Multiple resonance (MR) emitters are promising for highly efficient organic light-emitting diodes (OLEDs) with narrowband emission;h owever,t hey still face intractable challenges with concentration-caused emission quenching, exciton annihilation, and spectral broadening. In this study, sterically wrapped MR dopants with af luorescent MR core sandwiched by bulk substituents were developed to address the intractable challenges by reducing intermolecular interactions. Consequently,h igh photo-luminance quantum yields of ! 90 %a nd small full width at half maximums (FWHMs) of 25 nm over aw ide range of dopant concentrations (1-20 wt %) were recorded. In addition, we demonstrated that the sandwiched MR emitter can effectively suppress Dexter interaction when doped in at hermally activated delayed fluorescence sensitizer,eliminating exciton loss through dopant triplet. Within the above dopant concentration range,t he optimal emitter realizes remarkably high maximum external quantum efficiencies of 36.3-37.2 %, identical small FWHMs of 24 nm, and alleviated efficiency roll-offs in OLEDs.

No abstract

No abstract

In this study, an inter-turn fault diagnosis method is proposed based on deep learning algorithm. 12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms. The selected features are convoluted with the original data, generating a one-dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto-encoder, sparsity of sparse auto-encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the interturn fault detection of the three-phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next-generation smart transformers.

The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an e ort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques.In this paper we present an end-to-end model-based approach to medical device software development along with the artifacts created in the process. While outlining the approach, we also describe our experiences, challenges, and lessons learned in the process of formulating and analyzing the requirements, modeling the system, formally verifying the models, generating code, and executing the generated code in the hardware for generic patient controlled analgesic infusion pump (GPCA). We believe that the development artifacts and techniques presented in this paper could serve as a generic reference to be used by researchers, practitioners, and authorities while developing and evaluating cyber physical medical devices. Abstract. The advanced use of technology in medical devices has improved the way health care is delivered to patients. Unfortunately, the increased complexity of modern medical devices poses challenges for development, assurance, and regulatory approval. In an effort to improve the safety of advanced medical devices, organizations such as FDA have supported exploration of techniques to aid in the development and regulatory approval of such systems. In an ongoing research project, our aim is to provide effective development techniques and exemplars of system development artifacts that demonstrate state of the art development techniques. In this paper we present an end-to-end model-based approach to medical device software development along with the artifacts created in the process. While outlining the approach, we also describe our experiences, challenges, and lessons learned in the process of formulating and analyzing the requirements, modeling the system, formally verifying the models, generating code, and executing the generated code in the hardware for generic patient controlled analgesic infusion pump (GPCA). We believe that the development artifacts and techniques presented in this paper could serve as a generic reference to be used by researchers, practitioners, and authorities while developing and evaluating cyber physical medical devices.