A Universal Calibration Device for an Air Flow Sensor of the VAV Terminal Unit.

In this paper, a new calibration device for an air flow sensor of the VAV terminal unit is designed. Multi-aperture air outlets are designed to meet the calibration requirements of the air flow sensor in a variety of measurement range. The device can calibrate the air flow sensors of different types of VAV terminal unit by a movable flow rectifier without repeating the design of a different calibration pipeline. The Raspberry PI is used to design the high-performance GUI interface and controlling algorithm to achieve a one-button intelligent calibration. The air flow sensors in three different types of VAV terminal units are used to calibrate the experiment. After testing, the differential pressure value measured by the air flow sensor can accurately measure the air flow within the accuracy of 5% after the formula conversion. The conversion from differential pressure values to air flow values requires precise calibration in order to establish an accurate air flow equation, and here the calibration device plays a key role. The negative effect caused by the distance between the flow rectifiers and the VAV terminal unit is discovered. In other words, the distance between the inlet flow rectifier and the air inlet of VAV terminal unit should be kept as close as possible, or within a range of 2~3 cm. Moreover, the distance between the air outlet of VAV terminal unit and the middle-flow rectifier should be kept as close as possible; otherwise, any slight gap will cause a huge error in the calibration result. The research contributes to the further study of airflow sensing technology through the conversion and calibration of differential pressure measurements to accurate air flow values.

A novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in the indoor environment.

Ventilation is of critical importance to containing COVID-19 contagion in indoor environments. Keeping the ventilation rate at high level is recommended by many guidelines to dilute virus-laden respiratory particles and mitigate airborne transmission risk. However, high ventilation rate will cause high energy use. Demand-controlled ventilation is a promising technology option for controlling indoor air quality in an energy-efficient manner. This paper proposes a novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in indoor environments. First, the quantitative relationship is established between COVID-19 infection risk and average CO2 level. Then, a sufficient condition is proposed to ensure COVID-19 event reproduction number is less than 1 under a conservative consideration of the number of infectors. Finally, a ventilation control scheme is designed to make sure the above condition can be satisfied. Case studies of different indoor environments have been conducted on a testbed of a real ventilation system to validate the effectiveness of the proposed strategy. Results show that the proposed strategy can efficiently maintain the reproduction number less than 1 to limit COVID-19 contagion while saving about 30%-50% of energy compared with the fixed ventilation scheme. The proposed strategy offers more practical values compared with existing studies: it is applicable to scenarios where there are multiple infectors, and the number of infectors varies with time; it only requires CO2 sensors and does not require occupancy detection sensors. Since CO2 sensors are very mature and low-cost, the proposed strategy is suitable for mass deployment in most existing ventilation systems.

An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks.

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system's historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

Brain-derived neurotrophic factor protects against acrylamide-induced neuronal and synaptic injury via the TrkB-MAPK-Erk1/2 pathway.

Acrylamide has been shown to be neurotoxic. Brain-derived neurotrophic factor (BDNF) can alleviate acrylamide-induced synaptic injury; however, the underlying mechanism remains unclear. In this study, dibutyryl-cyclic adenosine monophosphate-induced mature human neuroblastoma (NB-1) cells were exposed with 0-100 µg/mL acrylamide for 24-72 hours. Acrylamide decreased cell viability and destroyed synapses. Exposure of co-cultured NB-1 cells and Schwann cells to 0-100 µg/mL acrylamide for 48 hours resulted in upregulated expression of synapsin I and BDNF, suggesting that Schwann cells can activate self-protection of neurons. Under co-culture conditions, activation of the downstream TrkB-MAPK-Erk1/2 pathway strengthened the protective effect. Exogenous BDNF can increase expression of TrkB, Erk1/2, and synapsin I, while exogenous BDNF or the TrkB inhibitor K252a could inhibit these changes. Taken together, Schwann cells may act through the BDNF-TrkB-MAPK-Erk1/2 signaling pathway, indicating that BDNF plays an important role in this process. Therefore, exogenous BDNF may be an effective treatment strategy for acrylamide-induced nerve injury. This study was approved by the Laboratory Animal Welfare and Ethics Committee of the National Institute of Occupational Health and Poison Control, a division of the Chinese Center for Disease Control and Prevention (approval No. EAWE-2017-008) on May 29, 2017.

The cyclopeptide -amatoxin induced hepatic injury via the mitochondrial apoptotic pathway associated with oxidative stress.

