a digital camera mounted on a tool for rotating around the pipe to follow the welding path. The captured image result of 3024 x 3024 pixels is first processed by converting RGB to grayscale. Afterwards, the image is resized to a smaller scale of 128 x 128 pixels to accelerate the training process. Finally, the training and testing processes are conducted using a CNN model. In the CNN model, there are several optimizers such as SGDM, Rmsrop, and Adam. In this study, a comparison is conducted to find the best optimizer in an effort to minimize the occurrence of overfitting as well as achieve optimal detection accuracy. For optimal detection, a dataset of 300 images is required, consisting of: 100 Normal images, 100 Excess Reinforcement images, and 100 Undercut images. © 2023 IEEE.

关键词： Neural network models

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Potato Leaf Disease Detection by Means of Vision Transformer and CoAtNet Architecture 5

Potato Leaf Disease Detection by Means of Vision Transformer...

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5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Kenan Sergio, Mesa Anugerah Stevano Siallagan, Ariel Brandon Joelianto, Endra Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

Potatoes are a food source that is beneficial for humans in various aspects, such as having nutritional and economic value. However, potato plants are susceptible to many diseases. To address this issue, various researchers have suggested methods utilizing machine learning and image processing for automatic disease type detection. This paper introduces a disease detection model for potato leaf images, employing two computer vision techniques: Vision Transformer (ViT) and CoAtNet. The dataset used in this paper consisted of 30,760 augmented images divided into seven classes. The ViT model had a test accuracy of 99.32% and a validation accuracy of99.12%. Meanwhile, the CoAtNEt model had a test accuracy of 99.58 % and a validation accuracy of 99.48 %. These results show that both models can be used in the detection of potato leaf diseases. © 2024 IEEE.

关键词： CoAtNet image processing leaf disease potato vision transformer

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health 5

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

引用

5th IEEE International Conference on Technology, Informatics, Management, engineering and Environment, TIME-E 2024

作者： Albert Panggabean, Good Fried Romadhon, Rahmat Lubis, Ramot Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Toba Indonesia Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health. © 2024 IEEE.

关键词： DDPG indoor vertical farming precision agriculture reinforcement learning smart farming

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Human Following Robot Based on Object Detection for Aerospace Application 1

Human Following Robot Based on Object Detection for Aerospac...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

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Enhancing Industry's Role in Vocational Education: An Analysis of Challenges and Opportunities Based on a Literature Review

Enhancing Industry's Role in Vocational Education: An Analys...

引用

STEM Education Conference (iSTEM-Ed), International

作者： Maman Somantri Resa Pramudita Electrical Engineering Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia Industrial Automation and Robotic Engineering Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

Enhancing industry engagement in vocational education is a crucial strategy for refining the quality of graduates to meet the continuously evolving job market needs. This article conducts a systematic review of the existing literature to identify the challenges and opportunities in industry involvement in vocational education. Through in-depth analysis of various sources, the article reveals that the skill gap between the vocational education curriculum and industry needs remains a significant issue. This challenge is exacerbated by assessment and certification systems that often do not accurately reflect the actual competencies required by the industry. Furthermore, the article explores opportunities through internship and work placement programs that enable students to gain relevant experience, as well as strategic partnerships between educational institutions and industries to develop responsive curricula. The article also recommends enhancing the synergy between vocational education and industry through policies that support this integration, which will not only improve the quality of vocational education but also expand employment opportunities for graduates. This research indicates that more integrated and sustained cooperation between vocational education and the industrial sector is key to ensuring the relevance of education with the dynamics of the global job market.

关键词： Industries Systematics Bibliographies Education Refining Employment Globalization

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Optimizing Methylammonium Tin Iodide-based Perovskite Solar Cells with Different Hole Transport Materials

Optimizing Methylammonium Tin Iodide-based Perovskite Solar ...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： Budi Mulyanti Jahril Nur Fauzan Chandra Wulandari Lilik Hasanah Roer Eka Pawinanto Study Program of Electrical Engineering Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Electrical Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Physics Institut Teknologi Bandung Bandung Indonesia Study Program of Physics Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350378313

ISBN: (纸本)9798350378320

Perovskite materials have garnered significant attention in the field of solar cell applications due to their highly promising performance. However, the utilization of lead (Pb) as the primary material in perovskite solar cells has been associated to health and environmental hazards. Consequently an alternative material for lead is necessary in order to develop environmentally friendly and high-efficiency solar cells. The reported efficiency value still necessitates further enhancement, indicating that the development of lead-free solar cells requires improvement. Methylammonium tin iodide (MaSnI 3 ), a lead-free perovskite compound, can be utilized as a substitute for lead halide-based solar cells. This substitution is possible since MaSnI 3 is stable, non-toxic, and has a high efficiency. The simulation was conducted by the Solar Cell Capacitance Simulator (SCAPS-1D), employing the AM 1.5G spectrum. The primary objective of this research is to enhance the performance of the hole transport layer materials in order to achieve a high power conversion efficiency (PCE) using MaSnI 3 material. Organic and inorganic HTL such as CuSBS, PEDOT:PSS+WO 3 , Spiro-OMeTAD, P3HT, NiO, Cul, SrCu 2 O 2 and PTAA are required in our simulations. These materials were selected to explore their potential in optimizing device performance due to their distinct properties, such as conductivity, stability, and compatibility with MaSnI3. Regarding HTM performance for device construction, our modelling results indicate that the 100 nm CuSBS 2 layer works better. We have found that by using the configuration of ITO/TIO 2 /MASnI 3 /CuSBS 2 /Au, a PCE of 27.67% with a Voc of 0.97 V can be achieved.

