Image Series Prediction Via Convolutional Recurrent Neural Networks With Limited Training Data

This thesis focuses on developing deep learning algorithms that can be used to forecast the image series under limited training data. Specifically, we study the problem of using a pine tree\u27s existing appearance images to predict its future appearance images

Reduced expression of IA channels is associated with post-ischemic seizures

Purpose Post-stroke seizures are considered as a major cause of epilepsy in adults. The pathophysiologic mechanisms resulting in post-stroke seizures are not fully understood. The present study attempted to reveal a new mechanism underlying neuronal hyperexcitability responsible to the seizure development after ischemic stroke. Methods Transient global ischemia was produced in adult Wistar rats using the 4-vessel occlusion (4-VO) method. The spontaneous behavioral seizures were defined by the Racine scale III – V. The neuronal death in the brain was determined by hematoxylineosin staining. The expression levels of A-type potassium channels were analyzed by immunohistochemical staining and western blotting. Results We found that the incidence of spontaneous behavioral seizures increased according to the severity of ischemia with 0% after 15-min ischemia and ~50% after 25- min ischemia. All behavioral seizures occurred with 48 hrs after ischemia. Morphological analysis indicated that brain damage was not correlated with behavioral seizures. Immunohistochemical staining showed that the expression levels of the A-type potassium channel subunit Kv4.2 was significantly reduced in ischemic brains with behavioral seizures, but not in ischemic brains without seizures. In addition, rats failing to develop spontaneous behavioral seizures within 2 days after ischemia were more sensitive to bicuculline-induced seizures at 2 months after ischemia than control rats. Meanwhile, Kv4.2 expression was decreased in brain at 2 months after ischemia. Conclusion Our results demonstrated the reduction of Kv4.2 expression might contribute to the development of post-ischemic seizures and long-term increased seizure susceptibility after ischemia. The mechanisms underlying post-stroke seizures and epilepsy is unknown so far. The down-regulation of IA channels may explained the abnormal neuronal hyperexcitability responsible for the seizure development after ischemic stroke

Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads

With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small & medium sized pedestrians and achieves state-of-the-art results in these challenging datasets.Comment: Accepted at AJCAI 202

Synthesis and characterization of aligned ZnO/BeO core/shell nanocable arrays on glass substrate

By sequential hydrothermal growth of ZnO nanowire arrays and thermal evaporation of Be, large-scale vertically aligned ZnO/BeO core/shell nanocable arrays on glass substrate have been successfully synthesized without further heat treatment. Detailed characterizations on the sample morphologies, compositions, and microstructures were systematically carried out, which results disclose the growth behaviors of the ZnO/BeO nanocable. Furthermore, incorporation of BeO shell onto ZnO core resulted in distinct improvement of optical properties of ZnO nanowire, i.e., significant enhancement of near band edge (NBE) emission as well as effective suppression of defects emission in ZnO. In particular, the NBE emission of nanocable sample shows a noticeable blue-shift compared with that of pristine ZnO nanowire, which characteristics most likely originate from Be alloying into ZnO. Consequently, the integration of ZnO and BeO into nanoscale heterostructure could bring up new opportunities in developing ZnO-based device for application in deep ultraviolet region

Multi-stage sizing approach for development of utility-scale BESS considering dynamic growth of distributed photovoltaic connection

The battery energy storage system (BESS) is regarded as one of the most promising address operational challenges caused by distributed generations. This paper proposes a novel multi-stage sizing model for utility-scale BESS, to optimize the BESS development strategies for distribution networks with increasing penetration levels and growth patterns of dispersed photovoltaic (PV) panels. Particularly, an integrated model is established in order to accommodate dispersed PVs in short-term operation scale while facilitating appropriate profits in long-term planning scale. Clusterwise reduction is adopted to extract the most representative operating scenarios with PVs and BESS integration, which is able to decrease the computing complexity caused by scenario redundancy. The numerical studies on IEEE 69-bus distribution system verify the feasibility of the proposed multi-stage sizing approach for the utility-scale BESS.</p

NUMERICAL SIMULATION OF ICE MILLING LOADS ON PROPELLER BLADE WITH COHESIVE ELEMENT METHOD

In ice-infested waters, propellers of a polar ship are likely to be exposed to ice loads in different scenarios. Propeller milling with ice is one of the most dangerous cases for ice-propeller interaction. In this study, we try to simulate dynamic milling process of ice-propeller and reproduce resulting physical phenomena. Cohesive element method is used to model ice in the simulation. To simulate material properties of ice, an elastoplastic softening constitutive law is developed. Both crushing and fracture failures are included in the ice-propeller milling process. The ice loads in 6 Dofs acting on blades of a propeller are calculated in time domain. The average and standard deviations of simulated dominant ice loads are compared with those from model test. A good agreement is achieved. By varying propeller rotation speed, advance velocity and cutting depth on ice block, the sensitivity study has been carried out. The results show that dominant ice loads are affected much by the three parameters. It is shown that decreasing rotation speed, or increasing advance velocity and cutting depth may lead to higher ice loads. Care should be taken to avoid over-loading on propeller when operating in ice for polar ship

Synthesis and nuclear magnetic resonance shielding effect of three triazine-linked porphyrin compounds

Three porphyrin compounds, monomer Por I, dimer Por II and trimer Por III have been synthesized from the reaction of cyanuric chloride, 5-(4-hydroxyphenyl)-10,15,20-triphenyl porphyrin and 1-phenylpiperazine. The properties of these novel porphyrins in nuclear magnetic shielding, the shift, coupling and splitting of beta hydrogens, were studied by ¹H NMR spectroscopy. The results shown that beta hydrogens in dimer and trimer were shielded by porphyrin ring greatly and displayed the complicated signal peaks