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Matching Images on the Face of A Buddha Statue Using the Scale Invariant Feature Transform (SIFT) Method 7

Matching Images on the Face of A Buddha Statue Using the Sca...

引用

7th International Conference on Information Technology, Computer, and Electrical engineering, ICITACEE 2020

作者： Marlinda, Linda Rustad, Supriadi Basuki, Ruri Suko Budiman, Fikri Fatchan, Muhamad Universitas Dian Nuswantoro Doctoral Program of Computer Science Semarang Central Java Indonesia Universitas Pelita Bangsa Informatica Engineering Cikarang West Java Indonesia

ISBN: (纸本)9781728172255

The condition of the statue in Indonesia at this time many are missing, especially the face of the statue. 504 Buddha statues in the Borobudur temple, there are 248 statues which are now in a headless condition. The condition of the lost statue face is due to being stolen, still buried in the area around the temple, or has changed hands to another party. To find the statue's head according to the face, a matching image is needed, one of which uses the sift method. Buddha face statue matching can be done based on the characteristics of hair, eyebrow shape, lips, head shape or attributes found on the head of the statue. The image matching method can describe an image with a specific keypoint using Scale Invariant Feature Transform (SIFT), where each keypoint has a gradient orientation (GO) and gradient magnitude (GM) which are processed into features in the image registration process. The purpose of this study is to match the face images of Buddha statues with different angles. This study aims to match the face image of the original statue with the image of the statue chosen from different angles. The results of this study are that the same image with different angles obtains 90% accuracy. © 2020 IEEE.

关键词： Face recognition

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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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Modeling and analysis of three-phase active power filter integrated photovoltaic as a reactive power compensator using the simulink matlab tool 5

Modeling and analysis of three-phase active power filter int...

引用

5th International Conference on Informatics and Computing, ICIC 2020

作者： Setiyono Arief Sudiro, Sunny Prasetyo Wibowo, Eri Department of Electrical Engineering Gunadarma University Jakarta Indonesia Jakarta Indonesia Stmik Jakarta Stik Jakarta Indonesia It Doctoral Program Gunadarma University Jakarta Indonesia

ISBN: (纸本)9781728180472

This paper presents an analysis of the modeling of reactive power compensation using renewable energy from sunlight through photovoltaic to eliminate harmonic signals in the electricity network with a three-phase active power system filter. Electric energy supply comes from three-phase electricity sources in the form of a sinusoid. Non-linear loads such as electronic devices and electrical equipment connected to the grid generate harmonics signals;can make the format of electrical energy distorted. This nonlinear load draws electric current in the form of non-sinusoid. This format increases Total Harmonic Distortion (THD), thereby worsening the performance of electrical equipment. An active power filter is a very effective method in reducing harmonics signals and increasing the quality performance of electricity. This power filter works by injecting compensation current into the grid. The energy that is injected comes from the direct current output voltage (DC) of the photovoltaic link stored in the capacitor via a Voltage Source Inverter (VSI) switch. The compensation current has the same format as the harmonics current but differs in 180°. The main parts of this power filter include the Reference Signal Generating Unit, PI Controller, VSI Switch Signal Ignition Gate Generator, Energy Storage Circuit (Battery), and Photovoltaic. Simulation modeling using Matlab Simulink Tools to reduce THD levels by IEEE 519 standards. Simulation results show a significant decrease in Total Harmonic Distortion (THD), where the grid before compensation contains THD of 29.74% and after injection of 2.25%. So this modeling is worth proposing as an active filter in a power system. © 2020 IEEE.

关键词： Simulink

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Effective Loss Function for Unbalanced Breast Thermal Image Segmentation

Effective Loss Function for Unbalanced Breast Thermal Image ...

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2021 International Conference on Computer System, Information Technology, and Electrical engineering, COSITE 2021

作者： Roslidar, Roslidar Saddami, Khairun Irhamsyah, Muhammad Arnia, Fitri Syukri, Maimun Munadi, Khairul Universitas Syiah Kuala Doctoral Program Post Graduate Program School of Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Medical Faculty Banda Aceh23111 Indonesia

ISBN: (纸本)9781665425094

The convolutional neural network's ability to learn images has reigned in computer vision tasks of object detection, classification, and segmentation. In segmentation, the CNN architectures of U-Net and SegNet have shown a good performance;thus, we implemented these networks to take the region of interest (ROI) of the breast thermal images from database for mastology research (DMR). We fine-tuned the networks to find the optimal hyperparameter that can result in the best learning performance. The networks were trained using the stochastic gradient descent optimization algorithm, and weights were updated using the error backpropagation of the algorithm. To minimize the error, the loss function is applied to evaluate a candidate solution of the weights. Thus, we conducted a preliminary study on finding the optimal loss function for breast thermal image segmentation. We applied loss functions of cross-entropy, dice, and focal and figured out the one that provides the highest segmentation accuracy. Then, to evaluate the segmentation result, we calculated the pixel accuracy rate and the dice coefficient to obtain the overlapping index measuring the overlay area between ground truth and predicted output. The result shows that the SegNet model with cross-entropy loss function can take the ROI of breast thermal images at the highest pixel accuracy and optimum dice coefficient of 0.8845 and 0.7928, respectively. © 2021 IEEE.

关键词： Image segmentation

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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The effect of fiber volume fraction on tensile and impact properties of eleusine indica grass reinforced polypropylene bio composite 1

The effect of fiber volume fraction on tensile and impact pr...

