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International Spring Seminar on Electronics technology

作者： Barbara Molnár Attila Géczy Dept. of Electronics Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Informatics Budapest Hungary

ISBN: (数字)9798350385472

ISBN: (纸本)9798350385489

In this paper, we present an electronic nose application case study based on an affordable Bosch BME688 AI (artificial intelligence) capable sensor module set (and development kit) to distinguish and identify different soft drinks and beverages. During the work, the device was prepared for TinyML and AI-based identification. The module contains eight pieces of BME688 sensors, and it is compatible with the Bosch BME AI Studio software. Based on their unique odor profiles, we created and tested various ML (machine learning) algorithms to distinguish between fruit palinka (fruit spirit) types, different beers, and ethanol. The case study aims to determine whether the module can distinguish between beverages. For this, we applied passive delivery. It was found that the differentiation between air and different samples (cola, lager beer) is possible. Still, ethanol and spirits with high alcohol content saturate the device, pointing to future dynamic sampling (active delivery).

关键词： Seminars Sequential analysis Ethanol Tiny machine learning Position measurement Electronic noses Software

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Design and Evaluation of AKP Application 14

Design and Evaluation of AKP Application

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14th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2022

作者： Simbolon, Iustisia Natalia Silitonga, Devi Wahyuni Bakara, Putri Era Waty Del Institute of Technology Faculty of Informatics and Electrical Engineering Laguboti Indonesia

ISBN: (数字)9781665489362

ISBN: (纸本)9781665489362

The Akuntabilitas Kinerja Pemerintah (AKP) Application is a website-based application that functions to collect, manage, measure, report, and store all reports of work in Kemenko Marves. The purpose of developing the AKP application is to centralize the mechanism for reporting work results with the same reporting standards. The AKP application interface to be built uses the User Centered Design (UCD) method. The AKP application interface will then carry out testing to find usability problems in the initial AKP application prototype. The AKP application testing in this study was carried out in two iterations. The first iteration test was carried out using the Cognitive Walkthrough and Heuristic Evaluation methods from the expert's point of view and the Think Aloud method from the end user side. While the second iteration test, only uses the Think Aloud method. After conducting usability testing, a final high-fidelity prototype of the AKP application will be built. Based on the results of the final prototype, usability testing was carried out using the SUS questionnaire to re-measure the usability level of the AKP application prototype. The target score for the usability value that is expected from this research is 80 (Grade Scale B). Before making improvements to the initial design of the AKP application, the SUS score was 79.5 (Grade Scale = C). However, after improvements were made to the AKP application design, the SUS score became 84 (Grade Scale = B). Based on the test results, the usability score increased by 4.5 and has reached the research target. © 2022 IEEE.

关键词： User interfaces

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Design of a Tourism Recommendation System Based on Sentiment Analysis with Lexicon LSTM

Design of a Tourism Recommendation System Based on Sentiment...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Lubihana, Elan Bandung, Y. Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

The growing tourism sector encourages the development of a system that can assist tourists in finding the best tourist attractions. The system that is being popularly developed in tourism is the recommendation system. It provides recommendations for tourist attractions based on several parameters. One of the parameters that can help in developing a recommender system is tourist satisfaction. Tourist satisfaction can be known by sentiment analysis on reviews on social media or tourism sites. One way of sentiment analysis is using machine learning. However, machine learning requires quite a lot of data labeled for learning. This process takes a lot of time, especially for a large amount of data, so the idea arose to use a lexicon dictionary. In this study, we tested two Indonesian datasets with four classifiers SVM, Random Forest, KNN, and LSTM. Its classifiers will combine with lexicon corpus, BOW (Bag of Words), and TFIDF (Term Frequency Inverse Document Frequency) to determine the best combination method. The experiments found that the scenario lexicon corpus, The LSTM achieves the best performance for all datasets with each accuracy is 90.93% and 76.98%. Meanwhile, in the scenario of lexicon corpus-BOW and lexicon corpus-TFIDF, the SVM method is the best on all datasets. In the Online Lecture dataset, it gets S7.40% and S7.36% of accuracy. On IDSA Dataset, lexicon corpus-BOW, and lexicon corpus-TFIDF, it gets 73.71% and 72.97% of accuracy. © 2022 IEEE.

