Explore the vast range of titles available.

There are two forms of engine fuel metering devices which are single and double chamber structures using electro-hydraulic servo valves as electro-hydraulic conversion devices to realize accurate fuel flow measurements and constant pressure differential valve is used to maintain the constant pressure difference of the metering valve. The main purpose of this article is to analyze the time domain and frequency domain characteristics of constant pressure differential valve assembly and compare the closed-loop characteristics of two metering mechanisms. Firstly, the mathematical model of constant pressure differential valve assembly (including the fuel pump and constant pressure differential valve) is established and its time and frequency domain characteristics are analyzed. The conclusion that the constant pressure differential of the metering valve can meet the practical requirements is obtained. Secondly, the mathematical models of single and double-chamber fuel metering mechanisms are established considering the specific working conditions. Finally, the characteristics of two kinds of metering mechanisms are compared and analyzed under the same control method in the time and frequency domain. The extension overshoot with a maximum value of 3.564% of the single chamber control metering valve mechanism is smaller than that of the double chamber control metering valve mechanism with a maximum extension overshoot of 6.04%. The negative overshoot with a maximum value of 1.391% of the single chamber control metering valve mechanism is bigger than the double chamber control metering valve mechanism with a maximum negative overshoot value of 1.17%. In terms of steady-state error, the steady-state error of a single chamber control metering valve mechanism with a maximum value of 0.194 mm and minimum value of 1.25e-6 mm is smaller than that maximum value of 0.302 mm and minimum value of 1.95e-6 mm of double chamber control metering valve mechanism under the same controller parameters. The bandwidth of the extension motion of the single and double chamber control metering valve is greater than that of the retraction motion. Under the same proportional control parameter, the bandwidth of the double chamber control metering valve extension motion system is greater than that of the single chamber extension motion system.

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states.

Identifying the growth period of bee colonies can guide beekeepers to make better decisions and promote the development of bee colonies. Unlike traditional manual experience-based recognition, this paper proposes a new approach, which combines multivariate temperature feature extraction and machine learning to intelligently recognize the growth period of bee colonies. Firstly, the year-round temperature data from 38 hives in Tai’an and Guilin was collected. Then, the 17 time domain characteristic indices were extracted from this dataset. To acquire the most sensitive features, the impact of different time scales on temperature feature extraction was analyzed. Subsequently, principal component analysis (PCA) was employed to reduce the dimensionality of the original feature vectors, thereby decreasing computational load and enhancing feature sensitivity. Finally, six machine learning algorithms, including both supervised and unsupervised learning, were utilized to identify the growth period of bee colonies. The results demonstrate that the proposed features can effectively characterize the growth period of bee colonies, and the BP method performs best in predicting growth period categories, with an MAE of only 1.45%. Moreover, the identification results of different regions also prove the practicability of the proposed method.

The frequency domain characteristics of acoustic emission can reflect issues such as rock structure and stress conditions that are difficult to analyze in time domain parameters. Studying the influence of immersion time on the mechanical properties and acoustic emission frequency domain characteristics of muddy mineral rocks is of great significance for comprehensively analyzing rock changes under water–rock coupling conditions. In this study, uniaxial compression tests and acoustic emission tests were conducted on sandstones containing montmorillonite under dry, saturated, and different immersion time conditions, with a focus on analyzing the effect of immersion time on the dominant frequency of rock acoustic emission. The results indicated that immersion time had varying degrees of influence on compressive strength, the distribution characteristics of dominant acoustic emission frequencies, the frequency range of dominant frequencies, and precursor information of instability failure for sandstones. After initial saturation, the strength of the rock sample decreased from 53.52 MPa in the dry state to 49.51 MPa, and it stabilized after 30 days of immersion. Both dry and initially saturated rock samples exhibited three dominant frequency bands. After different immersion days, a dominant frequency band appeared between 95 kHz and 110 kHz. After 5 days of immersion, the dominant frequency band near 0 kHz gradually disappeared. After 60 days of immersion, the dominant frequency band between 35 kHz and 40 kHz gradually disappeared, and with increasing immersion time, the dominant frequency of the acoustic emission signals increased. During the loading process of dry rock samples, the dominant frequency of acoustic emission signals was mainly concentrated between 0 kHz and 310 kHz, while after saturation, the dominant frequencies were all below 180 kHz. The most significant feature before the rupture of dry rock samples was the frequent occurrence of high frequencies and sudden changes in dominant frequencies. Before rupture, the characteristics of precursor events for initially saturated and immersed samples for 5, 10, and 30 days were the appearance and rapid increase in sudden changes in dominant frequencies, as well as an enlargement of the frequency range of dominant frequencies. After 60 days of immersion, the precursor characteristics of rock sample rupture gradually disappeared, and sudden changes in dominant frequencies frequently occurred at various stages of sample loading, making it difficult to accurately predict the rupture of specimens based on these sudden changes.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

