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PERSENGKETAAN AMERIKA SERIKAT-UNI SOVIET DI PASIFIK
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| Pengarang | : | Endi Rukmo |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1108-1116 |
| Abstrak | : | - |
KAWASAN TELUK PARSI PUSAT PERTARUNGAN SUPERPOWER
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1090-1107 |
| Abstrak | : | - |
PERIMBANGAN KEKUATAN LAUT SUPERPOWER
| Pengarang | : | Alfian Muthalib |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1076-1089 |
| Abstrak | : | - |
POKOK-POKOK POLITIK LUAR NEGERI AMERIKA SERIKAT DI BAWAH PRESIDEN REAGAN DAN DAMPAKNYA ATAS ASIA KHUSUSNYA ASIA TENGGARA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1065-1075 |
| Abstrak | : | - |
Federated Domain Generalization: A Survey
| Pengarang | : | Ying Li; Xingwei Wang; Rongfei Zeng; Praveen Kumar Donta; Ilir Murturi; Min Huang; Schahram Dustdar |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 4) |
| Halaman | : | 370-410 |
| Abstrak | : | Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions. |
RENCANA UNI SOVIET UNTUK MENCAPAI KEMENANGAN DALAM PERANG NUKLIR
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1056-1064 |
| Abstrak | : | - |
TUJUAN STRATEGI GLOBAL UNI SOVIET DALAM DASAWARSA 1980-AN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1041-1055 |
| Abstrak | : | - |
AMERIKA SERIKAT DAN UNI SOVIET DALAM "PERANG DINGIN" KEDUA DAN IMPLIKASINYA BAGI ASIA TENGGARA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-12, DESEMBER (No. 12) |
| Halaman | : | 1033-1040 |
| Abstrak | : | - |
Human vs. Computer Go: Review and Prospect
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 3) |
| Halaman | : | 67-72 |
| Abstrak | : | Tct he Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications. |
Talkative Power Conversion: A Tutorial
| Pengarang | : | Peter Adam Hoeher; Yang Leng; Rongwu Zhu; Marco Liserre |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 4) |
| Halaman | : | 344-369 |
| Abstrak | : | This article provides a systematic overview of the basics of talkative power conversion (TPC). TPC is an emerging technique for simultaneous information and power transmission, in which data modulation is integrated into a switched-mode power converter. The data sequence is embedded in the ripple voltage, which is superimposing the output voltage of the converter. In contrast to conventional power line communication (PLC), TPC can be used universally, not only in grid applications. Aspects of power electronics (PE) and digital communication are presented in a structured form, including new perspectives such as multiple-input multiple-output (MIMO) techniques applied to TPC, adaptive modulation and channel coding, and advanced receiver design with adaptive channel and load estimation. The new aspects aim to mitigate the inherent shortcomings of TPC. |