A5101794204- Advanced Memory and Neural Computing
- Transportation Planning and Optimization
- Traffic Prediction and Management Techniques
- Ferroelectric and Negative Capacitance Devices
- Millimeter-Wave Propagation and Modeling
- Human Mobility and Location-Based Analysis
- Transition Metal Oxide Nanomaterials
Andong National University
2024
University of Seoul
2022
Resistive random-access memory (RRAM) has been explored to implement neuromorphic systems accelerate neural networks. In this study, an RRAM crossbar array using parylene C (PPXC) as both a resistive switching layer and substrate was fabricated. PPXC is flexible transparent polymer with excellent chemical stability biocompatibility. We studied PPXC-based devices Ti/PPX-C/Cu Cu/PPX-C/Ti structures. Devices the structure offer stable electrical mechanical characteristics, such low set voltage...
This paper presents a model for predicting hourly subway ridership based on weather conditions. The goal is to provide efficient system service prediction results. Three artificial intelligence algorithms, Multiple Linear Regression (MLR), Random Forest (RFR), and Multi-Layer Perceptron (MLP), were used compare analyze the performance of our ridership. models was optimized across two stations with diverse different regional characteristics rather than single station. To results, 8 out 15...
초록·키워드 목차 오류제보하기 밀리미터파 다중셀 네트워크는 방해물, 심한 전파손실, 빔포밍과 같은 고유 속성들로 인해 일반적으로 복잡한 수학적 분석이나 긴 시뮬레이션 시간을 필요로 한다. 이에 따라, 본 논문은 다항식 로지스틱 함수와 인공신경망을 활용한 SINR 분포의 근사화 방안을 제안하고, 제안 방안은 신속하면서도 상대적으로 정확한 성능을 제공함을 컴퓨터 시뮬레이션을 통해 입증한다. #Millimeter wave #cellular network #SINR #machine learning #5G 요약ABSTRACTⅠ. 서론Ⅱ. 시스템 모델Ⅲ. 인공신경망 구성Ⅳ. 성능 분석 및 응용Ⅴ. 결론References