Hasil Pencarian  ::  Simpan CSV :: Kembali

"Layanan video real-time di jaringan unguaranteed yang sering mengalami delay, jitter, dan kehilangan paket. Penelitian ini melakukan simulasi protokol SRT yang dilengkapi mekanisme error handling gabungan antara selective repeat ARQ dan RaptorQ FEC, untuk mengevaluasi performa pada skenario random loss dengan loss rate bervariasi dan burst loss dengan variasi probabilitas transisi Good ke Bad dan Bad ke Good. Metode simulasi dilakukan di MATLAB menggunakan model Bernoulli untuk random loss dan model Markov dua-state Gilbert-Elliot untuk burst loss. Efektivitas dinilai dari metrik kapabilitas pemulihan paket (effective recovery rate dan residual loss rate), kinerja streaming (latency, jitter, dan overhead bandwidth), serta effective frame rate. Hasil simulasi menunjukkan bahwa strategi gabungan berhasil mempertahankan nilai recovery di atas 95 persen dan residual loss di bawah 1 persen hingga loss rate 20 persen. Latency rata-rata berkisar antara 27 ms hingga 76 ms, jitter antara 4 ms hingga 21 ms, dan overhead bandwidth di bawah 20 persen pada kondisi loss moderat. Effective frame rate stabil di sekitar 30 FPS, mengungguli metode ARQ murni dan RaptorQ tunggal. Temuan ini menegaskan bahwa integrasi ARQ selektif dan RaptorQ FEC pada protokol SRT meningkatkan ketahanan terhadap degradasi jaringan dan menjaga kualitas streaming video real-time.

---

Real-time video services over unguaranteed networks often experience delay, jitter, and packet loss. This study simulates the SRT protocol equipped with a combined error handling mechanism of selective repeat ARQ and RaptorQ FEC to evaluate performance under random loss scenarios with varying loss rates and burst loss scenarios with variations in Good-to-Bad and Bad-to-Good transition probabilities. The simulation method is implemented in MATLAB using a Bernoulli model for random loss and a two-state Gilbert–Elliot Markov model for burst loss. Effectiveness is assessed by packet recovery capability metrics (effective recovery rate and residual loss rate), streaming performance (latency, jitter, and overhead bandwidth), and effective frame rate. Simulation results show that the combined strategy maintains recovery values above 95 percent and residual loss below 1 percent up to a 20 percent loss rate. Average latency ranges from 27 ms to 76 ms, jitter from 4 ms to 21 ms, and overhead bandwidth remains below 20 percent under moderate loss conditions. Effective frame rate remains stable at around 30 FPS, outperforming pure ARQ and standalone RaptorQ methods. These findings confirm that integrating selective repeat ARQ and RaptorQ FEC into the SRT protocol enhances resilience to network degradation and preserves real-time video streaming quality.

"

"Monitoring proses hydraulic fracturing merupakan langkah penting untuk memastikan keberhasilan dan efisiensi stimulasi reservoar. Dalam pelaksanaannya, monitoring dilakukan menggunakan hasil rekaman seismik yang mendeteksi rekahan yang terjadi di ujung bawah wellbore. Sistem ini dibangun untuk mengidentifikasi event mikroseismik akibat rekahan, berdasarkan hasil sinyal seismik yang direkam selama proses berlangsung. Penelitian ini bertujuan mengembangkan sistem deteksi event mikroseismik secara otomatis menggunakan pendekatan deep learning. Konsep transfer learning dimanfaatkan karena telah banyak diterapkan dalam konteks seismologi, seperti pada model pre-trained PhaseNet, U-GPD, dan EQTransformer untuk keperluan klasifikasi event maupun phase picking gelombang P dan S. Penelitian ini bertujuan untuk mengembangkan pre-trained model, khusus untuk data mikroseismik dari wilayah Formasi Talang Akar. Arsitektur CNN yang digunakan, dilatih menggunakan data hasil pengukuran seismometer dari Lapangan X dan menghasilkan akurasi sebesar 75,63%. Lapisan fitur dari pre-trained model ini kemudian dibekukan (frozen) untuk mengekstraksi fitur dari data target, yaitu data selama proses Step Rate Test (SRT) di lokasi yang sama. Fitur tersebut selanjutnya diklasifikasikan menggunakan algoritma machine learning konvensional, dengan pendekatan ini dihasilkan akurasi terbaik sebesar 76,92% dalam mendeteksi event mikroseismik akibat proses SRT.

