177 research outputs found
Upravljanje aktivnom snagom regenerativnog kaskadnog invertera uz smanjenje izmjeniÄne komponente snage u istosmjernom meÄukrugu
This paper presents a regenerative cascade inverter topology with direct active power control scheme. From the mathematical model of the proposed regenerative cascade inverter, the instantaneous active power and reactive power are derived. Based on the instantaneous \u27abc\u27 theory, direct active power feed-forward control is proposed to reduce AC power and voltage fluctuation in DC link. Moreover, a load current full order observer is established to avoid the inconvenient of the current sensor installation. The proposed control scheme is analyzed theoretically by simulation, and verified experimentally.U ovome radu predstavljena je topologija regenerativnog kaskadnog invertera sa shemom direktnog upravljanja aktivnom snagom. Trenutna aktivna i reaktivna snaga dobiveni su iz matematiÄkog modela predloženog regenerativnog kaskadnog invertera. Na temelju \u27abc\u27 teorije, predloženo je unaprijedno upravljanje aktivnom snagom kako bi se smanjila izmjeniÄna komponenta snage i promjena napona u istosmjernom meÄukrugu. Ugradnja senzora za mjerenje struje izbjegla se razvijanjem observera punog reda. Predložena shema upravljanja testirana je simulacijski i eksperimentalno
HFGD: High-level Feature Guided Decoder for Semantic Segmentation
Existing pyramid-based upsamplers (e.g. SemanticFPN), although efficient,
usually produce less accurate results compared to dilation-based models when
using the same backbone. This is partially caused by the contaminated
high-level features since they are fused and fine-tuned with noisy low-level
features on limited data. To address this issue, we propose to use powerful
pretrained high-level features as guidance (HFG) when learning to upsample the
fine-grained low-level features. Specifically, the class tokens are trained
along with only the high-level features from the backbone. These class tokens
are reused by the upsampler for classification, guiding the upsampler features
to more discriminative backbone features. One key design of the HFG is to
protect the high-level features from being contaminated with proper
stop-gradient operations so that the backbone does not update according to the
gradient from the upsampler. To push the upper limit of HFG, we introduce an
context augmentation encoder (CAE) that can efficiently and effectively
operates on low-resolution high-level feature, resulting in improved
representation and thus better guidance. We evaluate the proposed method on
three benchmarks: Pascal Context, COCOStuff164k, and Cityscapes. Our method
achieves state-of-the-art results among methods that do not use extra training
data, demonstrating its effectiveness and generalization ability. The complete
code will be releasedComment: Revised version, refactored presentation and added more experiment
PREF: Phasorial Embedding Fields for Compact Neural Representations
We present an efficient frequency-based neural representation termed PREF: a
shallow MLP augmented with a phasor volume that covers significant border
spectra than previous Fourier feature mapping or Positional Encoding. At the
core is our compact 3D phasor volume where frequencies distribute uniformly
along a 2D plane and dilate along a 1D axis. To this end, we develop a tailored
and efficient Fourier transform that combines both Fast Fourier transform and
local interpolation to accelerate na\"ive Fourier mapping. We also introduce a
Parsvel regularizer that stables frequency-based learning. In these ways, Our
PREF reduces the costly MLP in the frequency-based representation, thereby
significantly closing the efficiency gap between it and other hybrid
representations, and improving its interpretability. Comprehensive experiments
demonstrate that our PREF is able to capture high-frequency details while
remaining compact and robust, including 2D image generalization, 3D signed
distance function regression and 5D neural radiance field reconstruction
Emotional Labor in Knowledge-Based Service Relationships: The Roles of Self-Monitoring and Display Rule Perceptions
Focusing on knowledge-based service relationships, this study examined knowledge-based service workersā (i.e., school teachers) emotional labor process and the consequential outcomes for their well-being. The study also examined the roles of two antecedents, namely, teachersā perceptions of display rules and self-monitoring tendencies. A sample of 1,656 school teachers participated in the study. The results showed that self-monitoring generally had stronger, though maladaptive, effects than display rule perceptions on individualsā use of emotional labor strategies (ELS) (i.e., surface acting and deep acting) and well-being (i.e., anxiety, depression, contentment, and enthusiasm). Both self-monitoring and display rule perceptions were positively related to two ELS. There were relatively stronger relationships between self-monitoring and surface acting, and between display rule perceptions and deep acting. Surface acting was positively related to anxiety and depression and negatively related to contentment and enthusiasm. Deep acting was positively related to anxiety, contentment, and enthusiasm. The examination of indirect effects showed that self-monitoring was positively related to anxiety and depression and negatively related to enthusiasm and contentment. Display rule perceptions were weakly, but positively, related to anxiety and depression. These results suggest that self-monitoring may be less beneficial than previously thought. Knowledge-based service workersā display rule perceptions and deep acting may not necessarily be harmful to their well-being, but reflect their role identification and commitment. Theoretical contributions and practical suggestions of this study were discussed
Do Chinese Teachers Perform Emotional Labor Equally? Multi-Group Comparisons Across Genders, Grade Levels and Regions