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1st International Conference on Design, Energy, Materials and Manufacture, ICDEMM 2018

作者： Lokantara, I.P. Suardana, N.P.G. Surata, I.W. Winaya, I.N.S. Doctoral Study Program of Engineering Science Faculty of Engineering Udayana University Kampus Sudirman Denpasar-Bali Indonesia Mechanical Engineering Department Udayana University Street of Bukit Jimbaran Bali Badung Indonesia

The purpose of this research was to investigate the tensile strength and impact strength of eleusine indica grass fiber-reinforced polypropylene recycle composite. In this study, the fiber was treated with alkali at 5% NaOH for 2 hours. The eleusine indica grass fiber cut with a length of 3 cm and the matrix used recycle polypropylene. In this research, the volume fraction of eleusine indica grass fiber varied by 20%, 25%, and 30% respectively. The composite molded with a hot press at temperature 200°C with a holding time for 2 hours. Tensile test used ASTM D3039 and impact test used ASTM D256. The highest tensile and impact strength results on fiber volume fraction 30%. © Published under licence by IOP Publishing Ltd.

关键词： Polypropylenes

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A Light Weight Multi-Head SSD Model for ADAS Applications 1

A Light Weight Multi-Head SSD Model for ADAS Applications

引用

1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Lai, Chun-Yu Wu, Bo-Xun Lee, Tsung-Han Shivanna, Vinay Malligere Guo, Jiun-In National Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu Taiwan National Chiao Tung University College Doctoral Program of Electrical and Computer Engineering Hsinchu Taiwan

ISBN: (纸本)9781665404839

Moving objects detection is considered as one of the prime safety indicators in the Advanced Driver Assistance System (ADAS). For implementing on resource-limited embedded platforms and still yield sufficient frame rate and quality, the paper proposes a lightweight multi-head single shot detector (SSD) model that strengthens the moving object detection significantly. The paper also introduces focal loss method to deal with imbalance problem of detecting pedestrians and bikes in training datasets (vehicles, bikes, and pedestrians). Lastly, the proposed lightweight network can be deployed on low-power embedded devices to achieve real-time processing performance (512x256) yielding 30fps. © 2020 IEEE.

关键词： Object detection

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Chien, Shao-Yu Wong, Ching-Chang Tamkang University College of Engineering Doctoral Program in Robotics New Taipei City25137 Taiwan Tamkang University Electrical and Computer Engineering Department New Taipei City25137 Taiwan

ISBN: (纸本)9798350302714

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

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Potential Classification Prediction of Solar and Wind Energy in Indonesia using Machine Learning with Random Forest Algorithm 5

Potential Classification Prediction of Solar and Wind Energy...

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5th IEEE International Conference on Computer and Informatics engineering, IC2IE 2022

作者： Ardiansyah, Deden Mantoro, Teddy Syafei, Wahyul Amien System School of Postgraduate Diponegoro University Doctoral Program of Information Bogor Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta Indonesia Diponegoro University Faculty of Engineering Semarang Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

The realization of the mass use of renewable energy needs to be supported by various parties, including Indonesia, which is a country that has great potential in the use of renewable energy. The community, government, and private parties who become developers in this regard need to realize the main potential of regions in Indonesia. This study aims to determine the prediction results of renewable energy in Indonesia, especially the potential for solar and wind energy. The method used in this research is machine learning with a random forest algorithm. Random forest classification prediction modeling was carried out using R software. The data samples used were data on maximum temperature, minimum temperature, average temperature, average humidity, duration of sunshine, and average wind speed from 34 provinces in Indonesia. Based on the prediction results with random forest, the average accuracy of the random forest classification model ranges from 81-92%. So, it can be said that the model can provide reasonable predictions. Based on random forest predictions, the classification of the largest level of solar energy potential is in 3 main areas, namely the provinces of East N usa Tenggara, Bali, and West Nusa Tenggara. While the areas with the greatest wind energy potential are in 3 provinces, namely Kep. Bangka Belitung, East Nusa Tenggara, and East Java. The province of East Nusa Tenggara has a high potential for solar and wind energy. The potential duration of solar radiation reaches 10.6 hours per day, while the average wind speed reaches 4 m/s per day. The results of this study are expected to be input for parties who play a role in developing renewable energy in Indonesia to support the renewable energy mix target of 23 % by 2025. © 2022 IEEE.

关键词： Forecasting

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Globally convergent newton-SOR method for statistical image reconstruction

Globally convergent newton-SOR method for statistical image ...

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2000 IEEE Nuclear Science Symposium Conference Record

作者： Sawada, Shinji Kudo, Hiroyuki Doctoral Program in Engineering University of Tsukuba Tsukuba Ibaraki Japan Inst. of Info. Sci. and Electronics University of Tsukuba Tsukuba Ibaraki Japan

This paper develops a new fast iterative method to minimize a general convex cost function over the nonnegative orthant for tomographic image reconstruction. The new method is based on the inexact Newton method where the convex cost function is approximated by a quadratic function at each iteration step and the quadratic cost is decreased using the projected successive overtaxation (SOR) method. To assure the global convergence property of the Newton method, we introduce the, trust region and the line search techniques. The resulting method can be applied to arbitrary convex cost function in a unified way and its global convergence is mathematically assured. The method is implemented with simulated and real data for emission and transmission tomography. The results demonstrate that the convergence speed of the proposed method is comparable to that of the ordered subsets method.

关键词： Image reconstruction

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