The optical properties of N,N’-bis (Inaphthyl)N,N’-diphenyl-1,1’-biphenyl-4,4’-diamine (NPB) and tris (8-hydroxyquinolinato) aluminum (Alq3) organic materials used as hole transport and electron transport layers i...The optical properties of N,N’-bis (Inaphthyl)N,N’-diphenyl-1,1’-biphenyl-4,4’-diamine (NPB) and tris (8-hydroxyquinolinato) aluminum (Alq3) organic materials used as hole transport and electron transport layers in organic light-emitting devices (OLED) have been investigated. The NPB and Alq3 layers were prepared using thermal evaporation method. The results show that the energy band gap of Alq3 is thickness independence while the energy band gap of NPB decreases with the increasing of sample thickness. For the case of photoluminescence the Alq3 with thickness of 84 nm shows the highest relative intensity peak at 510 nm.展开更多
为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency ide...为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency identification sonar,DIDSON)数据,开发了1种快速、准确的鱼类目标识别与计数方法。实验结果表明,YOLOv8X与ByteTrack联合方法与传统的Echoview软件识别精度接近(偏差率仅为1.36%),但处理时间显著减少(单条测线从约30 min减少至约3 min),表现出较强的实时处理能力和泛化性能。同时,通过重复实验验证了该方法的稳定性,确认其在不同场景中的可靠性。本研究方法与成果为水域鱼类资源的自动化监测提供了可靠的技术支持,可广泛地应用于大范围高频次的渔业资源监测与管理工作中。展开更多
文摘The optical properties of N,N’-bis (Inaphthyl)N,N’-diphenyl-1,1’-biphenyl-4,4’-diamine (NPB) and tris (8-hydroxyquinolinato) aluminum (Alq3) organic materials used as hole transport and electron transport layers in organic light-emitting devices (OLED) have been investigated. The NPB and Alq3 layers were prepared using thermal evaporation method. The results show that the energy band gap of Alq3 is thickness independence while the energy band gap of NPB decreases with the increasing of sample thickness. For the case of photoluminescence the Alq3 with thickness of 84 nm shows the highest relative intensity peak at 510 nm.
文摘为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency identification sonar,DIDSON)数据,开发了1种快速、准确的鱼类目标识别与计数方法。实验结果表明,YOLOv8X与ByteTrack联合方法与传统的Echoview软件识别精度接近(偏差率仅为1.36%),但处理时间显著减少(单条测线从约30 min减少至约3 min),表现出较强的实时处理能力和泛化性能。同时,通过重复实验验证了该方法的稳定性,确认其在不同场景中的可靠性。本研究方法与成果为水域鱼类资源的自动化监测提供了可靠的技术支持,可广泛地应用于大范围高频次的渔业资源监测与管理工作中。
