Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu

MetaViewer: Towards A Unified Multi-View Representation

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in features potentially degrade the quality of the derived representation. To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner. Specifically, we train a meta-learner, namely MetaViewer, to learn fusion and model the view-shared meta representation in outer-level optimization. Start with this meta representation, view-specific base-learners are then required to rapidly reconstruct the corresponding view in inner-level. MetaViewer eventually updates by observing reconstruction processes from uniform to specific over all views, and learns an optimal fusion scheme that separates and filters out view-private information. Extensive experimental results in downstream tasks such as classification and clustering demonstrate the effectiveness of our method.Comment: 8 pages, 5 figures, conferenc

Multi-objective optimization strategy for the distribution network with distributed photovoltaic and energy storage

The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate

Study on overburden damage and prevention of runoff disaster in multiseam mining of gully region

Multi-seam mining in gully region has resulted in serious and complex chain disasters, including fissure development, mountain landslides, river blockage, and intensified water inflow. To prevent and control landslides and water inrush disasters, it is crucial to explore the characteristics and laws of overlying strata failure under the coupling effect of gully terrain and repeated mining in coal seams. This study focuses on the mining of multiseam in the gully terrain of Xiqu Coal Mine. The comprehensive analysis method, integrating surface exploration, InSAR dynamic observation, rainfall-runoff analysis, and numerical simulation, is used to analyze the entire process of spatial expansion of overlying strata failure and surface subsidence caused by downward mining of multiseam in the gully region. The results reveal that after the critical mining of the lower coal seam in the gully region, the lower strata beneath the key stratum in interlayered formations are prone to develop cutting failure and vertical fissure, with tensile cracking being the dominant mode of failure. The proportion of shear fractures in the overburden above the key stratum increases significantly, and the gully slope is prone to shear slip under the effects of mining subsidence and gravity. The connection phenomenon between the downward fractures of the slope and the upward fractures of the overburden can even occur. In addition, if the accumulation formed by mountain landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, there is a risk of underground water inflow. To prevent such disasters, high-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time percentage of different durations in Fanshigou watershed during the “100-year return period” rainstorm in Shanxi Province. The research proposes a comprehensive prevention and control method of surface runoff water disaster based on fissure development and surface inundation range, which provides support for gully water disaster prevention and risk assessment in Fanshigou small watershed. This study can serve as a useful reference for the prevention and control of surface geological disasters and the protection of water resources under the condition of multiseam mining in gully regions

Polymer Nanoparticle-Based Chemotherapy for Spinal Malignancies

Malignant spinal tumors, categorized into primary and metastatic ones, are one of the most serious diseases due to their high morbidity and mortality rates. Common primary spinal tumors include chordoma, chondrosarcoma, osteosarcoma, Ewing’s sarcoma, and multiple myeloma. Spinal malignancies are not only locally invasive and destructive to adjacent structures, such as bone, neural, and vascular structures, but also disruptive to distant organs (e.g., lung). Current treatments for spinal malignancies, including wide resection, radiotherapy, and chemotherapy, have made significant progress like improving patients’ quality of life. Among them, chemotherapy plays an important role, but its potential for clinical application is limited by severe side effects and drug resistance. To ameliorate the current situation, various polymer nanoparticles have been developed as promising excipients to facilitate the effective treatment of spinal malignancies by utilizing their potent advantages, for example, targeting, stimuli response, and synergetic effect. This review overviews the development of polymer nanoparticles for antineoplastic delivery in the treatment of spinal malignancies and discusses future prospects of polymer nanoparticle-based treatment methods

JIEYUANSHEN DECOCTION EXERTS ANTIDEPRESSANT EFFECTS ON DEPRESSIVE RAT MODEL VIA REGULATING HPA AXIS AND THE LEVEL OF AMINO ACIDS NEUROTRANSMITTER

Background: Jieyuanshen decoction (JYAS-D) - a traditional Chinese medicine was invented by Professor Nie based on classic formulas, chaihu jia longgu muli decoction has been proved as having favorable curative effects on depression in clinical practices. The aim of this study was to investigate the antidepressant effects and its molecular mechanism of JYAS-D. Materials and Methods: The model of depression was established by Chronic Unpredictable Stress. Different doses (8.2 g/kg, 16.3 g/kg, 32.7 g/kg) of JYAS-D was orally administered; Fluoxetine was orally administered with 10mg/kg. All treatments lasted for 28 days. Sucrose preference and open-field tests were adopted to observe the behavior of rats. OPA (ortho-phthalaldehyde) derivatization method was used to detect the contents of amino acid neurotransmitter. RIA (Radiation immunity analysis) method was used to measure the serum concentrations of CORT (Corticosterone), ACTH (Adrenocorticotropic hormone) and CRH (Corticotropin-releasing hormone). ELISA (Enzyme linked immunosorbent assay) method was adopted to examine the contents of Glucocorticoid receptor (GR) and Mineralocorticoid receptor (MR) in hippocampus. Results: Compared with the model group, sucrose preference was increased in all treatment groups. The concentration of serum CORT was reduced in the middle dose of JYAS-D and control groups; the concentration of serum ACTH was reduced in the low and high-dose of JYAS-D; the concentration of serum CRH was reduced in the middle and high-dose of JYAS-D. The content of hippocampus GR was increased in the middle and high-dose of JYAS-D; the content of hippocampus Glu (Glutamic acid) was reduced among the low, middle and high-dose of JYAS-D and fluoxetine group, the ratio of Glu/γ-GABA (γ-aminobutyric acid was reduced in the low and high-dose of JYAS-D. Conclusion: JYAS-D had a significant antidepressant-like effect on rat model through regulating serum concentration of CORT, ACTH and CRH, increasing the content of hippocampus GR and regulating the equilibrium of amino acids neurotransmitter

Contrast-enhanced transesophageal echocardiography predicts neo-intimal coverage of device post-left atrial appendage closure

Background: Left atrial appendage (LAA) closure (LAAC) is a viable alternative to anticoagulation for stroke prevention in non-valvular atrial fibrillation. However, device-associated thrombosis (DAT) is known as a complication of LAAC as observed within the first few weeks after implantation. A noninvasive method is needed to predict the progress for endothelialization surveillance. The aim of the study was to develop a noninvasive visual contrast-enhanced transesophageal echocardiography (cTEE) method for monitoring the communication between left atrium (LA) and LAA post-LAAC by cTEE-score evaluating the contrast enhancement in LAA. Methods: A total of 29 healthy dogs were studied by LAAC at < 24 h and 1, 2, 3 and 6-months. The LAAC procedure was assessed by TEE with color Doppler flow imaging (CDFI) and contrast imaging. The cTEE score was calculated based on the differential contrast opacification of LA and LAA cavities, the CDFI on the width of peri-device color flow, and that of histology on the level of occluder surface endothelialization in postmortem histological examination. Spearman’s correlation analysis was used to correlate these scores. Results: The correlation between cTEE and histology scores was superior to that between CDFI and histology scores. The trend of average cTEE score was tracked with that of histology, while that of CDFI was far from that of histology. The correlation coefficient of CDFI and histology scores was not significant (p > 0.05). Conclusions: The noninvasive visual cTEE is feasible and reliable to monitor communication between the LA and LAA post-LAAC. cTEE is superior to CDFI as a tool in predicting the progress for endothelialization surveillance