Powerlines are important in the process of electricity transmission and distribution (T & D) and their essential role in transmitting electricity from the large generating stations to the final consumers cannot be over emphasized. Over the years, an increase in the demand for electrical energy (electricity) has led to the construction and inevitable use of high transmission voltage, sub-transmission voltage and distribution voltage power conducting lines, for the electricity T & D process. Along with this essential role, electricity conductors can also give rise to some electrically related effects such as interference with telecommunication circuits, electric shocks, electromagnetic fields, audible noise, corona ion discharges, etc. The presence of powerline generated corona ions in any ambient air environment can be associated with the local modification of the earth’s natural dc electric field (e-field), while the interactions between these ions and other airborne aerosol particles can be associated with the presence of charged aerosol particles in the environment of the corona ion emitting lines. When considering all the studies conducted to date on the possible direct and indirect effects of high voltage powerlines (HVPLs), of significant interest are those suggesting links between powerlines and some adverse human health effects – with such health effects alleged to be strongest amongst populations directly exposed to HVPLs. However, despite the numerous studies conducted on HVPLs, to date a lack of proper scientific understanding still exist in terms of the physical characterization of the electrical environment surrounding real-world HVPLs - mostly in terms of the entire dynamics of ions and charged particles, as well as the possible links/associations between the different parameters that characterize these electrical environments. Yet, gaining a sound understanding about the electrical environment surrounding energized real-world HVPLs is imperative for the accurate assessment of any possible human exposure or health effects that may be associated with powerlines. The research work presented in this thesis was motivated by the existing gaps in scientific understanding of the possible association between corona ions generated by real-world HVPLs and the production of ambient charged aerosol particles. The aim of this study was to supply some much needed scientific knowledge about the characteristics of the electrical environment surrounding real-world energized HVPLs. This was achieved by investigating the possible effects of corona ions generated by real-world overhead HVPLs on ambient aerosol particle number concentration level, ambient aerosol particle charge concentration level, ambient ion concentration level and the magnitude of the local vertical dc e-field; while also taking into consideration the possible effect of complex meteorological factors (such as temperature, pressure, wind speed wind direction, solar radiation and humidity) on the instantaneous value of these measured parameters, at different powerline sites. The existence of possible associations or links between these various parameters measured in the proximity of the powerlines was statistically investigated using simple linear regression, correlation and multivariate (principal component, factor, classification and regression tree-CART) analysis. The strength of the regression was tested with coefficient of determinations R2, while statistical significance was asserted at the 95 % confidence level. For the powerline sites investigated in this study, both positive and negative polarities of ions were found to be present in the ambient air environment. The presence of these ions was associated with perturbations in the local vertical dc e-field, increased net ambient ion concentrations and net particle charge concentration levels. The mean net ion concentration levels (with a range of 4922 ions cm-3 to -300 ions cm-3) in the ambient environment of these powerlines, were in excess of what was measured in a typical outdoor air (i.e -400 ions cm-3). The mean net particle charge concentration levels (1469 ions cm-3 to -1100 ions cm-3) near the powerlines were also found to be statistically significantly higher than what was obtained for a mechanically ventilated indoor room (-84 ± 49 ions cm-3) and a typical urban outdoor air (-486 ± 34 ions cm-3). In spite of all these measured differences however, the study also indicated that ambient ion concentration as well as its associated effects on ambient particle charge concentration and e-field perturbations gradually decreased with increase in distance from the powerlines. This observed trend provided the physical evidence of the localized effect of real-world HVPL generated corona ions. Particle number concentration levels remained constant (in the order of 103 particles cm-3) irrespective of the powerline site or the sampling distance from the lines. A close observation of the output signals of the sampling instruments used in this study consistently revealed large fluctuations in the instantaneous value of all the measured electrical parameters (i.e. non-periodic extremely high and low negative and positive polarities of ions/charged particles and e-field perturbations was recorded). Although the reason for these observed fluctuations is not particularly known at this stage, and hence in need of further investigations, it is however being hypothesized that, since these fluctuations appear to be characteristic of the highly charged environment surrounding corona ion emitting electrical infrastructures, they may be suggestive of the possibility that the release of corona ions by ac lines are not necessarily in the form of a continuous flow of ions. The results also showed that statistically significant correlations (R2 = 74 %, P < 0.05) exists between the instantaneous values of the ground-level ambient ion and the ground-level ambient particle charge concentration. This correlation is an indication of the strong relationship/association that exists between these two parameters. Lower correlations (R2 = 3.4 % to 9 %, P < 0.05) were however found to exist between the instantaneous values of the vertical dc e-field and the ground-level ambient particle charge concentration. These suggest that e-field measurements alone may not necessarily be a true indication of the ground-level ambient ion and particle charge concentration levels. Similarly, low statistical correlations (R2 = 0.2 % to 1.0 %, P < 0.05) were also found to exist between the instantaneous values of ambient aerosol particle charge concentration and ambient ultrafine (0.02 to 1 μm sized) aerosol particle number concentration. This low level of correlations suggests that the source contribution of aerosol particle charge and aerosol particle number concentration into the ambient air environment of the HVPLs were different. In terms of the implication of human exposure to charged aerosol particles, the results obtained from this study suggests that amongst other factors, exposure to the dynamic mixture of ions and charged particles is a function of : (a) distance from the powerlines; (b) concentration of ions generated by the powerlines; and (c) meteorology - wind turbulence and dispersal rate. In addition to all its significant findings, during this research, a novel measurement approach that can be used in future studies for the simultaneous monitoring of the various parameters characterizing the physical environment of different ion/charged particle emission sources (such as high voltage powerlines, electricity substations, industrial chimney stack, motor vehicle exhaust, etc.) was developed and validated. However, in spite of these significant findings, there is still a need for other future and more comprehensive studies to be carried out on this topic in order to extend the scientific contributions of in this research work.

