A novel osmosis membrane bioreactor-membrane distillation hybrid system for wastewater treatment and reuse

© 2016 . A novel approach was designed to simultaneously enhance nutrient removal and reduce membrane fouling for wastewater treatment using an attached growth biofilm (AGB) integrated with an osmosis membrane bioreactor (OsMBR) system for the first time. In this study, a highly charged organic compound (HEDTA3-) was employed as a novel draw solution in the AGB-OsMBR system to obtain a low reverse salt flux, maintain a healthy environment for the microorganisms. The AGB-OsMBR system achieved a stable water flux of 3.62 L/m2 h, high nutrient removal of 99% and less fouling during a 60-day operation. Furthermore, the high salinity of diluted draw solution could be effectively recovered by membrane distillation (MD) process with salt rejection of 99.7%. The diluted draw solution was re-concentrated to its initial status (56.1 mS/cm) at recovery of 9.8% after 6 h. The work demonstrated that novel multi-barrier systems could produce high quality potable water from impaired streams

Online support vector machine application for model based fault detection and isolation of HVAC system

Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC system

Hierarchical LSTM with adjusted temporal attention for video captioning

Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning. To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets

The classification for 'equilibrium triad' sensory loss based on sEMG signals of calf muscles

© 2017 IEEE. Surface Electromyography (sEMG) has been commonly applied for analysing the electrical activities of skeletal muscles. The sensory system of maintaining posture balance includes vision, proprioception and vestibular senses. In this work, an attempt is made to classify whether the body is missing one of the sense during balance control by using sEMG signals. A trial of combination with different features and muscles is also developed. The results demonstrate that the classification accuracy between vision loss and the normal condition is higher than the one between vestibular sense loss and normal condition. When using different features and muscles, the impact on classification results is also different. The outcomes of this study could aid the development of sEMG based classification for the function of sensory systems during human balance movement

EEG-based emotion classification using innovative features and combined SVM and HMM classifier

© 2017 IEEE. Emotion classification is one of the state-of-the-art topics in biomedical signal research, and yet a significant portion remains unknown. This paper offers a novel approach with a combined classifier to recognise human emotion states based on electroencephalogram (EEG) signal. The objective is to achieve high accuracy using the combined classifier designed, which categorises the extracted features calculated from time domain features and Discrete Wavelet Transform (DWT). Two innovative designs are involved in this project: a novel variable is established as a new feature and a combined SVM and HMM classifier is developed. The result shows that the joined features raise the accuracy by 5% on valence axis and 1.5% on arousal axis. The combined classifier can improve the accuracy by 3% comparing with SVM classifier. One of the important applications for high accuracy emotion classification system is offering a powerful tool for psychologists to diagnose emotion related mental diseases and the system developed in this project has the potential to serve such purpose

Evolution of superconductivity in K2-xFe4+ySe5: Spectroscopic studies of X-ray absorption and emission.

This study investigates the evolution of superconductivity in K2-xFe4+ySe5 using temperature-dependent X-ray absorption and resonant inelastic X-ray scattering techniques. Magnetization measurements show that polycrystalline superconducting (SC) K1.9Fe4.2Se5 has a critical temperature (T c) of ∼31 K with a varying superconducting volume fraction, which strongly depends on its synthesis temperature. An increase in Fe-structural/vacancy disorder in SC samples with more Fe atoms occupying vacant 4d sites is found to be closely related to the decrease in the spin magnetic moment of Fe. Moreover, the nearest-neighbor Fe-Se bond length in SC samples exceeds that in the non-SC (NS) sample, K2Fe4Se5, which indicates a weaker hybridization between the Fe 3d and Se 4p states in SC samples. These results clearly demonstrate the correlations among the local electronic and atomic structures and the magnetic properties of K2-xFe4+ySe5 superconductors, providing deeper insight into the electron pairing mechanisms of superconductivity

Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE

Innovative sponge-based moving bed-osmotic membrane bioreactor hybrid system using a new class of draw solution for municipal wastewater treatment

© 2016 Elsevier Ltd. For the first time, an innovative concept of combining sponge-based moving bed (SMB) and an osmotic membrane bioreactor (OsMBR), known as the SMB-OsMBR hybrid system, were investigated using Triton X-114 surfactant coupled with MgCl2 salt as the draw solution. Compared to traditional activated sludge OsMBR, the SMB-OsMBR system was able to remove more nutrients due to the thick-biofilm layer on sponge carriers. Subsequently less membrane fouling was observed during the wastewater treatment process. A water flux of 11.38 L/(m2 h) and a negligible reverse salt flux were documented when deionized water served as the feed solution and a mixture of 1.5 M MgCl2 and 1.5 mM Triton X-114 was used as the draw solution. The SMB-OsMBR hybrid system indicated that a stable water flux of 10.5 L/(m2 h) and low salt accumulation were achieved in a 90-day operation. Moreover, the nutrient removal efficiency of the proposed system was close to 100%, confirming the effectiveness of simultaneous nitrification and denitrification in the biofilm layer on sponge carriers. The overall performance of the SMB-OsMBR hybrid system using MgCl2 coupled with Triton X-114 as the draw solution demonstrates its potential application in wastewater treatment

Dynamic modelling of heart rate response under different exercise intensity.

Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise

Applicability of an integrated moving sponge biocarrier-osmotic membrane bioreactor MD system for saline wastewater treatment using highly salt-tolerant microorganisms

© 2017 Elsevier B.V. Osmotic membrane bioreactors (OsMBRs) are a recent breakthrough technology designed to treat wastewater. Nevertheless, their application in high-salinity wastewater treatment is not widespread because of the effects of saline conditions on microbial community activity. In response, this study developed an integrated sponge biocarrier-OsMBR system using highly salt-tolerant microorganisms for treating saline wastewater. Results showed that the sponge biocarrier-OsMBR obtained an average water flux of 2 L/m2 h during a 92-day operation when 1 M MgCl2 was used as the draw solution. The efficiency in removing dissolved organic compounds from the proposed system was more than 99%, and nutrient rejection was close to 100%, indicating excellent performance in simultaneous nitrification and denitrification processes in the biofilm layer on the carriers. Moreover, salt-tolerant microorganisms in the sponge biocarrier-OsMBR system worked efficiently in salt concentrations of 2.4%. A polytetrafluoroethylene MD membrane (pores = 0.45 μm) served to regenerate the diluted draw solution in the closed-loop system and produce high-quality water. The moving sponge biocarrier-OsMBR/MD hybrid system demonstrated its potential to treat salinity wastewater treatment, with 100% nutrient removal and 99.9% conductivity rejection