Analysis of terrestrial laser scanning technology for structural

Monitoring displacements and deformations of anthropogenic spatial structures and objects represents\ud one of the most intricate areas in geodetic surveying. Besides the measurement technologies\ud that have been traditionally used for such tasks, terrestrial laser scanning represents another possibility\ud employing the surface-wise deformation inspection of the objects’ surfaces. The main aim of\ud the thesis is to try to provide answers whether terrestrial laser scanning can be used for monitoring\ud displacements and deformations in a long-term perspective and how this could be achieved for any\ud arbitrary surface. Furthermore, the hypothesis will be challenged with the statement that the deformation\ud inspection can be performed in the millimeter domain with this remote sensing measurement\ud technology. In order to solve the problem of a stable reference system and to assure the high quality\ud of possible position changes of point clouds, scanning is integrated with two complementary\ud surveying techniques, i.e., high quality static GNSS positioning and precise classical terrestrial surveying.\ud The methodology of such high precision monitoring approach is proposed in the thesis and\ud was tested in two case study outdoor experiments. Besides these two outdoor experiments, also indoor\ud tests were designed to evaluate the quality of the surveying equipment (laser scanning targets)\ud as well as the response of the scanner to the surface material

Analysis and Use of MapReduce for Recommender Systems

MapReduce je programski model, namenjen za razvoj skalabilnih paralelnih aplikacij za obdelavo velikih množic podatkov, izvajalno okolje, ki podpira programski model in koordinira izvajanje programov, in implementacija programskega modela in izvajalnega okolja. Cilj diplomskega dela je analizirati MapReduce in ga preizkusiti na dveh primerih priporočilnih sistemov. Cilj smo dosegli, saj smo uspeli realizirati izračun s pomočjo MapReduce na testnih primerih. Najprej smo analizirali programski model in izvajalno okolje ter primerjali tri implementacije MapReduce: Hadoop MapReduce, MongoDB in knjižnico MapReduce-MPI. Ugotovili smo, da je za realizacijo izbranih primerov priporočilnih sistemov najprimernejša implementacija Hadoop MapReduce, saj nudi toleranco za okvare in reproducira podatke, s čimer zagotavlja zanesljivost. Nato smo z uporabo navidezne naprave Cloudera QuickStart VM, ki je gruča Hadoop z enim vozliščem, realizirali izbrana primera priporočilnih sistemov.MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming model and the execution framework are analysed and three implementations for MapReduce: Hadoop MapReduce, MongoDB and MapReduce-MPI Library are compared. It is discovered that Hadoop MapReduce is the most suitable implementation for developing the selected examples of recommender systems as it provides fault tolerance and data reproduction which ensure reliability. Then the selected examples of recommender systems are developed using Cloudera QuickStart VM which is a one node Hadoop cluster

Prediction of the peak shear strength of the rock joints with artificial neural networks

With the development of computer technology, artificial neural networks are becoming increasingly useful in the field of engineering geology and geotechnics. With artificial neural networks, the geomechanical properties of rocks or their behaviour could be predicted under different stress conditions. Slope failures or underground excavations in rocks mostly occurred through joints, which are essential for the stability of geotechnical structures. This is why the peak shear strength of a rock joint is the most important parameter for a rock mass stability. Testing of the shear characteristics of joints is often time consuming and suitable specimens for testing are difficult to obtain during the research phase. The roughness of the joint surface, tensile strength and vertical load have a great influence on the peak shear strength of the rock joint. In the presented paper, the surface roughness of joints was measured with a photogrammetric scanner, and the peak shear strength was determined by the Robertson direct shear test. Based on six input characteristics of the rock joints, the artificial neural network, using a backpropagation learning algorithm, successfully learned to predict the peak shear strength of the rock joint. The trained artificial neural network predicted the peak shear strength for similar lithological and geological conditions with average estimation error of 6 %. The results of the calculation with artificial neural networks were compared with the Grasselli experimental model, which showed a higher error in comparison with the artificial neural network model

Use of Terrestrial Laser Scanning Technology for Long Term High Precision Deformation Monitoring

The paper presents a new methodology for high precision monitoring of deformations with a long term perspective using terrestrial laser scanning technology. In order to solve the problem of a stable reference system and to assure the high quality of possible position changes of point clouds, scanning is integrated with two complementary surveying techniques, i.e., high quality static GNSS positioning and precise tacheometry. The case study object where the proposed methodology was tested is a high pressure underground pipeline situated in an area which is geologically unstable

Ultrasensitive Label-Free Detection of Protein-Membrane Interaction Exemplified by Toxin-Liposome Insertion.

