Search

Despite being a promising biodegradable material, polylactic acid (PLA) still has several limitations mainly concerning its poor thermal resistance and brittleness. In addition, the high temperature during processing can lead to the deformation of PLA and thus reduce its performance of PLA. To overcome the above problems, L-Lactide functionalized cellulose nanocrystals enhanced PLA (PLA/CNC-g-PLA) were successfully prepared by electrospinning technology and the morphology, hydrophilic, thermal, and mechanical properties of the nanocomposite membranes with different ratios of CNC-g-PLA (1–10%) were characterized. The results demonstrated that the introduction of L-Lactide improved the interfacial compatibility between PLA and CNC. In addition, the mechanical properties and thermal stability of the composite fiber membranes were enhanced by improving the molecular chain orientation through electrospinning. The tensile strength and elongation at break of the composite fiber film increased by 2.4 and 8.2 times, and T0 and Tmax increased by 17 and 38 °C, respectively, when the addition of CNC-g-PLA was 5%. This promising nanocomposite with good properties is expected to be used in tissue engineering and food packaging applications.

Because of the obvious deformation difference for the alkali resistant glass tubes with different internal stress, in this paper, a new method for evaluating the internal stress and annealing quality of alkali resistant glass tubes based on digital image correlation algorithm is proposed. According to the deformation change rate of alkali resistant glass tube that obtained by digital image correlation algorithm, it can directly determine whether the internal stress meets the production standard. The alkali resistant glass tubes which have undergone abnormal(annealing time less than 5 hours and annealing temperature lower than 630 ℃) and normal annealing operation were selected as samples of experimental group and control group. The samples of these two groups were heated to 200 ℃ and then cooled to room temperature. The displacement fields between deformed images and the initial image are calculated by digital image correlation algorithm and the mean values change rate of the displacement fields are regard as the evaluated basis for the internal stress and annealing quality. Since the new method has many advantages such as simple, efficient and non-contact and only two continuous pictures are required in the detection process, it has been applied in the actual industrial production testing. In addition, this method provides a new approach for the internal stress detection of other solid materials.

The energy storage system connected at the end of distribution network is widely considered to be an effective means of achieving voltage regulation and ensuring the more stable voltage operation level. This paper proposes a voltage regulation method based on deep reinforcement learning to reduce the voltage fluctuation in the distribution network with battery energy storage system (BESS). The operation of battery energy storage system is modelled as a Markov Decision Making process. Then, by embedding a deep Q-network to obtain the optimal charge and discharge strategy, the calculation problem caused by too large state space could be efficaciously solved. Furthermore, the deep Q-network will be trained by replay strategy to achieve iterative convergence. And an intelligent BESS control method that provides auxiliary service for voltage regulation is ultimately obtained. Finally, the modified IEEE33 node system with BESS is analyzed by using Matpower in the framework of TensorFlow, and the effectiveness of the proposed method is proved.

The dynamic nature of the electricity market need an efficient energy management and control system to take perfect decisions accordingly. House hold appliances is the contemporary study being adopted to improve the performance and balance the fluctuation between power system and smart home. This article proposes an intelligent home energy management system (IHEMS) incorporated with a prediction model and optimization model. To address the uncertainty of future energy load and its cost, a suitable prediction model based on Bi-directional long short Term memory (Bi-LSTM) is contributed. In collaboration with the prediction model, an optimization model based on reinforcement learning is presented to schedule the home appliances by taking optimal decisions. To validate the performance of the proposed scheme, Intensive simulation is performed with adoptable, un-adoptable and manageable loads of household appliances. The results confirm that the proposed scheme address the problem of energy management for numerous appliances, reduce the total energy consumption with total energy bill and minimize the user comfort level.

The anti-vignetting glass (AVG) is the key material for super-second and third-generation low-light image intensifiers. With the development of low-light night vision technology, the requirements of high precision and low damage are put forward to AVG. However, traditional measurement methods, such as vernier calipers, micrometers, dial indicators, etc., are all contact measurement, which will inevitably cause damage to AVG during the measurement process. They cannot meet the technical requirements for low damage. Non-contact measurement technology is a non-destructive testing method that realizes the geometric measurement of AVG by writing measurement programs and setting measurement parameters. However, due to the special structure of AVG, the non-contact measurement technology has measurement errors and cannot meet the high-precision measurement requirements. In this paper systematically analyzes the causes of errors in non-contact measurement technology by studying the characteristics of the light source, the difference in light intensity, and the way of grabbing contour edges. Through the error correction technology, the error of the non-contact measurement technology is eliminated, the AVG high-precision and low-damage non-destructive testing is realized.

Traffic congestion, collision, and long delay on a traffic signal, etc., are prominent issues in today’s transportation system. To address these issues, literature constitutes different traffic flow models to analyze road conditions. In this context, this paper proposed an optimum scheme called Traffic Signal Delay Avoidance Protocol (TSDAP) based on frequent beacon messages, which aim to estimate speed for vehicles to avoid a long wait on traffic signals. Secondly, the dynamic speed assistance to the driver is another feature of TSDAP. The dynamic speed assistance feature enabled TADAP to avoid a collision. Our simulation results show that the proposed scheme significantly outperforms in terms of speed recommendation for collision and delay avoidance.