---

Monitoring the hydraulic fracturing process is a crucial step to ensure the success and efficiency of reservoir stimulation. In practice, monitoring is carried out using seismic recordings that detect fractures occurring at the bottom tip of the wellbore. This system is designed to identify microseismic events caused by fracturing, based on the seismic signals recorded throughout the process. This study aims to develop an automatic microseismic event detection system using a deep learning approach. Transfer learning is utilized, as it has been widely applied in seismology, for example in pre-trained models such as PhaseNet, U-GPD, and EQTransformer for event classification and P- and S-phase picking. This research focuses on developing a pre-trained model specifically for microseismic data from the Talang Akar Formation. The proposed CNN architecture was trained using seismic data recorded at Field X and achieved an accuracy of 75.63%. The feature layers of this pre-trained model were then frozen to extract features from the target data, which were obtained during the Step Rate Test (SRT) process at the same location. These features were subsequently classified using conventional machine learning algorithms, resulting in the best accuracy of 76.92% in detecting microseismic events induced by the SRT process. "

"Monitoring proses hydraulic fracturing merupakan langkah penting untuk memastikan keberhasilan dan efisiensi stimulasi reservoar. Dalam pelaksanaannya, monitoring dilakukan menggunakan hasil rekaman seismik yang mendeteksi rekahan yang terjadi di ujung bawah wellbore. Sistem ini dibangun untuk mengidentifikasi event mikroseismik akibat rekahan, berdasarkan hasil sinyal seismik yang direkam selama proses berlangsung. Penelitian ini bertujuan mengembangkan sistem deteksi event mikroseismik secara otomatis menggunakan pendekatan deep learning. Konsep transfer learning dimanfaatkan karena telah banyak diterapkan dalam konteks seismologi, seperti pada model pre-trained PhaseNet, U-GPD, dan EQTransformer untuk keperluan klasifikasi event maupun phase picking gelombang P dan S. Penelitian ini bertujuan untuk mengembangkan pre-trained model, khusus untuk data mikroseismik dari wilayah Formasi Talang Akar. Arsitektur CNN yang digunakan, dilatih menggunakan data hasil pengukuran seismometer dari Lapangan X dan menghasilkan akurasi sebesar 75,63%. Lapisan fitur dari pre-trained model ini kemudian dibekukan (frozen) untuk mengekstraksi fitur dari data target, yaitu data selama proses Step Rate Test (SRT) di lokasi yang sama. Fitur tersebut selanjutnya diklasifikasikan menggunakan algoritma machine learning konvensional, dengan pendekatan ini dihasilkan akurasi terbaik sebesar 76,92% dalam mendeteksi event mikroseismik akibat proses SRT.

---

Monitoring the hydraulic fracturing process is a crucial step to ensure the success and efficiency of reservoir stimulation. In practice, monitoring is carried out using seismic recordings that detect fractures occurring at the bottom tip of the wellbore. This system is designed to identify microseismic events caused by fracturing, based on the seismic signals recorded throughout the process. This study aims to develop an automatic microseismic event detection system using a deep learning approach. Transfer learning is utilized, as it has been widely applied in seismology, for example in pre-trained models such as PhaseNet, U-GPD, and EQTransformer for event classification and P- and S-phase picking. This research focuses on developing a pre-trained model specifically for microseismic data from the Talang Akar Formation. The proposed CNN architecture was trained using seismic data recorded at Field X and achieved an accuracy of 75.63%. The feature layers of this pre-trained model were then frozen to extract features from the target data, which were obtained during the Step Rate Test (SRT) process at the same location. These features were subsequently classified using conventional machine learning algorithms, resulting in the best accuracy of 76.92% in detecting microseismic events induced by the SRT process. "

"This is the first book to focus on the problem of ensuring the correctness of floating-point hardware designs through mathematical methods. Formal Verification of Floating-Point Hardware Design advances a verification methodology based on a unified theory of register-transfer logic and floating-point arithmetic that has been developed and applied to the formal verification of commercial floating-point units over the course of more than two decades, during which the author was employed by several major microprocessor design companies. The book consists of five parts, the first two of which present a rigorous exposition of the general theory based on the first principles of arithmetic. Part I covers bit vectors and the bit manipulation primitives, integer and fixed-point encodings, and bit-wise logical operations. Part II addresses the properties of floating-point numbers, the formats in which they are encoded as bit vectors, and the various modes of floating-point rounding. In Part III, the theory is extended to the analysis of several algorithms and optimization techniques that are commonly used in commercial implementations of elementary arithmetic operations. As a basis for the formal verification of such implementations, Part IV contains high-level specifications of correctness of the basic arithmetic instructions of several major industry-standard floating-point architectures, including all details pertaining to the handling of exceptional conditions. Part V illustrates the methodology, applying the preceding theory to the comprehensive verification of a state-of-the-art commercial floating-point unit. All of these results have been formalized in the logic of the ACL2 theorem prover and mechanically checked to ensure their correctness. They are presented here, however, in simple conventional mathematical notation. The book presupposes no familiarity with ACL2, logic design, or any mathematics beyond basic high school algebra. It will be of interest to verification engineers as well as arithmetic circuit designers who appreciate the value of a rigorous approach to their art, and is suitable as a graduate text in computer arithmetic."