关键词： Performance evaluation Semiconductor device modeling Photovoltaic cells Lead Tin Perovskites Stability analysis

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Real-Time Hand Gesture Recognition for Humanoid Robot Control Using Python CVZone 7th

Real-Time Hand Gesture Recognition for Humanoid Robot Contro...

引用

7th International Conference on Smart City Applications, SCA 2022

作者： Baihaqi, Muhammad Yeza Vincent Simatupang, Joni Welman Graduate Institute of Automation and Control College of Engineering National Taiwan University of Science and Technology Taipei Taiwan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Study Program of Electrical Engineering President University Bekasi Indonesia

ISBN: (纸本)9783031268519

Humanoid robot is able to mimic repetitive activities done by human. By having them, humans can focus on tasks that require extra focus, while leaving the simple one to be done by humanoid robot. To extend the functionality of the 3D-printed upper-body humanoid robot, a camera along with a machine learning algorithm is implemented. In this case, their responses can be categorized based on the gesture it sees. In this paper, the authors evaluate the performance of an upper-body humanoid robot that implements CVZone. The method of evaluation is conducted under three different distances, and each distance has three different brightness levels. It is found that the success rate is highest at the shortest distance which is 50 cm, and under medium-lighted environment. As the distance increases, the hand gesture gets harder to differentiate. At the same time, low or high brightness environment makes the image that contain hand gesture hard to interpret, thus lowering the success rate. Finally, hand gesture that involves third and little finger has the lowest success rate of 75%, while the movement that involves three fingers simultaneously has the second lowest success rate of 80.9%. Meanwhile, simple hand gesture such as clenched-fist or lifting a thumb has 100% success rate under all condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anthropomorphic robots

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Enhancing Localized Surface Plasmon Resonance Response for Albumin Detection by Optimizing the Lateral Size of Hexagonal Gold Nanoparticles

Enhancing Localized Surface Plasmon Resonance Response for A...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： Silva Nurfasha Chandra Wulandari Lilik Hasanah Ahmad Aminuddin Yanurita Dwi Hapsari Mohammad Arifin Roer Eka Pawinanto Budi Mulyanti Study Program of Physics Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Physics Institut Teknologi Bandung Bandung Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Study Program of Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Electrical Engineering Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350378313

ISBN: (纸本)9798350378320

The kidney is a human organ with numerous vital functions, mainly for blood filtration. Damage to the kidneys can be detected from albumin levels in urine, higher levels of albumin in urine indicate the kidney's filtration function is impaired. The latest breakthrough in the medical field is the use of localized sensor plasma resonance (LSPR) in the field of biosensors to detect albumin protein in urine. Using Finite-Difference Time-Domain (FDTD) simulation, the lateral size of hexagonal gold nanoparticles (AuNPs) is varied to increase the optimal LSPR. AuNPs are well-known as the most promising material for plasmonic applications. In several studies, the hexagonal geometry was reported to increase the surface area of nanoparticles as well as enhance the LSPR signal. Varying the geometry of the hexagonal AuNPs will affect their sensitivity and optical properties. In this study, the lateral size of the hexagonal AuNPs varied from 10 nm to 60 nm, with a controlled thickness of 30 nm. The absorption graph blue shifts with the peak decreasing until the graph has almost no peak as the lateral size decreases. The lateral size of 40 nm at a thickness of 30 nm was found to have the highest shifting response towards the change of refractive index, indicating good sensitivity for sensors. Simulation of hexagonal AuNPs-based LSPR detection of albumin concentrations of 0.35 –5.71 mM produces a sensitivity of 2.59 nm/mM, equivalent to 245.96 nm/RIU. The competitive performance and ease of detection procedures in real applications show the potential of the hexagonal AuNPs-based LSPR sensor to be further explored in developing alternative albumin detection techniques.

关键词： Nanoparticles Geometry Gold Sensitivity Filtration Absorption Refractive index

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Analysis of Optimizer Effects on CNN Model for Defect Identification in Welding Results of A 106 Grade B Carbon Steel Pipe

Analysis of Optimizer Effects on CNN Model for Defect Identi...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Mochammad Karim Al Amin Mohammad Thoriq Wahyudi Muhammad Ainul Yaqin Ryan Yudha Adhitya Dika Anggara Aygasta Firstya Alliyrahmanda Moh Miftachul Munir Eka Cahya Septian Welding Engineering Study Program Shipbuilding Institute Polytechnic of Surabaya Surabaya Indonesia Automation Engineering Study Program Shipbuilding Institute Polytechnic of Surabaya Surabaya Indonesia PT. Kintomo Engineering Group Surabaya Indonesia

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Potato Leaf Disease Detection by Means of Vision Transformer and CoAtNet Architecture

Potato Leaf Disease Detection by Means of Vision Transformer...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Mesa Anugerah Kenan Sergio Ariel Brandon Stevano Siallagan Endra Joelianto Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

关键词： Productivity Computer vision Accuracy Computational modeling Image processing Machine learning Transformers Informatics Monitoring Diseases

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