A Recurrent Neural Network Enhanced Unscented Kalman Filter for Human Motion Prediction

This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions

A Comprehensive Study on Energy Management Strategy Design of an Extended-Range Electric Vehicle

University of Technology Sydney. Faculty of Engineering and Information Technology.A comprehensive research on the development process of an extended range logistics vehicle (ERLV) is conducted in the automobile theory and vehicle control points of view. The research priority of the study consists of three parts: the background review and the vehicle structure design, the consumption and cost analysis and the innovation on its control strategy. Firstly, a comprehensive background investigation and literature review of the energy management strategy is discussed. Secondly, an extended range mathematical platform for a logistics van is proposed. It presents a thorough energy consumption and Total cost of ownership analysis for an ERLV. Dynamic programming (DP) algorithm is adopted in the energy management strategy optimization to reveal the optimal energy consumption. Thirdly, a novel auxiliary power unit charging strategy with multi-object optimization is proposed using Reinforcement learning algorithms on the ERLV platform to achieve high fuel conversion efficiency while maintaining battery charging health. The comparative results show that the Soft Actor-Critic (SAC) had a 36% faster convergence speed than a traditional algorithm while providing a smoother and more stable action space. The fuel consumption with SAC also outplays by around 3%, which achieves almost 90% of the DP results

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit

Signal Spectrum Analysis of Sediment Water Impact of Hydraulic Turbine Based on ICEEMDAN-Wavelet Threshold Denoising Strategy

Studies show that sediment erosion is one of the main factors attributing to hydraulic turbine failure. The present paper represents an investigation into acoustic vibration signals generated by the water flow impacting the hydraulic turbine runner under three different operating conditions. Collected signals were denoised using the ICEEMDAN-wavelet threshold method, and then the spectral characteristics and sample entropy characteristics of the signals for the three operating conditions were analyzed. The results show that when clean water flows through the hydraulic turbine, the sample entropy reaches its smallest values and the dominant frequency component in the spectrogram is 59.39 Hz. When transitioning from clean water to the flood flow containing 2–4 mm sediment particles, the sample entropy is increasing and a high-frequency component higher than 59.39 Hz becomes the prominent frequency of the spectrogram. Meanwhile, the formation of high-frequency components increases with the sand-containing particle size. Based on the spectral characteristics and sample entropy characteristics of the acoustic vibration signals under different operating conditions, it can provide a reference for the sand avoidance operation of the hydraulic turbine during flood season. In addition, it provides a supplement to the existing hydraulic turbine condition’s monitoring systems and a new avenue for subsequent research on early warning of hydraulic turbine failure

Assessment of sleep disturbances with the athlete sleep screening Questionnaire in Chinese athletes

This study investigated the factors that are associated with sleep disturbances among Chinese athletes. Sleep quality and associated factors were assessed by the Athlete Sleep Screening Questionnaire (ASSQ, n ​= ​394, aged 18–32 years, 47.6% female). Sleep difficulty score (SDS) and level of sleep problem (none, mild, moderate, or severe) were used to classify participants' sleep quality. Categorical variables were analyzed by Chi-square or fisher's exact tests. An ordinal logistic regression analysis was used to explore factors with poor sleep (SDS ≥8). Approximately 14.2% of participants had moderate to severe sleep problem (SDS ≥8). Fifty-nine percent of the athletes reported sleep disturbance during travel, while 43.3% experienced daytime dysfunction when travelling for competition. No significant difference was found in the SDS category between gender, sports level and events. Athletes with evening chronotype were more likely to report worse sleep than athletes with morning and intermediate chronotype (OR, 2.25; 95%CI, 1.44–3.52; p ​< ​0.001). For each additional year of age, there was an increase of odds ratio for poor sleep quality (OR, 1.15; 95%CI, 1.04–1.26; p ​= ​0.004), while each additional year of training reduced the odds ratio (OR, 0.95; 95%CI, 0.91–0.99; p ​= ​0.044). To improve sleep health in athletes, chronotype, travel-related issues, age and years of training should be taken into consideration.peerReviewe

Smart phase transformation system based on lyotropic liquid crystalline@hard capsules for sustained release of hydrophilic and hydrophobic drugs

Smart phase transformation systems@hard capsule (SPTS@hard capsule) based on lyotropic liquid crystalline (LLC) were developed for oral sustained release in this study. Doxycycline hydrochloride (DOXY) and meloxicam (MLX) were used as hydrophilic and hydrophobic model drug, respectively. Two systems were added with different additives, that is, gelucire 39/01, PEG 1000 and Tween 80 to adjust their melting point and release profiles. The phase transformation of these systems could be triggered by water as well as temperature. They could spontaneously transform into cubic phase or hexagonal phase when coming across with water, to achieve the 24 h sustained release profile. In addition, the obtained systems could switch between semisolid state and liquid state when temperature changed within room temperature and body temperature, which facilitated the phase transformation in gastrointestinal tract and during their encapsulation into hard capsules. LLC-based SPTS@hard capsule revealed potential for the industrialization of its oral administration on account of its drugs accommodation with different solubility, controllable release profile and simple preparation process