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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sutikno, Tole Wahono, Tri Karim Fathurrahman, Haris Imam Arsadiando, Watra Hendril Satrian, P. Universitas Ahmad Dahlan Master Program of Electrical Engineering Yogyakarta Indonesia Universitas Ahmad Dahlan Undergraduate Program of Electrical Engineering Yogyakarta Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798350355314

This research discusses the safety challenges faced by bicycle smart parking systems, which are often irregular and unsafe, especially in Indonesia. The purpose of this research is to develop an RFID-based smart parking system for electric bicycles that improves user safety and accessibility. The system is designed using components such as the NodeMCU 8266 microcontroller, RFID module, and solenoid lock, and is tested under real-world conditions. Using the C++ programming language to be embedded in a microcontroller. Quantitative results show that the system achieved an 80% success rate in RFID detection on various materials with optimal performance through acrylic barriers. The system demonstrates better safety compared to traditional bicycle parking solutions, which contributes to increased public confidence in the safety of electric bicycles. © 2024 IEEE.

关键词： C++ (programming language)

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Internet of Things-Based Early Forest Fire Detection System with *** Platform 11

Internet of Things-Based Early Forest Fire Detection System ...

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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sutikno, Tole Akbar, Son Ali Bayusuta, Tosan Aji Sabila, Liya Yusrina Hendril Satrian, P. Universitas Ahmad Dahlan Master Program of Electrical Engineering Yogyakarta Indonesia Universitas Ahmad Dahlan Undergraduate Program of Electrical Engineering Yogyakarta Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798350355314

Forest and land fires in Indonesia are a major concern for the government when entering the dry season. The researchers have widely developed early detection technology, including satellite and airborne-based technology, but it faces several limitations due to the influence of weather and other unavoidable factors. Furthermore, fire detection technology based on wireless sensor networks (WSNs) and the Internet of Things (loT) is one of the most effective solutions. This study developed a sensor node that includes an Arduino UNO, a smoke sensor, a fire sensor, and temperature and humidity sensors and code program using C++, to detect potential forest fires. We place these nodes at fire-prone points in the forest. Additionally, the gateway node receive data from the sensor node, process it using the NodeMCU ESP8266, and send it to the *** database for real-time report. The developed system's test results demonstrate that it can display and send data in real-time to the *** platform via an internet connection. Additionally, the base station's physical 12C 20x4 LCD screen displays the information. The forest fire early warning system functions well, and the test results show good detection and data transmission capabilities. © 2024 IEEE.

关键词： C++ (programming language)

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EESRGAN: Efficient & Effective Super-Resolution Generative Adversarial Network

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IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) 2024年第2期41卷 200-211页

作者： Tsai, An-Chao Tsou, Cheng-Han Wang, Jhing-Fa International Master Program of Information Technology and Application National Pingtung University Pingtung900 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In Taiwan, traditional production equipment for the mainframe panels is imported from overseas, and the parameters are adjusted through the operation panel for automated manufacturing. However, these parameters are slightly different from those used in Taiwan, requiring manual recording of warning parameters to ensure quality control. Currently, the only way to capture the operation records of these machines is by manually recording the system panel information, which is a time-consuming and laborious process that is expensive for production line personnel. In this work, we use image recognition to capture and analyze data externally without damaging the machine. This research improves the ESRGAN network to restore the image details and textures of the mainframe panels. It also captures relevant data with Google OCR to convert panel images into parameter values. The captured results are combined with data analysis to provide more accurate standard mainframe panel information. Even under different interference source conditions, data extraction and analysis can still be carried out to output the text files, effectively assisting the production line in recording the machine panel parameters and reducing personnel’s workload. © 2024 IETE.

关键词： Extraction

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems 4

Early Detection of Heart Rate Conditions in Autistic Childre...

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4th International Conference on electrical engineering and Computer Science, ICECOS 2024

作者： Albar, Nizam Melinda, Melinda Away, Yuwaldi Universitas Syiah Kuala Dept. Electrical Engineering Master of Electrical Engineering Study Program Banda Aceh Indonesia Universitas Syiah Kuala Engineering Faculty Dept. Electrical Engineering and Computer Banda Aceh Indonesia

ISBN: (纸本)9798350368253

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums. © 2024 IEEE.

关键词： Butterworth filters

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Robot and Human Interaction using Natural Language Understanding in Indonesian based on Mental Image Based Understanding 11

Robot and Human Interaction using Natural Language Understan...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Abrari, Muhammad Giriarda Hindersah, Hilwadi Rusmin, Pranoto Hidaya Bandung Institute of Technology Master Program in Electrical Engineering Bandung Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350321982

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans. © 2023 IEEE.

