Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques

A Non-MCMC Procedure for Fitting Dirichlet Process Mixture Models

Cluster analysis is concerned with partitioning cases into clusters such that the cases in a cluster are similar in terms of a set of variables. The K-Means Clustering Algorithm is a popular clustering method. It finds k clusters by choosing k data points as initial cluster centroids. Each data point is then assigned to the cluster with center that is closest to that point. In K-Means, the number of clusters has to be supplied in advance, which may be difficult in practice. A new method, the X-Means Clustering Algorithm, was proposed to solve this problem, which starts with an initial partition, then recursively runs a local K-Means in each cluster to split it until a lower Bayesian Information Criterion (BIC) value is reached compared with the previous larger cluster. However, this method would introduce a more severe local mode problem, that is, the previous inappropriate partition of cases cannot be corrected in the following local splitting. In this work, we develop a new algorithm that is based on Bayesian Dirichlet process mixture models, called the Non-MCMC DPM clustering algorithm. In the new clustering algorithm, we run the EM algorithm with all the cases to find a tentative partition, and then decide whether to accept the new partion with a criterion called DPC. We have tested our new clustering algorithm based on several simulated data sets, and found that it performs better than X-Means. We have also applied the algorithm to a real micorarray sample data set for predicting the class label (cancer or normal) based on the clustering results found by our new algorithm, and found that the prediction performance is comparable to state-of-the-art methods

An improved image segmentation algorithm for salient object detection

Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection

Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes

The conversion of other forms of energy into mechanical work through the geometrical extension and retraction of nanomaterials has a wide variety of potential applications, including for mimicking biomotors. Here, using molecular dynamic simulations, we demonstrate that there exists an intrinsic energy conversion mechanism between thermal energy and mechanical work in the telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can inherently convert heat into mechanical work in its telescopic extension process, while convert mechanical energy into heat in its telescopic retraction process. These two processes are thermodynamically reversible. The underlying mechanism for this reversibility is that the entropy changes with the telescopic overlapping length of concentric individual tubes. We find also that the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a result, the spontaneously telescopic motion of a condensed DWCNT can be switched to extrusion by rising the system temperature above a critical value. These findings are important for fundamentally understanding the mechanical behavior of concentric nanotubes, and may have general implications in the application of DWCNTs as linear motors in nanodevices

Practice Study on Operation Evaluation and Limitation for Merchant Ships in Polar Water

All environmental hazards impact safety for polar ships, especially polar merchant ship with light ice-class. In order to provide a systematic guidance to deal with any situation during polar operation, International Maritime Organization (IMO) raised mandatory requirements of “Polar Water Operation Manual”(PWOM) in Polar Code. This paper focuses on how to determinate operational evaluation and limitation for the PWOM, which is an important measure to avoid polar ships exceeding operational capability. Features of polar navigation are summarized based on the former polar navigation experience, and typical risk model is set up to describe the process of operation evaluation. The operational limitation is analyzed to indicate the actual capability and limitation as the ship encounters unexpected incident in polar waters. In conclusion, the operation procedure is studied to give a detailed technical proposals for the whole polar operation, which is the main component of PWOM. The outcome may provide helpful to arctic shipping of merchant ships

Cripto-1 overexpression is involved in the tumorigenesis of nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Human Cripto-1, a member of the EGF-CFC family, is indispensable for early embryonic development. Cripto-1 plays an important oncogenic role during tumorigenesis and is overexpressed in a wide range of epithelial carcinomas, yet little is known about Cripto-1 in nasopharyngeal carcinoma (NPC). The aim of this study was to analyze the roles of Cripto-1 in the progression and clinical characteristics in NPC clinical samples and cell lines.</p> <p>Methods</p> <p>The expression of Cripto-1 at mRNA level was detected by the reverse transcription-polymerase chain reaction (RT-PCR) and real time RT-PCR, and western blot was used to examine the protein expression. Cripto-1 expression and its clinical characteristics were investigated by performing immunohistochemical analysis on a total of 37 NPC clinical tissue samples. Lentiviral vectors were constructed to get an efficient expression of anti-Cripto-1 siRNA in CNE-2 and C666-1 cells, with invalid RNAi sequence as control. After the inhibition of the endogenous Cripto-1, the growth, cell cycle and invasion of cells were detected by MTT, FACS and Boyden chamber assay respectively. Moreover, <it>in vivo</it>, the proliferation of the tumor cells was evaluated in xenotransplant nude mice model with whole-body visualizing instrument.</p> <p>Results</p> <p>The results of real-time RT-PCR and western blot showed that the expression level of Cripto-1 was markedly higher in NPC cell lines than that in the immortalized nasopharyngeal epithelial cell at both mRNA and protein levels. RT-PCR of 17 NPC tissues showed a high expression rate in 76.5% (13/17) cases. In an immunohistochemical study, Cripto-1 was found to express in 54.1% (20/37) cases of NPC. In addition, Cripto-1 overexpression was significantly associated with N classification (<it>p </it>= 0.034), distant metastasis (<it>p </it>= 0.036), and clinical stage (<it>p = </it>0.007). Inhibition of endogenous Cripto-1 by lentivirus-mediated RNAi silencing technique suppressed NPC cell growth and invasion <it>in vitro</it>. <it>In vivo</it>, the average weight (<it>p </it>= 0.026) and volume (<it>p </it>= 0.044) of tumor in CNE-2/GFP⁺/Cripto-1^-xenotransplant mice group were significantly lower than those in the control group. The Ki67 index was obviously lower in Cripto-1 RNAi treated tumors (<it>p </it>< 0.01).</p> <p>Conclusion</p> <p>Data of this study suggest that Cripto-1 overexpression is connected with the tumorigenesis and progression of NPC, lentivector-mediated RNAi might be feasible for the inhibition of the growth and invasion of NPC.</p

Oscillation of mineral compositions in Core SG-1b, western Qaidam Basin, NE Tibetan Plateau

Uplift of the Tibetan Plateau since the Late Miocene has greatly affected the nature of sediments deposited in the Qaidam Basin. However, due to the scarcity of continuously dated sediment records, we know little about how minerals responded to this uplift. In order to understand this response, we here present results from the high-resolution mineral profile from a borehole (7.3–1.6 Ma) in the Basin, which shows systematic oscillations of various evaporite and clay minerals that can be linked to the variation of regional climate and tectonic history. In particular, x-ray diffraction (XRD) analyses show that carbonate minerals consist mainly of calcite and aragonite, with minor ankerite and dolomite. Evaporates consist of gypsum, celesite and halite. Clay minerals are principally Fe-Mg illite, mixed layers of illite/smectite and chlorite, with minor kaolinite and smectite. Following implications can be drawn from the oscillations of these minerals phases: (a) the paleolake was brackish with high salinity after 7.3 Ma, while an abrupt change in the chemical composition of paleolake water (e.g. Mg/Ca ratio, SO4 2− concentration, salinity) occurred at 3.3 Ma; (b) the three changes at ~6.0 Ma, 4.5–4.1 Ma and 3.3 Ma were in response to rapid erosions/uplift of the basin; (c) pore water or fluid was Fe/Mg-rich in 7.3–6.0 Ma, Mg-rich in 6.0–4.5 Ma, and K-rich in 4.1–1.6 Ma; and (d) evaporation rates were high, but weaker than today’s