Upravljanje aktivnom snagom regenerativnog kaskadnog invertera uz smanjenje izmjenične komponente snage u istosmjernom međukrugu

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 matematičkog modela predloženog regenerativnog kaskadnog invertera. Na temelju \u27abc\u27 teorije, predloženo je unaprijedno upravljanje aktivnom snagom kako bi se smanjila izmjenična komponenta snage i promjena napona u istosmjernom međukrugu. 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 workers’ (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, teachers’ 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 individuals’ 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 workers’ 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 teachers’ 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, teachers’ 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 ‘kind.’ However, some differences of ‘degree’ 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 teachers’ 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 μm/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 μm/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