Bridge damage detection using an intelligent engineering system

This thesis concerns the design of an algorithm that is capable to detect structural damage in civil infrastructure bridges. The algorithm, which will be dubbed Damage Diagnostics System throughout the thesis, is the software component of a broader Bridge Health Monitoring System. This broader system integrates software and hardware,such as sensors and data acquisition components...The rationale for the Structural Damage Diagnosis is based on the principle of the structural vibration testing. The Health Monitoring System captures the vibration signals, as the bridge responds to excitation from various sources. The purpose of the Diagnostic System is to extract information from the vibration signals concerning the damage condition of the bridge. This system will identify and quantify the damage, by examining the shifts in the vibration signature. This can be performed with a comparison between the actual vibration signal and the vibration behavior of the undamaged bridge --Introduction, page 1

Effectively incorporating expert knowledge in automated software remodularisation

Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowledge, leading to results that can be nonsensical to developers. Nevertheless, suppling such knowledge is a potentially burdensome task to perform manually. A lot information may need to be specified, particularly for large systems. Addressing these concerns, we propose the SUMO (SUpervised reMOdularisation) approach. SUMO is a technique that aims to leverage a small subset of domain knowledge about a system to produce a remodularisation that will be acceptable to a developer. With SUMO, developers refine a modularisation by iteratively supplying corrections. These corrections constrain the type of remodularisation eventually required, enabling SUMO to dramatically reduce the solution space. This in turn reduces the amount of feedback the developer needs to supply. We perform a comprehensive systematic evaluation using 100 real world subject systems. Our results show that SUMO guarantees convergence on a target remodularisation with a tractable amount of user interaction

Architectural Layer Recovery for Software System Understanding and Evolution

This paper presents an approach to identify software layers for the understanding and evolution of software systems implemented with any object-oriented programming language. The approach first identifies relations between the classes of a software system and then uses a link analysis algorithm (i.e. the Kleinberg algorithm) to group them into layers. Additionally to assess the approach and the underlying techniques, the paper also presents a prototype of a supporting tool and the results from a case study

Nonparametric data segmentation in multivariate time series via joint characteristic functions

Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series

Fishing Ground Mapping Model in The Semi-Enclosed Saleh Bay, West Nusa Tenggara

Saleh Bay is a semi-enclosed area of water in Nusa Tenggara Barat Province that is enriched by fisheries resources. The bay’s strategic position, surrounded by several small islands, makes it an area of fertile water. An area of water is considered a potentially ideal fishing ground if it contains several oceanographic phenomena, including thermal fronts and upwelling. Fishing activities in Saleh Bay have been found to be ineffective and inefficient due to local people’s continued use of traditional methods such as fishing by signs of nature (instincts), wind direction, astrological signs and previous experience. This study aimed to create a mapping model of the fishing grounds in Saleh Bay based on remote sensing satellite data. Spatial analysis of daily level 3 images from the Suomi-National Polar-Orbiting Partnership (SNPP) was conducted throughout January and August 2019. The image acquisition period was adapted based on the seasonal system of Indonesia. The study area was determined based on thermal front events as identified by sea surface temperature (SST) data analysed using statistical regression with a Non-Linear Multi-Channel SST (NLSST) approach. An ideal fishing ground is characterised by several oceanographic settings such as upwelling and thermal front occurrence. The average SST distribution in January 2019 was relatively high, ranging from 30.39 to 33.70 oC, while in August 2019, the temperature declined significantly, ranging from 25.09 to 29.30 oC. Concerning the fishing ground model, a plethora of potential fishing ground areas were identified in August compared to January 2019, at 144 and 42 points respectively. This reflected the density of the fishing grounds observed. The fishing grounds were most likely to be concentrated in the bay mouth during the southwest monsoon and within the bay near the plateau during the northeast monsoon. The seasonal variability of Saleh Bay played a significant role in the spatial extraction of the fishing ground data

