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"Indonesia kaya akan warisan budaya, salah satunya adalah aksara Pegon. Aksara ini merupakan sistem penulisan yang berkembang selama penyebaran Islam di nusantara. Warisan budaya ini adalah adaptasi dari aksara Arab yang sering digunakan oleh para ulama Islam dalam penulisan manuskrip di masa lalu. Namun, seiring dominasi aksara Latin di Indonesia, penggunaan aksara Pegon saat ini semakin berkurang, hanya pada kalangan tertentu, sehingga menyebabkan sedikitnya individu yang mampu membaca aksara Pegon. Oleh karena itu, transliterasi antara aksara Pegon ke Latin diperlukan. Penelitian ini berfokus pada pengembangan transliterasi menggunakan pendekatan berbasis data dengan model sequence-to-sequence, mengikuti pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil penelitian menunjukkan bahwa pendekatan berbasis data menggunakan metode sequence-to-sequence mencapai akurasi 99.76% dan CER 0.010624 untuk dataset bahasa Sunda dengan model terbaik BiGRU-Att, akurasi 99.31% dan CER 0.029255 untuk dataset bahasa Jawa dengan model terbaik BiGRU-Att, dan akurasi 99.58% dan CER 0.020466 untuk dataset gabungan bahasa dengan model BiLSTM-Att. Dari hasil ini, dapat dikatakan bahwa hasil prediksi tergolong baik dengan nilai akurasi di atas 70%, nilai loss mendekati 0, dan nilai Character Error Rate (CER) mendekati 0 untuk semua dataset.

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Indonesia, rich in cultural heritage, includes Pegon script, a writing system that evolved during the spread of Islam in the archipelago. This cultural heritage is an adaptation of the Arabic script often used by Islamic scholars in manuscript writing in the past. However, the current use of Pegon script is less popular compared to the past due to the dominance of the Latin script in Indonesia, resulting in few individuals being able to read Pegon script. Therefore, transliteration between Pegon and Latin scripts is necessary. The research concentrates on developing transliteration using a data-driven approach with sequence-to-sequence models, following the Arabic-Latin transliteration guidelines from the Ministry of Religious Affairs in 1987, the results of the Pegon Script Congress 2022, and SNI 9047:2023. The results show that the data-driven approach using the sequence- to-sequence method achieves an accuracy of 99.76% and a CER of 0.010624 for the Sundanese dataset with the best model BiGRU-Att, an accuracy of 99.31% and a CER of 0.029255 for the Javanese dataset with the best model BiGRU-Att, and an accuracy of 99.58% and a CER of 0.020466 for the combined language dataset with the BiLSTM-Att model. From these results, it can be said that the prediction results are classified as good with accuracy values above 70%, loss values close to 0, and Character Error Rate (CER) values close to 0 for all datasets."

"This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. It features a large number of practical designs performed for different classes of processes: thermal, fluid processing and electromechanical. "

"This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security. Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing. This SpringerBrief provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks. Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief very useful as a short reference or study guide book. Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well."

"This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management."

"Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity."

"Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data."

"Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art."

"Materials informatics: a ?hot topic? area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis.The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche", and the resulting complex, multi-factor analyses required to understand it, means that interest, investment, and research are revisiting informatics approaches as a solution.This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science."

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Penjaminan kualitas, keandalan dan keselamatan di sistem industri yang semakin kompleks menjadi tantangan di era modern. Strategi pemeliharaan yang tepat menjadi kunci. Pemeliharaan berbasis kondisi, yang didukung dengan kegiatan pemonitoran kondisi, menjadi pilihan yang tepat untuk menghadapi tantangan tersebut. Informasi dari hasil pemonitoran tersebut menjadi dasar dalam mengambil keputusan pemeliharaan. Tahap awal yang penting dalam ekstraksi informasi mengenai kondisi proses maupun peralatan adalah dengan membangun  sistem deteksi dan diagnosis fault yang cepat dan akurat.