关键词： Recommender systems

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Effects of Ground Trimming and Feedline Extension in Designing A Microstrip Patch Antenna for RF Applications 8

Effects of Ground Trimming and Feedline Extension in Designi...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Hasanuddin, Muhammad Ogin Dinata, Christian Izzuddin, Ahmad Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Radio-frequency energy harvesting (RFEH) has attracted significant interest as one of the methods of enabling a battery-free sustainable wireless IoT system. Rectenna comes in handy in scavenging an amount of energy at a specific range of frequency, radiation-to-ac harvesting. In that sense, however, not a single antenna managed to perform perfectly for various distinct cases and even in its own one considering its limitations and dynamic parameters. This study proposed a microstrip patch antenna that is designed particularly to operate at 915 MHz frequency utilizing the ground trimming and feedline extension technique to cover the changes of the printed one's parameter. The analysis of the 79.1 mm times 69 mm microstrip patch antenna showed a return loss of -16.664 dB and a VSWR of 1.3437 at a frequency of 915 MHz. The implemented antenna has a wide operating frequency from 742.21 MHz to 1549.2 MHz although it has a higher input impedance of 56.3 ohms. In conclusion, this antenna can work with various RFEH sources at a wider frequency band. © 2022 IEEE.

关键词： Rectennas

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Robust Metric Localization in Autonomous Driving via Doppler Compensation With Single-Chip Radar

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 491-502页

作者： Gao, Pengen Zhang, Shengkai Wang, Wei Lu, Chris Xiaoxuan Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Wuhan University of Technology School of Information Engineering Wuhan430074 China The University of Edinburgh School of Informatics EdinburghEH8 9AB United Kingdom

Metric localization is vital to autonomous driving where it corrects cumulative errors in a long-term run. Such errors are inevitable in real scenarios where GPS signals or some other drift-free exteroceptive measurements are not available, e.g., when an automobile goes through a tunnel. Using FMCW-based mmWave radars is an attractive metric localization technique with improved robustness as RF signals can traverse small particles in harsh weather conditions like snowing, foggy, and storming, but it faces a fundamental challenge of Doppler distortion. Existing works take spatial constraints to mitigate the Doppler distortion of point clouds from mechanical radars with limited accuracy. Modern single-chip mmWave radars that provide dynamic estimates, i.e., radial velocities, bring new opportunities to develop more accurate approaches. This paper presents DC-Loc++, a robust metric localization framework by compensating Doppler distortions using a single-chip mmWave radar. It consists of an explicit velocity-assisted Doppler compensation module for each radar sub-map, an uncertainty-aware metric registration algorithm, and a failure recovery method that validates measurement constraints to generate a more confident pose graph for optimizing vehicle poses. Extensive experiments on both nuScenes dataset and a synthetic CARLA dataset show the effectiveness of DC-Loc++, achieving 99.2% success rate and more than 20.0%, 30.2% error reductions in terms of translation and rotation estimates, respectively, compared with existing approaches. © 2000-2011 IEEE.

关键词： Radar measurement

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High-Efficiency Multiphase Modified DC-DC Cuk Converter 5

High-Efficiency Multiphase Modified DC-DC Cuk Converter

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5th International Conference on Power engineering and Renewable Energy, ICPERE 2022

作者： Assidiq, A.R. Furqani, J. Rizqiawan, A. School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781665455480

The problem in DC-DC power converters penetrating new and renewable energy is the ripple of high current, non-continuous current, and low efficiency. This paper proposes a multiphase modification DC-DC Cuk converter to obtain a continuous current on the input and output sides with predetermined component values and duty cycles. This power converter proposal aims to get a DC-DC power converter with a continuous input and outputs current, small current ripples, and high efficiency by looking at a fraction of power losses in the converter. The current ripple in this proposed converter is close to zero using a duty cycle value of 0.5, so there is ripple cancellation in the Lm inductor. The largest loss fraction lies in the switch, so efficiency improvements are obtained by replacing the IGBT switch with a MOSFET looking at better specifications. So that an efficiency increase was obtained in the proposed converter by 90.81 % in the B circuit and 90.07% in the C circuit. Verification has been performed from the experiment. © 2022 IEEE.

