This study presents an appliance identification algorithm for use with a non-intrusive home energy monitor based on a cogent confabulation neural network. As a cogent confabulation neural network does not require multiplications during the identification phase, it is an effective choice for systems with low-computational capability. A non-intrusive home energy monitor needs to learn not only the energy patterns of individual appliances but also those of combinations of appliances. To relieve the burden of learning power patterns of the combinations, this study proposes a parameter-building scheme based on the parameters of individual appliances. The proposed algorithm is evaluated on datasets prepared by the Reference Energy Disaggregation Dataset (REDD) and the authors. The average success rate was 83.8% for up to eight appliances and showed better performance than the combinatorial optimization and artificial neural network approaches.

Although both noise suppression and speech restoration are fundamental to speech enhancement, many Deep neural network (DNN)-based approaches tend to focus disproportionately on one, often overlooking the importance of their joint handling. In this study, we propose a dual-path architecture designed to balance noise suppression and speech restoration. The main path consists of an encoder and two specialized decoders: one dedicated to estimating the clean speech spectrum and the other to predicting a noise suppression mask. To reinforce the joint modeling of noise suppression and speech restoration, we introduce an auxiliary refinement path. This path consists of a separate encoder–decoder structure and is designed to further refine the enhanced speech by incorporating complementary information, learned independently from the main path. By using this dual-path architecture, the model better preserves fine speech details while reducing residual noise. Experimental results on the VoiceBank + DEMAND dataset show that our model surpasses conventional methods across multiple evaluation metrics in the causal setup. Specifically, it achieves a PESQ score of 3.33, reflecting improved speech quality, and a CSIG score of 4.48, indicating enhanced intelligibility. Furthermore, it demonstrates superior noise suppression, achieving an SNRseg of 10.44 and a CBAK score of 3.75.

인공위성의 부품은 발사 전 지상에서의 우주 궤도환경 하에서의 성능검증이 필수적이다. 본 논문은 열진공시험을 위한 위성 부품 및 소형 열진공챔버 간의 전도, 복사, 열손실 및 내부 발열의 열환경을 고려한 모델식을 설계하였다. 최근 한국항공우주연구원에서 수행된 열진공시험 20건의 시험데이터를 이용한 시스템 식별을 수행하였다. 시험품 기능시험의 발열이 일정하지 않는 경우를 제외하고 시스템 식별의 정확성을 확인함으로써 개발된 모델식이 유효함을 검증하였다.