关键词： Semantics

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Security and Privacy Relate Future Aspects of Artificial Intelligence-Powered Electric Vehicle Technology 11

Security and Privacy Relate Future Aspects of Artificial Int...

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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sutikno, Tole Pamungkas, Anggit Santosa, Budi Puriyanto, Riky Dwi Cahya Baswara, Ahmad Raditya Universitas Ahmad Dahlan Master Program of Electrical Engineering Yogyakarta Indonesia Yogyakarta Indonesia Universitas Ahmad Dahlan Automotive Technology of Vocational Education Yogyakarta Indonesia Universitas Ahmad Dahlan Undergraduate Program of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350355314

Artificial Intelligence (AI) and electric vehicles EVs are revolutionizing the future of transportation, automation, and connectivity. The integration of AI in EV technology heralds a transformative era in transportation, characterized by enhanced efficiency, autonomy, and user experience. However, as AI-powered EVs become increasingly prevalent, critical security and privacy challenges emerge that necessitate comprehensive scrutiny. This paper explores the future aspects of security and privacy in AI-powered EVs, highlighting potential vulnerabilities and the implications of data exploitation. Key areas of focus include the cybersecurity threats posed by interconnected vehicle networks, the safeguarding of personal data from unauthorized access, and the development of robust encryption methods to protect vehicular communication systems. Additionally, the paper examines regulatory frameworks and industry standards essential for ensuring the secure deployment of AI in EVs. By addressing these concerns, the research aims to provide a holistic understanding of the measures required to fortify the security and privacy of AI-driven electric vehicles, ultimately contributing to developing a safe and trustworthy transportation ecosystem. © 2024 IEEE.

关键词： Differential privacy

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Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naïve Bayes Classifier

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Journal of Computing Science and engineering 2022年第3期16卷 153-164页

作者： Yudhana, Anton Umar, Rusydi Saputra, Sabarudin Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Master Program of Informatics Universitas Ahmad Dahlan Yogyakarta Indonesia

Fish is one of the food sources that should be examined for freshness before being consumed. The consumption of rotten fish can cause various diseases. The rotten fish have changed color on the gills, skin, flesh, and eyes and have a pungent odour. Fish freshness can be assessed using a variety of conventional methods, but these methods have limitations, such as requiring relatively expensive equipment, trained personnel and being destructive. The machine learning method is used because it is non-destructive, reduces costs, and is easy to use. This study aims to identify the freshness of fish using k-nearest neighbor (k-NN) and Naïve Bayes (NB) classification methods based on the fish-eye image. The features used in the classification process are RGB and GLCM. The research stages consist of the fish collection process, image acquisition and class division, preprocessing and ROI detection, feature extraction and dataset split, and the classification process. Based on these results, it can be stated that the k-NN method has better performance than NB with average accuracy, precision, recall, specificity, and AUC of 0.97, 0.97, 0.97, 0.97, and 0.97. © 2022. The Korean Institute of Information Scientists and Engineers

关键词： Fish

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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The Evolution of Non-invasive Hemoglobin Measurement with IoT and AI-Based Optical Sensors 11

The Evolution of Non-invasive Hemoglobin Measurement with Io...

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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Sutikno, Tole Handayani, Lina Ruliyandari, Rochana Wijaya, Oktomi Hendril Satrian, P. Arsadiando, Watra Universitas Ahmad Dahlan Master Program of Electrical Engineering Yogyakarta Indonesia Universitas Ahmad Dahlan Faculty of Public Health Yogyakarta Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798350355314

Hemoglobin measurement has significantly evolved with the integration of IoT and artificial intelligence (AI) technologies. This paper presents the evolution of non-invasive hemoglobin measurement using IoT and AI-based optical sensors. IoT and AI-based optical sensors have revolutionized the field of hemoglobin quantification, allowing us to measure this important biomarker in a way that isn't invasive or difficult to handle. The use of optical sensing technology, along with IoT frameworks and AI methods, has improved the accuracy and usefulness of hemoglobin tests, showing a promising way to improve healthcare outcomes. However, challenges remain in the field of non-invasive hemoglobin measurement, such as precision constraints, data security, and privacy safeguards. Despite these challenges, the future landscape of non-invasive hemoglobin measurement leveraging IoT and AI-powered optical sensors appears poised to transform healthcare delivery significantly. Together, IoT and AI-based optical sensors have the potential to revolutionize healthcare monitoring and diagnosis. © 2024 IEEE.

关键词： Diagnosis

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