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Minjie Ye

Publications and source records attributed to Minjie Ye.

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Development and Performance Study of Vertical GaN $α$-Particle Detector with High Energy Resolution

High-energy-resolution GaN $α$-particle detectors have significant potential for space radiation, nuclear instrumentation, and harsh-environment applications. However, existing GaN $α$-particle detectors still face several key challenges, including reducing the dead-layer thickness, suppressing leakage current under high reverse bias, improving energy resolution, and clarifying the physical mechanism underlying the low-energy tail phenomenon. This study presents a vertical homoepitaxial GaN $α$-particle detector integrating a 20-nm ultrathin dead layer and a guard-ring structure. The detector exhibits an ultralow leakage current of 2.195 nA at -200 V and an intrinsic energy resolution of 2.69% with a charge collection efficiency (CCE) of 95.9% at -260 V. More importantly, this work demonstrates for the first time through Geant4 simulations that depletion-width nonuniformity is the dominant source of partial energy leakage, leading to an extended low-energy tail in the energy spectrum. We establish a depletion-width nonuniformity model and observe good agreement between simulation and experiment. This finding provides practical guidance for the design and optimization of high-performance GaN-based radiation detectors.

physics.ins-det

Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN

Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from various research fields. Traditionally, the diagnosis of depression is formulated through semi-structured interviews and supplementary questionnaires, which makes the diagnosis heavily relying on physicians experience and is subject to bias. Mental health monitoring and cloud-based remote diagnosis can be implemented through an automated depression diagnosis system. In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. For the audio modality, we use the collaborative voice analysis repository (COVAREP) features provided by the dataset and employ a Bidirectional Long Short-Term Memory Network (Bi-LSTM) followed by a Time-distributed Convolutional Neural Network (T-CNN). For the text modality, we use global vectors for word representation (GloVe) to perform word embeddings and the embeddings are fed into the Bi-LSTM network. Results show that both audio and text models perform well on the depression severity estimation task, with best sequence level F1 score of 0.9870 and patient-level F1 score of 0.9074 for the audio model over five classes (healthy, mild, moderate, moderately severe, and severe), as well as sequence level F1 score of 0.9709 and patient-level F1 score of 0.9245 for the text model over five classes. Results are similar for the multimodality fused model, with the highest F1 score of 0.9580 on the patient-level depression detection task over five classes. Experiments show statistically significant improvements over previous works.

cs.HC