关键词： Cuk converter

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Dynamic Security Level Assessment of Special Protection System (SPS) Using Fuzzy Techniques 23

Dynamic Security Level Assessment of Special Protection Syst...

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23rd International Seminar on Intelligent technology and Its Applications, ISITIA 2022

作者： Sari, Indah Permata Nahor, Kevin Marojahan Banjar Hariyanto, Nanang Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

This study will be focused on efforts to increase the reliability of the Bangka Electricity System by designing the interconnection of the Bangka system with another system that is stronger and has a better energy mix, the Sumatra System. The novelty element in this research is the design of system protection using Special Protection System (SPS) as well as a different assessment method using the Fuzzy Technique This research will analyze the implementation of the SPS event-based and parameter-based as a new defense scheme by taking corrective actions to keep the system stable and reliable. These actions include tripping generators, loads, and reconfiguring the system automatically and quickly. The performance of this SPS will be tested on 10 contingency events with four different load profiles and the system response will be observed in terms of frequency stability, voltage, and rotor angle. From the research results, it can be concluded that the SPS performance on the Bangka-Sumatra Interconnection System has a better and more effective performance than the existing defense scheme, as evidenced by the results of dynamic security assessment (DSA) testing using Fuzzy Techniques. © 2022 IEEE.

关键词： Network security

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Color image restoration by filtering methods: a review

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Soft Computing 2024年第13-14期28卷 7755-7782页

作者： Salamat, Nadeem Missen, Malik Muhammad Saad Akhtar, Nadeem Mustahsan, Muhammad Surya Prasath, V.B. Department of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Computer Science Pediatrics and Biomedical Informatics University of Cincinnati CincinnatiOH45221 United States

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. © The Author(s) 2024.

关键词： Restoration

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ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2022年第3期66卷 236-247页

作者： Szántó, Mátyás Bogár, György Richárd Vajta, László Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 2 Magyar tudosok Blvd. BudapestH-1117 Hungary

In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based Deep Learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and lowlatency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control. © 2022 Budapest University of technology and Economics. All rights reserved.

关键词： Autonomous vehicles

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Classification of Coffee Beans Defect Using Mask Region-based Convolutional Neural Network 7

Classification of Coffee Beans Defect Using Mask Region-base...

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7th International Conference on Information technology Systems and Innovation, ICITSI 2022

作者： Heryanto, Taufiq Alif Nugraha, I Gusti Bagus Baskara School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

The recognition and calculation of coffee bean defects has become one of the references in determining the quality of coffee beans. Indonesia already has a standard set forth in Indonesian National Standard (SNI) to determine the quality of coffee beans based on the special quality requirements of beans by calculating the value of defects. In the standard there are several types of seed defects defined such as black seeds, hollow seeds, cracked seeds and others. The system for recognizing and calculating the value of coffee bean defects is still done conventionally so that it requires a very high ability and skill from the examiner. The purpose of this study is to design a model that is able to recognize coffee bean defects using one of the models in the Deep Learning approach, namely Convolutional Neural Network (CNN). This study will use an improvised workflow from CNN, namely Mask R-CNN (Region-based Convolutional Neural Network) which utilizes the workflow of Faster R-CNN with the addition of a masking feature on the detected object. This study uses a dataset of 480 images with black, broken and hole classes with 360 images for training and 120 images for validation. The test is carried out with 2 forms of images, namely images with individual objects with 30 images and images with plural objects with 20 images. The accuracy obtained is 93.3% for testing individual objects and 75% for testing plural objects. © 2022 IEEE.

关键词： Convolutional neural networks

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