In order to explore the role of apoptosis in alpha-amatoxin (α-AMA) induced liver injury and probable upstream activation signals, we established animal and cellular models, respectively, for this pathophysiological condition. To this end, we evaluated the survival rate and serum biochemical parameters in BALB/c mice exposed to α-AMA at different time periods, along with the levels of oxidative and antioxidant enzymes in the liver tissue of these mice and proteins involved in apoptosis-related pathways. Our results reveal that α-AMA-induced apoptosis occurs primarily through the mitochondrial apoptotic pathway and is associated with oxidative damage. Further, in order to verify the key nodes and important upstream activators in this apoptotic pathway, we estimated the levels of p53 protein and downstream mitochondrial apoptotic pathway-related proteins in L-02 cells, all of which were found to change significantly. We also found that the levels of total and mitochondrial reactive oxygen species (ROS) in L-02 cells increased with time. Collectively, our findings suggest that α-AMA affects many cellular processes, including the expression of p53 independent of transcription and the expression of Bax and Bcl-2, thereby activating the subsequent caspase cascade pathways. In addition, we identified ROS to be an upstream signaling molecule involved in the α-AMA-induced apoptosis of mouse liver cells and L-02 cells.

Computer-generated volume holograms fabricated by femtosecond laser micromachining.

We define computer-generated volume holograms (CGVHs) as arbitrary 3D refractive index modulations designed to perform optical functions based on diffraction, scattering, and interference phenomena. CGVHs can differ dramatically from classical volume holograms in terms of coding possibilities, and from thin computer-generated holograms in terms of efficiency and selectivity. We propose an encoding technique for designing such holograms and demonstrate the concept by scanning focused femtosecond laser pulses to produce localized refractive index modifications in glass. These CGVHs show a significant increase in efficiency with thickness. Consequently, they are attractive for photonic integration with free-space and guided-wave devices, as well as for encoding spatial and temporal information.

Polarization selective computer-generated holograms realized in glass by femtosecond laser induced nanogratings.

We demonstrate polarization selective computer-generated holograms (PSCGH) for visible light operation fabricated in glass by a femtosecond laser. For this purpose we create arrays of tailored microwaveplates by controlling the laser formation of nanogratings embedded in fused silica. A birefringent cell-oriented encoding method adapted to the characteristics of the physical writing process is proposed and implemented. According to this method, each cell contains a micro-waveplate with controlled phase retardation and orientation. A detour of each microwaveplate, combined with the orientation of its principal optical axis, simultaneously realizes a different phase function for each polarization. PSCGH's are attractive for integration with other free-space and guided-wave devices embedded in glass.

Auto-tuning of PID controller parameters with supervised receding horizon optimization.

In this paper, a novel two-layer online auto-tuning algorithm is presented for a nonlinear time-varying system. The lower layer consists of a conventional proportional-integral-derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. The purpose of the upper layer is to find a set of optimal PID parameters for the lower layer via an online receding horizon optimization approach, which result in a time-varying PID controller. Through mathematical analysis, the proposed system performance is equivalent to that of a standard generalized predictive control. Simulation and experiment demonstrate that the new method has a better control system performance compared with conventional PID controllers.

Cascade generalized predictive control strategy for boiler drum level.

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.

Multi-model direct adaptive decoupling control with application to the wind tunnel system.

In this paper, a new multi-model direct adaptive decoupling controller is presented for multivariable processes, which includes multiple fixed optimal controllers, one free-running adaptive controller, and one re-initialized adaptive controller. The fixed controllers provide initial control to the process if its model lies in the corresponding region. For each controller selected, the re-initialized adaptive controller uses the values of this particular controller to improve the adaptation speed. This controller may replace the fixed controller at a later stage according to the switching criterion which is to select the best one among all controllers. A free-running adaptive controller is also added to guarantee the overall system stability. Different from the multiple models adaptive control structure proposed in Narendra, Balakrishnan, and Ciliz [Adaptation and learning using multiple models, switching, and tuning. IEEE Control Syst. Mag. 15, 37-51 (1995)], the method not only is applicable to the multi-input multi-output processes but also identifies the decoupling controller parameters directly, which reduces both the computational burden and the chances of a singular matrix during the process of determining controller parameters. Several examples for a wind tunnel process are given to demonstrate the effectiveness and practicality of the proposed method.

Auto-tuning of cascade control systems.

In this paper, a novel auto-tuning method for a cascade control system is proposed. By employing a simple relay feedback test, both inner and outer loop model parameters can be simultaneously identified. Consequently, well-established proportional-integral-derivative (PID) tuning rules can be applied to tune both loops. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.