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Released 2016, November 160g, 8.9mm thickness Android 6.0 8GB/16GB storage, microSDXC Samsung Galaxy J2 Prime was announced at 2016, November. Released 2016, November with Android 6.0 (Marshmallow), have chipset Mediatek MT6737T (28 nm) CPU Quad-core 1.4 GHz Cortex-A53 and GPU Mali-T720MP2. It have 8GB 1.5GB RAM, 16GB 1.5GB RAM storage and there are colors: Black, Gold, Silver, Pink NETWORK Technology GSM / HSPA / LTE 2G GSM 850 / 900 / 1800 / 1900 - SIM 1 & SIM 2 (dual-SIM model only) 3G HSDPA 850 / 900 / 1900 / 2100 - G532F, G532G 4G 1, 3, 5, 7, 8, 20 - G532F Speed HSPA 42.2/5.76 Mbps, LTE Cat4 150/50 Mbps TIMELINE Announced 2016, November. Released 2016, November Released Released 2016, November BODY Dimensions 144.8 x 72.1 x 8.9 mm (5.70 x 2.84 x 0.35 in) Build Glass front, plastic back, plastic frame SIM Single SIM (Micro-SIM) or Hybrid Dual SIM (Micro-SIM, dual stand-by) DISPLAY Size 5.0 inches, 68.9 cm2 (~66.0% screen-to-body ratio) Resolution 540 x 960 pixels, 16:9 ratio (~220 ppi density) PLATFORM OS Android 6.0 (Marshmallow) Chipset Mediatek MT6737T (28 nm) CPU Quad-core 1.4 GHz Cortex-A53 MEMORY Card slot microSDXC (uses shared SIM slot) - dual SIM model only Internal 8GB 1.5GB RAM, 16GB 1.5GB RAM COMMUNICATES WLAN Wi-Fi 802.11 b/g/n, Wi-Fi Direct, hotspot GPS Yes, with A-GPS, GLONASS Radio FM radio, RDS, recording USB microUSB 2.0, USB On-The-Go FEATURES Sensors Accelerometer, proximity BATTERY Battery Li-Ion 2600 mAh, removable MISC Colors Black, Gold, Silver, Pink SAR US 0.45 W/kg (head) 0.99 W/kg (body) SAR EU 0.53 W/kg (head) 1.27 W/kg (body) Specs source: gsmarena.com Disclaimer. We can not guarantee that the information on this page is 100% correct 1. Extract (unzip) the Samsung firmware file. Recommended using WinRAR or 7-Zip 2. Download Odin Tool 3.13.1 3. Extract Odin ZIP file 4. Open Odin execute file 5. Reboot Samsung phone in Download Mode (hold Home + Power + Volume Down buttons / hold Volume Down + Bixby buttons then plug-in cable) 6. Connect you Samsung phone and wait until you get a blue sign in Odin 7. Add W printableBlank w 9 Blank w 9 printableForm w 9 2025 printable. W 9 form irs printableBlank w download W- printableIrs form w 9 W printableBlank w 9 Blank w 9 printableForm w 9 2025 printable. W 9 form irs printableBlank w download W- printableIrs form w 9 Adversarial transfer learning. In: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, pp 434–439Liu H, Liu Z, Jia W, Lin X, Zhang S (2020) A novel transformer-based neural network model for tool wear estimation. Meas Sci Technol 31(6):065106Article Google Scholar Zhao R, Wang J, Yan R, Mao K (2016) Machine health monitoring with LSTM networks. In: 2016 10th international conference on sensing technology (ICST), IEEE, pp 1–6Aghazadeh F, Tahan AS, Thomas M (2019, July) Tool condition monitoring method in milling process using wavelet transform and long short-term memory. In Surveillance, Vishno and AVE conferences Google Scholar Cai W, Zhang W, Hu X, Liu Y (2020) A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 31(6):1497–1510Article Google Scholar Zhou JT, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol 104(9):4715–4726Article Google Scholar Gugulothu N, Tv V, Malhotra P, Vig L, Agarwal P, Shroff G (2017) Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv preprint arXiv 1709:01073 Google Scholar Yu W, Kim IY, Mechefske C (2019) Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mech Syst Signal Process 129:764–780Article Google Scholar Wu X, Li J, Jin Y, Zheng S (2020) Modeling and analysis of tool wear prediction based on SVD and BiLSTM. Int J Adv Manuf Technol 106(9):4391–4399Article Google Scholar Wang, J., Yan, J., Li, C., Gao, R. X., & Zhao, R. (2019). Deep heterogeneous GRU model for predictive analytics in smart manufacturing: application to tool wear prediction. Comput Ind, 111, 1-14, 1.Marani M, Zeinali M, Songmene V, Mechefske CK (2021) Tool wear prediction in high-speed turning of a steel alloy using long short-term memoryComments