Background The homeodomain-leucine zipper ( HD-Zip ) transcription factors play crucial roles in plant growth and development or in response to various abiotic stresses. Results In our research, fifty-eight HD-Zip genes were identified in tea plant, namely CsHDZ01 - 58 and divided into four subfamilies, I-IV. All the CsHDZ proteins of four subfamilies harbored HD and LZ domains. Subfamily II CsHDZs contained two additional motifs, EAR and CPSCE. Two extra characteristic domains, START and SAD, were included in subfamily III and IV CsHDZs. While MEKHLA domain was distinctive in subfamily III members. All the subfamily III CsHDZ s were predicted to be targets of csn-miR166. A total of 24 duplicated CsHDZ gene pairs were identified and generated by segmental duplication in tea plant. The expression analysis indicated that most subfamily IV CsHDZ genes were expressed at high level in apical bud, and most subfamily III CsHDZs were high-expressed in stem. CsHDZ family genes exibited diverse expression profiles under cold, drought and salt stresses respectively. It implied that CsHDZ gene family may participate in regulating the response to various abiotic stresses in tea plant. Conclusion These results will provide an important foundation for further exploring the function and molecular mechanism of HD-Zip genes in response to various abiotic stresses in tea plant.

Domain adaptive person re-identification (re-ID) is a challenging task due to the large domain divergency between different datasets and the complicated variations of the target domain. Style-transferring based methods mainly narrow the domain divergency with respect to a specific imaging factor, e.g., illumination. However, in consideration of the complex practical scenarios, domain adaptive re-ID demands comprehensive domain characteristic knowledge, i.e., seasons, illuminations, camera views, etc., to alleviate the domain divergency and intra-domain variations. This paper proposes a data-driven Instance Interactive Association Graph Convolutional Network (IIAGCN) to tackle the problems. Specifically, our IIAGCN method first constructs a cross-domain knowledge graph with the inter-domain nodes (target-source image pairs) and the intra-domain nodes (target-target image pairs). Then an Information Interactive Graph Convolutional (IIGC) layer is designed to extract the instance-level domain characteristic knowledge from the knowledge graph. With the learned knowledge, we can learn domain characteristic-aware and discriminative image representations for better domain adaptation. In addition, we introduce the memory bank component to store image features of the whole dataset, which enlarges the node diversity of the knowledge graph. Experiments on large-scale person re-ID datasets demonstrate the superiority of our method under the unsupervised re-ID setting.

Resonance boards of Chinese traditional instruments such as the Guzheng and Guqin typically are arched, with the arch height influencing their resonance characteristics. This study focuses on Paulownia wood utilized for resonance boards. The bottom surfaces were thinned in 1 mm increments, with vibration signatures acquired at each reduction stage using a multi-channel FFT analyzer. Subsequently, time-domain characteristic parameters of the signals were extracted through MATLAB-based signal processing. Modal and harmonic response simulations of the structure were conducted using finite element software. The results indicated that variations in arch height affected the frequency spectrum response of the vibrations of Paulownia wood, altering the structural energy radiation levels. Lower arch heights (0–2 mm) had a greater impact on the fundamental frequency. The arch height was 1 mm and 2 mm, with R1,1 and R1,2 being −5.31% and −8.62%, respectively. Skewness and kurtosis were negatively correlated with arch height. When ΔH was 3.06, the radiation effect was optimal. The changes in arch height influenced the vibrational modes and energy distribution of Paulownia. Higher arch heights (3–6 mm) have less effect on the fundamental frequency and impose some constraints on the mode vibration pattern. Furthermore, the results of the frequency-domain and time-domain analyses were found to be largely consistent with the finite element simulation results. The results provide guidance for changing the arch height to modulate the acoustic vibration response of the resonance board, which is of significance for the personalized design of future musical instruments.

Aiming at the essential feature of the time-continuity of birdsong in nature, this paper proposed a birdsong classification model composed of two feature channels, which combines the features of time domain and time frequency domain. In order to make better use of the features, we used the improved average threshold method to denoise the original time-domain waveform features to reduce the influence of noise features. The most suitable feature extractor and the best fusion method of these two features are discussed. In this paper, the 3D convolutional neural network (3DCNN) and 2D convolutional neural network (2DCNN) were respectively applied as feature extractors of log_mel spectrum and waveform images. Then the advanced feature, which was extracted from these two feature channels, was fused in the middle stage, and the output enhanced feature was used as the input of double gated recurrent unit (d-GRU) network. In the work, birdsongs of four species from Xeno-Canto were selected for testing. The results showed that these three methods had improved the classification effect: feature fusion method in time domain and time-frequency domain, weighted average threshold noise reduction method and the method of extracting birdsong features via different types of feature extractors. The method of this paper had achieved mean average precision ( MAP ) of 95.9% in the classification comparison experiments, which was an inspiring outcome.

Three-point bending fatigue test of 5083 and 5A06 aluminum alloy T-welded joints is carried out, and the fatigue life of the specimens with different influencing factors are obtained. Finite element model of the T-welded joint is established and the nodal force based structural stress is calculated. Neighborhood rough set theory is used for analysis of the factors which influence the fatigue life of the aluminum alloy welded joints. Key influencing factors are studied and the fatigue characteristic domains are determined. The master S-N curve characterized by the nodal force based structural stress range and cycles to failure on semi log coordinate as well as S-N curves corresponding to the fatigue characteristic domain is fitted. A case study of fatigue life prediction of 5A06 aluminum alloy welded joint indicates the effectiveness of the fatigue life prediction method based on the fatigue characteristic domain.