画像は参考用です
S3N
+BOMDIODE GEN PURP 1200V 3A SMC
-
メーカーオンセミコンダクター社
-
メーカー品番 #S3N
-
データシート S3N DataSheet
-
パッケージ DO-214AB
-
在庫状況5779
365 日間品質保証
7*24 日間の品質保証
90-時間単位のサービス保証
1日間のアフターサービス保証
仕様
| 属性 | 値 |
| Package / Case | Tape & Reel (TR),Cut Tape (CT) |
| Part Status | Active |
| Diode Type | Standard |
| Voltage - DC Reverse (Vr) (Max) | 1200 V |
| Current - Average Rectified(Io) | 3A |
| Voltage - Forward (Vf) (Max) @ If | 1.2 V @ 3 A |
| Speed | Standard Recovery >500ns, > 200mA (Io) |
| Reverse Recovery Time (trr) | 2.5 µs |
| Current - Reverse Leakage @ Vr | 5 µA @ 1200 V |
| Capacitance @ Vr F | 60pF @ 4V, 1MHz |
| Mounting Type | Surface Mount |
| Supplier Device Package | SMC (DO-214AB) |
概要
Description
S3N (Scalable Storage Service Network) is a distributed storage system designed for high scalability, reliability, and performance. It leverages peer-to-peer (P2P) architecture to decentralize data storage, ensuring fault tolerance and efficient resource utilization.
Key features include:
- Decentralization: Eliminates single points of failure by distributing data across nodes.
- Scalability: Dynamically accommodates growing storage demands.
- Security: Uses encryption and redundancy to protect data integrity.
- Cost-Efficiency: Reduces reliance on centralized cloud providers.
S3N is ideal for applications requiring robust, scalable storage, such as big data, IoT, and blockchain. Its open-source nature encourages community-driven innovation.
Features
1. Self-Supervised Learning: Reduces reliance on labeled data by leveraging unlabeled data for pre-training.
2. Contrastive Learning: Enhances feature discrimination by contrasting positive/negative samples.
3. Multi-Task Framework: Combines pixel-level and region-level tasks for robust feature learning.
4. Adaptive Augmentation: Uses dynamic data augmentation to improve generalization.
5. Efficiency: Optimized for performance with lower computational costs compared to fully supervised methods.
S3N excels in scenarios with limited labeled data, offering scalable and accurate segmentation.