Pembangkit listrik termal, sebagai representasi sistem industri yang kompleks,  merupakan penyedia listrik utama dalam kehidupan modern. Untuk menjamin keandalannya, pengembangan sistem cerdas  sistem berbasis data-driven untuk deteksi dan diagnosis fault yang cepat dan akura, menjadi sebuah kebutuhan.  Keunggulan dari pendekatan ini adalah tidak diperlukannya pengetahuan yang komprehensif mengenai sebuah sistem, sehingga sangat sesuai untuk diaplikasikan pada industri yang kompleks, seperti pada pembangkit listrik. Algoritma berbasis pembelajaran mesin menjadi pilihan tepat pada era di mana data pemonitoran kondisi dihasilkan terus menerus. Penelitian ini bertujuan untuk mendapatkan rancangan sistem cerdas berbasis data-driven, untuk deteksi dan diagnosis fault pada sistem utama di pembangkit listrik termal. Penelitian ini mengusulkan aplikasi metode berbasis Neural Networks (NN) yang dikenal mampu menangani permasalahan kompleks yang non linier dan menghasilkan model dengan akurasi yang tinggi.  Extreme learning machine-radial basis function (ELM-RBF), yang merupakan metode berbasis NN yang dikenal memiliki waktu pembelajaran yang sangat cepat, dibandingkan dengan metode klasik NN, yaitu Backpropagation (BPNN), serta Support Vector Machine (SVM), yang selalu menghasilkan solusi yang global.

Struktur penyelesaian masalah dalam penelitian ini terdiri atas perancangan sistem cerdas untuk deteksi dan diagnosis fault pada  dua sistem utama, yaitu turbin uap dan transformator yang berperan vital.  Data untuk eksperimen berasal dari data riil industri PLTU Muara Karang, ditambah dengan data fault dan normal yang sudah dipublikasikan ke dalam basis data IEC-TC10. BPNN paling unggul dalam akurasi, namun waktu komputasi yang sangat lama. SVM menunjukkan akurasi yang lebih baik dibandingkan ELM-RBF, namun kalah dalam waktu komputasi. ELM-RBF unggul dalam waktu komputasi tanpa perbedaan yang signifikan pada akurasi. Dengan semakin lengkapnya basis data training, ELM-RBF berpotensi mendeteksi dan mendiagnosis dengan akurasi tinggi dan waktu komputasi yang sangat cepat, sesuai kebutuhan pembangkit listrik termal.

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Quality, reliability and safety assurance in the increasingly complex industrial systems is a challenge in the modern era. The right maintenance strategy is vital. Condition based maintenance, supported by condition monitoring activities, is the right choice to face these challenges. Information from the monitoring results becomes the basis for determining maintenance decisions. An important initial step in the extraction of information, regarding process and equipment conditions, is to establish a fast and accurate fault detection and diagnosis system.

Thermal power plants, as a representation of complex industrial systems, are the primary electricity providers in modern life. To ensure its reliability, the development of intelligent systems based on data-driven systems for rapid and accurate detection and diagnosis of faults is a necessity. The advantage of this approach is that it does not need comprehensive knowledge about a system, so it is very suitable to be applied to complex industries, such as power plants. Machine learning-based algorithms are the most suitable choice in an era where condition monitoring data is generated continuously. This study aims to obtain a data-driven intelligent system design for fault detection and diagnosis in the main system of thermal power plants. This research proposes the application of Neural Networks (NN) based methods, which are known to be able to handle complex non-linear problems and produce models with high accuracy. Extreme learning machine-radial basis function (ELM-RBF), which is an NN-based method that is known to have an extremely fast learning time, compared to the classical NN method, namely Backpropagation (BPNN), and Support Vector Machine (SVM), which always produces a global solution.

The structure of problem-solving includes designing intelligent systems for fault detection and diagnosis in the two main systems, namely steam turbines, and transformers, which play a vital role. Experiment data is acquired from a power plant real data and added with a published IEC-TC10 database. BPNN is superior for accuracy, but it is accomplished with the longest computation time. SVM shows better accuracy than ELM-RBF, but the computation time is slower than ELM-RBF. ELM-RBF excels in computation time without a significant accuracy difference. With the more comprehensive training data, ELM-RBF has the potential to detect and diagnose faults with high accuracy and high-speed computation time, according to the requirement of thermal power plants.

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