Released 2016, November 160g, 8.9mm thickness Android 6.0 8GB/16GB storage, microSDXC Samsung Galaxy J2 Prime was announced at 2016, November. Released 2016, November with Android 6.0 (Marshmallow), have chipset Mediatek MT6737T (28 nm) CPU Quad-core 1.4 GHz Cortex-A53 and GPU Mali-T720MP2. It have 8GB 1.5GB RAM, 16GB 1.5GB RAM storage and there are colors: Black, Gold, Silver, Pink NETWORK Technology GSM / HSPA / LTE 2G GSM 850 / 900 / 1800 / 1900 - SIM 1 & SIM 2 (dual-SIM model only) 3G HSDPA 850 / 900 / 1900 / 2100 - G532F, G532G 4G 1, 3, 5, 7, 8, 20 - G532F Speed HSPA 42.2/5.76 Mbps, LTE Cat4 150/50 Mbps TIMELINE Announced 2016, November. Released 2016, November Released Released 2016, November BODY Dimensions 144.8 x 72.1 x 8.9 mm (5.70 x 2.84 x 0.35 in) Build Glass front, plastic back, plastic frame SIM Single SIM (Micro-SIM) or Hybrid Dual SIM (Micro-SIM, dual stand-by) DISPLAY Size 5.0 inches, 68.9 cm2 (~66.0% screen-to-body ratio) Resolution 540 x 960 pixels, 16:9 ratio (~220 ppi density) PLATFORM OS Android 6.0 (Marshmallow) Chipset Mediatek MT6737T (28 nm) CPU Quad-core 1.4 GHz Cortex-A53 MEMORY Card slot microSDXC (uses shared SIM slot) - dual SIM model only Internal 8GB 1.5GB RAM, 16GB 1.5GB RAM COMMUNICATES WLAN Wi-Fi 802.11 b/g/n, Wi-Fi Direct, hotspot GPS Yes, with A-GPS, GLONASS Radio FM radio, RDS, recording USB microUSB 2.0, USB On-The-Go FEATURES Sensors Accelerometer, proximity BATTERY Battery Li-Ion 2600 mAh, removable MISC Colors Black, Gold, Silver, Pink SAR US 0.45 W/kg (head) 0.99 W/kg (body) SAR EU 0.53 W/kg (head) 1.27 W/kg (body) Specs source: gsmarena.com Disclaimer. We can not guarantee that the information on this page is 100% correct 1. Extract (unzip) the Samsung firmware file. Recommended using WinRAR or 7-Zip 2. Download Odin Tool 3.13.1 3. Extract Odin ZIP file 4. Open Odin execute file 5. Reboot Samsung phone in Download Mode (hold Home + Power + Volume Down buttons / hold Volume Down + Bixby buttons then plug-in cable) 6. Connect you Samsung phone and wait until you get a blue sign in Odin 7. Add
2025-04-18Adversarial transfer learning. In: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, pp 434–439Liu H, Liu Z, Jia W, Lin X, Zhang S (2020) A novel transformer-based neural network model for tool wear estimation. Meas Sci Technol 31(6):065106Article Google Scholar Zhao R, Wang J, Yan R, Mao K (2016) Machine health monitoring with LSTM networks. In: 2016 10th international conference on sensing technology (ICST), IEEE, pp 1–6Aghazadeh F, Tahan AS, Thomas M (2019, July) Tool condition monitoring method in milling process using wavelet transform and long short-term memory. In Surveillance, Vishno and AVE conferences Google Scholar Cai W, Zhang W, Hu X, Liu Y (2020) A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 31(6):1497–1510Article Google Scholar Zhou JT, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol 104(9):4715–4726Article Google Scholar Gugulothu N, Tv V, Malhotra P, Vig L, Agarwal P, Shroff G (2017) Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv preprint arXiv 1709:01073 Google Scholar Yu W, Kim IY, Mechefske C (2019) Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mech Syst Signal Process 129:764–780Article Google Scholar Wu X, Li J, Jin Y, Zheng S (2020) Modeling and analysis of tool wear prediction based on SVD and BiLSTM. Int J Adv Manuf Technol 106(9):4391–4399Article Google Scholar Wang, J., Yan, J., Li, C., Gao, R. X., & Zhao, R. (2019). Deep heterogeneous GRU model for predictive analytics in smart manufacturing: application to tool wear prediction. Comput Ind, 111, 1-14, 1.Marani M, Zeinali M, Songmene V, Mechefske CK (2021) Tool wear prediction in high-speed turning of a steel alloy using long short-term memory
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