International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The CENTS Program is a Central Test and Evaluation Investment Program (CTEIP) effort conducted by the 46th Test Wing at Eglin Air Force Base, Florida. This project uses advanced internetworking technology to collect data unobtrusively from multiple Line Replaceable Units (LRU's) within an aircraft without the expense of running new wiring. The data is transported to a master network controller using the existing aircraft powerlines at a raw data rate of over 10 Mbits/s. Sensors are integrated into the shells of the LRU's data bus connectors to minimize the number of aircraft modifications required for a test. CENTS began in January 2000 as an OSD CTEIP Sponsored Test Technology Development and Demonstration (TTD&D) project and is currently in Phase 2 of the effort. Phase 1 saw the successful demonstration of the use of MIL-STD-704 power busses to establish a virtual network for data transport. This paper reviews the current status and past achievements of the CENTS TTD&D program as well as describing some immediate potential pay- offs for the Test and Evaluation community in the near-term.

De alltmer påtagliga klimatförändringar som sker runt om i världen ställer allt större krav på varje enskilt land att minska utsläppen av fossila bränslen. Därav jobbar både Sverige och många andra länder för de globala målen, som bland annat innebär att lösa klimatkrisen innan år 2030. För att uppnå detta mål krävs en stor omställning i samhället, varav en viktig del är att bygga ut landets elnät så att mer förnybara energikällor kan brukas. Att bygga ut elnäten är en stor utmaning som kräver bra geografisk information som kan skapa underlag för en effektiv planering. I nuläget är det dock brist på geografisk information över alla typer av ledningar, och den minimala datamängd som finns saknar viktig höjdinformation som behövs vid planering och olika typer av analyser. På så vis har denna studie utförts i syfte att försöka utveckla en djupinlärningsmodell som ska kunna klassificera främst lokala- och regionala luftledningar utifrån Lantmäteriets ”laserdata nedladdning, skog”. Det klassade punktmolnet ska sedan kunna användas för att skapa en 3D-modell över luftledningarna och omkringliggande miljö, för att bättre kunna visualisera verkligheten. För att utföra detta har de två djupinlärningsmodellerna PointCNN och RandLA-Net testats i ett område öster om Degerfors, där punkttätheten är tillräckligt hög samt att det finns både lokala- och regionala ledningar i området. Den färdigtränade modellen har sedan testats i ett nytt område i Olofström för att kontrollera hur väl generaliserad modellen blev. För att ta reda på hur lönsamt det är att ta fram en egenutvecklad djupinlärningsmodell, har den även jämförts med Esris förtränade modell. Samtliga processer har utförts i ArcGIS Pro. Resultatet visar att den framtagna modellen enligt RandLA-Net arkitekturen är något överanpassad för att kunna leverera bra klassificeringsresultat på andra områden än där modellen tränades. Det har även visat sig att den framtagna modellen inte kan ge avsevärt bättre resultat jämfört med Esris förtränade modell. Dock uppnådde ingen modell bra klassificeringsresultat, vilket innebär att en fortsatt studie med vissa förbättringar skulle behöva utföras för att kunna fortsätta arbetet framåt.  Sammanfattningsvis har studien visat att punktavståndet har en påverkande roll för hur bra klassificeringsresultat som kan uppnås. I de områden där punktätheten är tillräckligt bra och klassificeringen är korrekt, kan en tillförlitlig 3D-modell skapas. En förbättring skulle vara att använda en laserdatamängd med mer mångfald vid träning. Därmed skulle det kunna utvecklas en mer generell modell som kan leverera bra resultat över fler laserskannade områden. På så vis skulle en fortsatt studie vara relevant när Lantmäteriet utfört sin tredje laserskanning över Sverige. / The increased climate change that occurring around the world requires even more from each individual country to reduce the emissions of fossil fuels. Thus, both Sweden and many other countries are working towards the global goals, including solving the climate crisis before 2030. To achieve this goal, a major change in society is required. One major part of this is to expand the electricity network so more renewable energy sources can be used. Expanding the electricity networks is a big challenge that requires good geographical information that can create a basis for effective planning. Furthermore, there is a lack of geographic information about all types of powerlines, and the minimal amount of data that exists missing important height information that is needed for planning and different types of analyses. In this way, the purpose with this study is to develop a deep learning model that should be able to classify primarily local and regional overhead powerlines based on the Land Survey´s "Laserdata nedladdning, skog". The classified point cloud should then be used to create a 3D model of the overhead powerlines and the surrounding environment to better visualize reality. To do this, the two deep learning models PointCNN and RandLA-Net have been tested in an area east of Degerfors, where the point density is high enough. The fully trained model has then been tested in a new area in Olofström to check how well the model was generalized. In order to find out how profitable it is to develop a deep learning model, the developed model has also been compared with Esri's pre-trained model. All processes have been carried out in ArcGIS Pro. The result shows that the developed model according to the RandLA-Net architecture is somewhat over-adapted to be able to deliver good classification results in areas other than where the model was trained. The study has also shown that the developed model cannot provide significantly better results compared to Esri's pretrained model. However, no model achieved good classification results, which means that a further study with some improvements would be appropriate to continue the work forward.To summarize, the study has shown that the point distance has a great impact for the classification results. In areas where the point density is high enough and the classification is correct, a reliable 3D model can be created. An improvement would be to use a lidar dataset with more diversity in training to be able to develop a more general model that can deliver good results over more scanned areas. In this way, a continued study would be relevant when the Land Survey has performed the third scan of Sweden.