The emotional aspects of teaching are important and teachersā emotional labor, or, how teachers manage emotions at school, has been attracting more and more attention recently. Using multi-group structural equation modeling, this study investigated the measurement invariance of, and the relationships between, teachersā emotional labor strategies and teaching satisfaction. Participants included teachers from primary and secondary schools in Hong Kong and mainland China. Three sets of group comparisons have been made between female and male teachers, between primary and secondary school teachers, and between teachers in Hong Kong and mainland China. The multi-group invariance tests showed no significant subgroup differences in the measurement and structural models. Thus, there was no difference of ākind.ā However, some differences of ādegreeā were observed across genders, grade levels and regions. These differences in the relationship between surface/deep acting and teaching satisfaction can be attributed to the possible influence of some cognitive factors and socio-cultural contexts. With due methodological rigor, the results of this study provide deeper understanding of teachersā emotional labor and its relationship with teaching satisfaction
A Multilevel Analysis of Job Characteristics, Emotion Regulation, and Teacher Well-Being: A Job Demands-Resources Model
This study integrated personal factors into the job demands-resources (JD-R) model to examine school- and individual-level predictors of teacher well-being. Survey data were gathered from 1,656 teachers from 54 schools. The results of hierarchical linear modeling indicated that the school-level emotional job demands of teaching and suppression at the individual level were positively related to teachers' anxiety and depression whereas school-level trust in colleagues and individual-level reappraisal were positively associated with enthusiasm and contentment. Positive relationship between emotional job demands and suppression was also found. These findings support the claim that reappraisal should be considered a personal resource and suppression a personal demand
LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation
Gestures are non-verbal but important behaviors accompanying people's speech.
While previous methods are able to generate speech rhythm-synchronized
gestures, the semantic context of the speech is generally lacking in the
gesticulations. Although semantic gestures do not occur very regularly in human
speech, they are indeed the key for the audience to understand the speech
context in a more immersive environment. Hence, we introduce LivelySpeaker, a
framework that realizes semantics-aware co-speech gesture generation and offers
several control handles. In particular, our method decouples the task into two
stages: script-based gesture generation and audio-guided rhythm refinement.
Specifically, the script-based gesture generation leverages the pre-trained
CLIP text embeddings as the guidance for generating gestures that are highly
semantically aligned with the script. Then, we devise a simple but effective
diffusion-based gesture generation backbone simply using pure MLPs, that is
conditioned on only audio signals and learns to gesticulate with realistic
motions. We utilize such powerful prior to rhyme the script-guided gestures
with the audio signals, notably in a zero-shot setting. Our novel two-stage
generation framework also enables several applications, such as changing the
gesticulation style, editing the co-speech gestures via textual prompting, and
controlling the semantic awareness and rhythm alignment with guided diffusion.
Extensive experiments demonstrate the advantages of the proposed framework over
competing methods. In addition, our core diffusion-based generative model also
achieves state-of-the-art performance on two benchmarks. The code and model
will be released to facilitate future research.Comment: Accepted by ICCV 202
Manipulation of LIPSS orientation on silicon surfaces using orthogonally polarized femtosecond laser double-pulse trains
Laser-induced periodic surface structures (LIPSS) provide an easy and costeffective means of fabricating gratings and have been widely studied in recent decades. To overcome the challenge of orientation controllability, we developed a feasible and efficient method for manipulating the orientation of LIPSS in real time. Specifically, we used orthogonally polarized and equal-energy femtosecond laser (50 fs, 800 nm) double-pulse trains with time delay about 1ps, total peak laser fluence about 1.0 J/cm2, laser repetition frequency at 100 Hz and scanning speed at 150 Ī¼m/s to manipulate the LIPSS orientation on silicon surfaces perpendicular to the scanning direction, regardless of the scanning paths. The underlying mechanism is attributed to the periodic energy deposition along the direction of surface plasmon polaritons (SPPs), which can be controlled oriented along the scanning direction in orthogonally polarized femtosecond laser double-pulse trains surface scan processing. An application of structural colors presents the functionality of our method
Manipulation of LIPSS orientation on silicon surfaces using orthogonally polarized femtosecond laser double-pulse trains
Laser-induced periodic surface structures (LIPSS) provide an easy and costeffective means of fabricating gratings and have been widely studied in recent decades. To overcome the challenge of orientation controllability, we developed a feasible and efficient method for manipulating the orientation of LIPSS in real time. Specifically, we used orthogonally polarized and equal-energy femtosecond laser (50 fs, 800 nm) double-pulse trains with time delay about 1ps, total peak laser fluence about 1.0 J/cm2, laser repetition frequency at 100 Hz and scanning speed at 150 Ī¼m/s to manipulate the LIPSS orientation on silicon surfaces perpendicular to the scanning direction, regardless of the scanning paths. The underlying mechanism is attributed to the periodic energy deposition along the direction of surface plasmon polaritons (SPPs), which can be controlled oriented along the scanning direction in orthogonally polarized femtosecond laser double-pulse trains surface scan processing. An application of structural colors presents the functionality of our method
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