Measuring the high-affinity binding of proteins to liposome membranes remains a challenge. Here, we show an ultrasensitive and direct detection of protein binding to liposome membranes using high throughput second harmonic scattering (SHS). Perfringolysin O (PFO), a pore-forming toxin, with a highly membrane selective insertion into cholesterol-rich membranes is used. PFO inserts only into liposomes with a cholesterol concentration >30%. Twenty mole-percent cholesterol results in neither SHS-signal deviation nor pore formation as seen by cryo-electron microscopy of PFO and liposomes. PFO inserts into cholesterol-rich membranes of large unilamellar vesicles in an aqueous solution with Kd = (1.5 ± 0.2) × 10-12 M. Our results demonstrate a promising approach to probe protein-membrane interactions below sub-picomolar concentrations in a label-free and noninvasive manner on 3D systems. More importantly, the volume of protein sample is ultrasmall (<10 μL). These findings enable the detection of low-abundance proteins and their interaction with membranes

Evaluation of High-Precision Sensors in Structural Monitoring

One of the most intricate branches of metrology involves the monitoring of displacements and deformations of natural and anthropogenic structures under environmental forces, such as tidal or tectonic phenomena, or ground water level changes. Technological progress has changed the measurement process, and steadily increasing accuracy requirements have led to the continued development of new measuring instruments. The adoption of an appropriate measurement strategy, with proper instruments suited for the characteristics of the observed structure and its environmental conditions, is of high priority in the planning of deformation monitoring processes. This paper describes the use of precise digital inclination sensors in continuous monitoring of structural deformations. The topic is treated from two viewpoints: (i) evaluation of the performance of inclination sensors by comparing them to static and continuous GPS observations in deformation monitoring and (ii) providing a strategy for analyzing the structural deformations. The movements of two case study objects, a tall building and a geodetic monument in Istanbul, were separately monitored using dual-axes micro-radian precision inclination sensors (inclinometers) and GPS. The time series of continuous deformation observations were analyzed using the Least Squares Spectral Analysis Technique (LSSA). Overall, the inclinometers showed good performance for continuous monitoring of structural displacements, even at the sub-millimeter level. Static GPS observations remained insufficient for resolving the deformations to the sub-centimeter level due to the errors that affect GPS signals. With the accuracy advantage of inclination sensors, their use with GPS provides more detailed investigation of deformation phenomena. Using inclinometers and GPS is helpful to be able to identify the components of structural responses to the natural forces as static, quasi-static, or resonant

Analysis of terrestrial laser scanning technology for structural deformation monitoring

Spremljanje premikov in deformacij antropogenih prostorskih struktur in objektov predstavlja eno izmed najbolj zahtevnih področij v geodeziji. Poleg merskih tehnologij, ki se tradicionalno uporabljajo za izvedbo takšnih nalog, predstavlja terestrično lasersko skeniranje dodatno možnost ploskovnega načina analiziranja objektnih površin. Glavni cilj doktorske naloge je v zagotovitvi odgovorov o možnostih uporabe terestričnega laserskega skeniranja za dolgoročno spremljanje premikov in deformacij ter o načinu izvedbe takšne oblike spremljave na poljubnih objektih. Poleg tega bo v okviru naloge ovrednotena hipoteza, da lahko s pomočjo te tehnologije daljinskega zaznavanja k analizi deformacij pristopimo v območju milimetrov. Za rešitev problema stabilnega referenčnega sistema, ki pogojuje visoko kakovostno analiziranje morebitnih sprememb položajev oblakov točk, je skeniranje treba povezati z ostalimi geodetskimi tehnikami, tj. zelo natančno statično izmero GNSS in precizno klasično terestrično izmero. Naloga predlaga metodologijo takšnega zelo natančnega načina spremljanja, ki je bila preizkušena v okviru dveh testov v naravi. Poleg teh dveh testov so bili za potrebe naloge zasnovani tudi testi za preverjanje kakovosti uporabljene merske opreme (tarč laserskega skeniranja) in odzivnosti skenerja na lastnosti površinskega materiala..Monitoring displacements and deformations of anthropogenic spatial structures and objects represents one of the most intricate areas in geodetic surveying. Besides the measurement technologies that have been traditionally used for such tasks, terrestrial laser scanning represents another possibility employing the surface-wise deformation inspection of the objects’ surfaces. The main aim of the thesis is to try to provide answers whether terrestrial laser scanning can be used for monitoring displacements and deformations in a long-term perspective and how this could be achieved for any arbitrary surface. Furthermore, the hypothesis will be challenged with the statement that the deformation inspection can be performed in the millimeter domain with this remote sensing measurement technology. In order to solve the problem of a stable reference system and to assure the high quality of possible position changes of point clouds, scanning is integrated with two complementary surveying techniques, i.e., high quality static GNSS positioning and precise classical terrestrial surveying. The methodology of such high precision monitoring approach is proposed in the thesis and was tested in two case study outdoor experiments. Besides these two outdoor experiments, also indoor tests were designed to evaluate the quality of the surveying equipment (laser scanning targets) as well as the response of the scanner to the surface material

Analysis and Use of MapReduce for Recommender Systems

MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming model and the execution framework are analysed and three implementations for MapReduce: Hadoop MapReduce, MongoDB and MapReduce-MPI Library are compared. It is discovered that Hadoop MapReduce is the most suitable implementation for developing the selected examples of recommender systems as it provides fault tolerance and data reproduction which ensure reliability. Then the selected examples of recommender systems are developed using Cloudera QuickStart VM which is a one node Hadoop cluster