WiPrint: 3D Printing Your Wireless Coverage

Wireless signals are everywhere in residential, commercial and industrial environments. Directing wireless signals to conform to custom physical boundaries is of great importance in improving the performance, security and privacy of a wireless system. Unfortunately current solutions like directional antennas are bulky and expensive for ordinary users. We propose WiPrint, a novel approach to customizing wireless signal maps using 3D printed glossy reflectors. This solution is easily manufactured and adapts easily to different environments. The WiPrint system is highly flexible as it does not require adding additional APs or moving the AP to new locations. This is significant in the field of wireless networking as it provides consumers with an intuitive and novel solution to performance and security problems

Design evaluation on the production of sloping walls without support structures in additive manufacturing

Technologies have been advancing significantly over the years. Additive manufacturing is a technology that is in constant growth in the matter of applications, materials, processes and machines. In spite of its advanced technology, in most cases the produced models need to build with support structures which slows down production. Hence it is necessary to understand this type of limitation. Additive manufacturing has the ability of producing geometrical parts from a CAD model, creating rapidly physical models by joining materials, layer by layer, to represent models or even to test its functionality. It is capable of printing geometrical complex parts with an extended design freedom, but in some systems, needs to build support structures to support the part during production. The FDM technology is one of the additive manufacturing processes that produces the model by connecting polymeric materials one layer at a time. The machine software reads and manipulates the STL file to define all the proper conditions to print the required model, as well as defining the need to build support structures. It is relevant to establish design guidelines to achieve an improved result. Therefore, the focus of this thesis is to evaluate the need of support structures in a set of defined models with designated geometric characteristics. The work consisted in producing models with sloping walls using the FDM process in different machines in order to understand the different behaviours of the shapes and to conclude at which point it is possible to produce a geometric feature without support structures while maintaining geometric accuracy

Towards quantitative high-throughput 3D localization microscopy

Advances in light microscopy have allowed circumventing the diffraction barrier, once thought to be the ultimate resolution limit in optical microscopy, and given rise to various superresolution microscopy techniques. Among them, localization microscopy exploits the blinking of fluorescent molecules to precisely pinpoint the positions of many emitters individually, and subsequently reconstruct a superresolved image from these positions. While localization microscopy enables the study of cellular structures and protein complexes with unprecedented details, severe technical bottlenecks still reduce the scope of possible applications. In my PhD work, I developed several technical improvements at the level of the microscope to overcome limitations related to the photophysical behaviour of fluorescent molecules, slow acquisition rates and three-dimensional imaging. I built an illumination system that achieves uniform intensity across the field-of view using a multi-mode fiber and a commercial speckle-reducer. I showed that it provides uniform photophysics within the illuminated area and is far superior to the common illumination system. It is easy to build and to add to any microscope, and thus greatly facilitates quantitative approaches in localization microscopy. Furthermore, I developed a fully automated superresolution microscope using an open-source software framework. I developed advanced electronics and user friendly software solutions to enable the design and unsupervised acquisition of complex experimental series. Optimized for long-term stability, the automated microscope is able to image hundreds to thousands of regions over the course of days to weeks. First applied in a system-wide study of clathrin-mediated endocytosis in yeast, the automated microscope allowed the collection of a data set of a size and scope unprecedented in localization microscopy. Finally, I established a fundamentally new approach to obtain three-dimensional superresolution images. Supercritical angle localization microscopy (SALM) exploits the phenomenon of surface-generated fluorescence arising from fluorophores close to the coverslip. SALM has the theoretical prospect of an isotropic spatial resolution with simple instrumentation. Following a first proof-of-concept implementation, I re-engineered the microscope to include adaptive optics in order to reach the full potential of the method. Taken together, I established simple, yet powerful, solutions for three fundamental technical limitations in localization microscopy regarding illumination, throughput and resolution. All of them can be combined within the same instrument, and can dramatically improve every cutting-edge microscope. This will help to push the limit of the most challenging applications of localization microscopy, including system-wide imaging experiments and structural studies