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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Cyber-physical infrastructures for advancing pyrolysis conversion process: A case study of biochar production

Cyber-physical infrastructures for advancing pyrolysis conve...

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ASME 2020 International Design Engineering Technical Conferences and computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Hersh, Benjamin Mohajeri, Amir Mirkouei, Amin Xian, Min Department of Mechanical Engineering University of Idaho ID United States Industrial Technology Program University of Idaho ID United States Industrial Technology Program Department of Mechanical Engineering University of Idaho ID United States Department of Computer Science University of Idaho ID United States

ISBN: (纸本)9780791883983

Biomass-based products (bioproducts) have been introduced as a means to address food-energy-water nexus challenges. However, the existing approaches have not been integrated to convert biomass into market-competitive bioproducts (e.g., biochar and bio-oil). Pretreatment and conversion processes represent the most substantial portion of the total bioproduct cost. This study proposes a portable conversion process for high-quality biochar and bio-oil production, using mixed reactors and advanced cyber-physical infrastructures to promote cross-cutting technological and commercialization opportunities. The proposed portable process converts biomass to bioproducts near the collection sites, which can address collection, staging, and logistics challenges. The fundamental novelty of this study lies in utilizing cyber-physical technologies for advancing pre-/postconversion processes and crossing the boundaries to meet the market needs. A case study for biochar production from various biomass feedstocks is used to demonstrate and verify the application of the conversion process and the cyber-based advances. The results indicate that the integrated cyber-physical conversion pathway can increase process yield and quality. It is also found that biomass properties and conversion process configurations play a crucial role in determining the process yield and biochar quality in terms of the physical-chemical structure. Copyright © 2020 ASME.

关键词： Biomass

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Performance Investigation of Tri-Layer Double-Gate Ingazno Thin-Film Transistor in Synchronous Bias for Active-Matrix Lcds

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SSRN 2023年

作者： Mishra, Richa Dargar, Shashi Kant Dargar, Abha Birla, Shilpi Lai, Wen-Cheng Department of Electronics Engineering Kalasalingam Academy of Research and Education TN India Department of Electronics Engineering Manipal University Jaipur India Department of Computer Science Engineering Parul University Vadodara India Bachelor Program in Industrial Projects National Yunlin University of Science and Technology Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Taiwan

In the current display electronics, a high-resolution pixel density and refresh rate ranging 7680 × 4320 pixels, 300 ppi, and 240 Hz are in high demand. The pixel-driving Thin–Film Transistors (TFTs) are primarily responsible for the switch in such displays. The amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) TFTs are popular in realizing better high–speed properties in line to achieve those needs. In previous years, amorphous-IGZO channel TFTs in one-gate and dual-gate structures have exhibited improved gate control. In this paper, the authors present the analytical performance of double–gate tri-layer channel structure design of a-IGZO TFT with three stacked channel layers (IGZO/ITO/ITO1) to model and evaluate the device performance at different gate biasing. The proposed device simulation results were encouraging, in terms of high capacitance in dual gate bias (CDG) 26.2 nF/cm2, high mobility (µFE) 16.93 cm2/V.s, and steeper subthreshold swing (SS) 95 mV/decade. © 2023, The Authors. All rights reserved.

关键词： Thin film transistors

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Estimating the 3D center point of an object with Kinect sensor RGB-D images

Estimating the 3D center point of an object with Kinect sens...

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IEEE Latin American Robotics Symposium, LARS

作者： Gustavo Fardo Armênio João Alberto Fabro Renzo De Rosa Tognella Felipe Pierre Conter Marlon Vaz De Oliveira Everson De Souza Silva Computer Engineering Course - UTFPR Curitiba PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR-Federal University of Technology - Paraná Curitiba PR Brazil Information System Course - UTFPR Curitiba PR Brazil UNILA - University of Latin American Integration Foz do Iguaçu PR Brazil CPGEI-Graduate Program on Electrical Engineering and Industrial Informatics/UTFPR IFPR- Federal Institute of Technology - Paraná Pinhais PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR Curitiba Brazil

This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating System (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which are used to create a Point Cloud representing the geometry of the environment. ROS is used as a robotics development framework, while YOLO is employed to identify objects in the scene. The article presents the packages used, the datasets used for measurement, and the configuration of ROS and YOLO. Additionally, the functionalities of RViz, a 3D visualization tool used in the tests, are explored. Furthermore, it covers the methods employed, the acquired data, and an analysis of the error margin in relation to the measurement of the distance between the Kinect and the object. The findings and techniques presented in this study contribute to addressing the challenges faced in the RoboCup@Home competition, specifically in the context of object manipulation tasks.