This dissertation addresses the problem of automated vector extraction from Airborne Laser Scanner (ALS) data in urban areas. The recent popularity of Geographic Information Systems (GIS) has stimulated research on automated object extraction in order to simplify the data acquisition and update process. By automatically generating GIS inputs and updates from a single data source, the cost of data acquisition and processing will be kept to a minimum. Compared with other remote sensing techniques, extraction of objects from ALS data is in its infancy. ALS sensor technology has evolved rapidly and now allows the acquisition of very dense point clouds in a short period of time. ALS data is unique in that it explicitly contains 3D information and is acquired from an active sensor. As such, there are several benefits that can be immediately realised by using an ALS only data approach; data acquisition will not be limited to daylight hours as with other sensors and accurate height information is contained in the data. This means that registration of different data sources is not required and as only one data source is used, acquisition costs are minimised. Apart from these facts, ALS data has some other unique properties that have not been utilised to their full potential. An ALS sensor can record both height and intensity information from multiple returns in a single swath. To date, all the available information has rarely been used. For example, the intensity of a laser return has regularly been dismissed as it was considered under-sampled and noisy. This data still contains usable information and until this information is used, the optimum object recognition results will not be achieved; thus the use of as much of this information as possible is a major focus of this thesis. As ALS is an explicit 3D data source, the early stages of development were primarily focused on topographic mapping of terrain in forested areas in order to generate Digital Terrain Models (DTMs). As sensor technology has improved, so has the achievable resolution of point clouds from ALS data, and methods to extract objects from stand-alone ALS data have emerged. Attempts have been made to create city models and maps from ALS data that included buildings, roads, trees and powerlines. Each of these spatial object types has unique attributes, which means that automatic map creation is not an easy task. For example, buildings in general are easily detected in ALS data but the building outline is not easily delineated. Another difficulty with building detection is the separation of buildings and bridges as they have many of the same properties as observed by an ALS system. Bridges can usually be found in a road network but road extraction techniques typically produce poor detection rates and often require existing data and / or user interaction in semi-automatic techniques. These simple examples highlight the complexity of automatically generating vectorised maps from ALS data or in fact any data source. In this thesis, new methods are presented for the automatic creation of vectorised maps from ALS data. A two-step processing paradigm is adopted for this purpose, namely the classification of the ALS data and the vectorisation of the classification results. A classification strategy is introduced that creates a hierarchy for object detection with respect to the ALS data itself. This approach develops an ontology between the spatial object classes and the ALS data. The hierarchical framework highlights the fact that one object might not be discernable within the data without considering another. New classification algorithms are then presented within this framework. Each algorithm attempts to exploit the attributes of the data that are consistent within the spatial object class being considered as described by the classification framework. New algorithms for the classification of roads, trees and powerlines are all introduced whilst an extension to existing building classification methods is presented. Once classification is complete, a vectorisation process specific to the task at hand can be employed to yield vectorised results. These developed vectorisation processes are new and include an algorithm that has been generalised to allow the vectorisation of thick lines in images by detecting the centreline, direction and width. The primary goal of this thesis is to present a framework of new algorithms that will allow automatic spatial object detection and vectorisation whilst providing results of an acceptable quality. The algorithms presented rely solely on ALS data and require minimal operator knowledge. Each algorithm has been designed to exploit the way in which the object exhibits itself in the data. The new algorithms are integrated into a software package called JTD (Join The Dots) that will facilitate the effective automatic processing of ALS data. The results of the new algorithms have been evaluated over four 2 x 2 km areas that have been sampled with medium resolution ALS data. The results for each area are displayed and analysed to show the applicability of the whole process in an exemplary way.