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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International Conference on Green technology and Sustainable Development (GTSD)

作者： Hoang-Hiep Le Hao-Yu Lu Md. Aftab Baig Sourav De Ing-Chao Lin Darsen D. Lu Department of Computer Science and Information Engineering Minghsin University of Science and Technology Hsinchu Taiwan R.O.C Intelligent Computing Industrial Doctorate Program National ChengKung University Tainan Taiwan R.O.C Institute of Microelectronics National ChengKung University Tainan Taiwan R.O.C Department of Computer Science and Information Engineering National ChengKung University Tainan Taiwan R.O.C

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf 0.5 Zr 0.5 O 2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations.

关键词： Training Performance evaluation Energy consumption Accuracy Neuromorphic engineering Neural networks Multilayer perceptrons

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Web Scraping Methods on Odoo Framework to Collect Rupiah Exchange Rate From Bank Indonesia Website

Web Scraping Methods on Odoo Framework to Collect Rupiah Exc...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Masmur Tarigan Ford Lumban Gaol Tukoro Matsuo Doctor of Computer Science Graduate Program Faculty of Computer Science Universitas Bina Nusantara Universitas Esa Unggul Jakarta Indonesia Doctor of Computer Science Universitas Bina Nusantara Jakarta Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan Asia University Taiwan

ISBN: (纸本)9781665424219

The foreign currency exchange rate against the rupiah in the Odoo ERP framework system accommodates multi-currencies. However, Odoo has not updated the rupiah exchange rate based on the value from Bank Indonesia regularly. The exchange rate is the price at which one country's currency can be exchanged for another country's currency. The rupiah exchange rate in business has an influence on the cost revenue as well as the profit of the Indonesian company. The problem is updating the rupiah exchange rate must be done manually by looking at the exchange rate on the Bank Indonesia website and entering it into the ERP system according to the current date. The Rupiah exchange rate in foreign currencies needs to be updated regularly by Bank Indonesia exchange rates. To collect rupiah exchange rate uses the Odoo Framework ERP system using the web scraping methods to extract data from Bank Indonesia exchange rates, the library used in scraping is Beautiful Soup. Beautiful Soup is a Python package for parsing HTML and XML. Is used to extract data from HTML, which is useful for web scraping. Rupiah exchange rate updates will be scheduled by the system every day at 12.30 p.m. GMT+07.

关键词： Manufacturing industries Exchange rates Schedules Supply chain management XML Libraries Data mining

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Aldian Nurcahyo Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Compuer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Compuer Science Departement Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices.

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A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

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Wireless Communications and Mobile Computing 2022年第0期2022卷

作者： Srividhya, S.R. Kavitha, C. Lai, Wen-Cheng Mani, Vinodhini Khalaf, Osamah Ibrahim Department Of Computer Science And Engineering Sathyabama Institute Of Science And Technology Chennai India Bachelor Program In Industrial Projects National Yunlin University Of Science And Technology Douliu640301 Taiwan Department Electronic Engineering National Yunlin University Of Science And Technology Douliu640301 Taiwan Al-Nahrain University Al-Nahrain Nanorenewable Energy Research Center Baghdad Iraq

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic;this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using tex

关键词： Object detection

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Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models 9

Digital Forensics Investigation and Incident Response in Int...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Zaki Hamidi, Eki Ahmad Riskiono, Sampurna Dadi Negara, Ridha Muldina Mayasari, Ratna Ramadona, Suci Nurhayati, Ade Sucipto, Adi Jupriyadi Nurkahfi, Galih Nugraha Farahiyah, Dzata Naseer, Muchammad Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung40614 Indonesia School of Electrical Engineering Telkom University Bandung40257 Indonesia Elearical Engineering Study Program Politeknik Caltex Riau Riau28265 Indonesia Institut Teknologi Telkom Jakarta Faculty of Engineering Jakarta11710 Indonesia Bandung40135 Indonesia Universitas Islam Indonesia Yogyakarta Faculty of Industrial Technology Yogyakarta55584 Indonesia Sekolah Tinggi Teknologi Bandung Informatics Department Bandung40235 Indonesia

ISBN: (纸本)9798350305029

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article. © 2023 IEEE.

关键词： Internet of things

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