Electrical Evaluation of a Low Concentrating PVT Collector Based on Performance Ratio

Photovoltaic Thermal/Hybrid collectors are an emerging technology that combines PV and solar thermal collectors by producing heat and electricity simultaneously. In this paper, the electrical performance evaluation of a low concentrating PVT collector was done through two testing parts: power comparison and performance ratio testing. For the performance ratio testing, it is required to identify and measure the factors affecting the performance ratio on a low concentrating PVT collector. Factors such as PV cell configuration, collector acceptance angle, flow rate, tracking the sun, temperature dependence and diffuse to irradiance ratio. Solarus low concentrating PVT collector V12 was tested at Dalarna University in Sweden using the electrical equipment at the solar laboratory. The PV testing has showed differences between the two receivers. Back2 was producing 1.8 energy output more than Back1 throughout the day. Front1 and Front2 were almost the same output performance. Performance tests showed that the cell configuration for Receiver2 with cells grouping (6- 32-32-6) has proved to have a better performance ratio when to it comes to minimizing the shading effect leading to more output power throughout the day because of lowering the mismatch losses. Different factors were measured and presented in this thesis in chapter 5. With the current design, it has been obtained a peak power at STC of 107W per receiver. The solar cells have an electrical efficiency of approximately 19% while the maximum measured electrical efficiency for the collector was approximately 18 % per active cell area, in addition to a temperature coefficient of -0.53%/ ˚C. Finally a recommendation was done to help Solarus AB to know how much the electrical performance is affected during variable ambient condition and be able to use the results for analyzing and introducing new modification if needed.

Load balancing solution and evaluation of F5 content switch equipment

The Thesis focused on hardware based Load balancing solution of web traffic through a load balancer F5 content switch. In this project, the implemented scenario for distributing HTTPtraffic load is based on different CPU usages (processing speed) of multiple member servers.Two widely used load balancing algorithms Round Robin (RR) and Ratio model (weighted Round Robin) are implemented through F5 load balancer. For evaluating the performance of F5 content switch, some experimental tests has been taken on implemented scenarios using RR and Ratio model load balancing algorithms. The performance is examined in terms of throughput (bits/sec) and Response time of member servers in a load balancing pool. From these experiments we have observed that Ratio Model load balancing algorithm is most suitable in the environment of load balancing servers with different CPU usages as it allows assigning the weight according to CPU usage both in static and dynamic load balancing of servers.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Induktiv provning av ferritiskt rostfritt stå

The report examines the factors which may be a contributing cause to the problems that are present when ferritic stainless steel are eddy current tested in a warm condition. The work is carried out at Fagersta Stainless AB in Fagersta which manufactures stainless steel wire. In the rolling mill there is an eddy current equipment for detection of surface defects on the wire. The ferritic stainless steels cause a noise when testing and this noise complicates the detection of defects.Because of this, a study was made of how the noise related to factors such as steel grade, temperature, size and velocity. By observing the signal and with the possibilities to change the equipment settings the capability to let a signal filter reduce the noise level were evaluated. Theories about the material's physical properties have also been included, mainly the magnetic properties, electrical conductivity and the material's tendency to oxidize.Results from the tests show that a number of factors do not affect the inductive test significantly and to use a filter to reduce the noise level does not seem to be a viable option. The level of noise does not relate to the presence of superficial particles in form of oxides.The ferritic stainless steels showed some difference in noise level. Which noise level there was did match well with the steels probability for a precipitation of a second phase, and precipitation of austenite may in this case contribute to noise when using an eddy current instrument.The noise is probably due to some physical material property that varies within the thread.

Acupuncture with manual and electrical stimulation for labour pain : a longitudinal randomised controlled trial

Background: Acupuncture is commonly used to reduce pain during labour despite contradictory results. The aim of this study is to evaluate the effectiveness of acupuncture with manual stimulation and acupuncture with combined manual and electrical stimulation (electro-acupuncture) compared with standard care in reducing labour pain. Our hypothesis was that both acupuncture stimulation techniques were more effective than standard care, and that electro-acupuncture was most effective.  Methods: A longitudinal randomised controlled trial. The recruitment of participants took place at the admission to the labour ward between November 2008 and October 2011 at two Swedish hospitals. 303 nulliparous women with normal pregnancies were randomised to: 40 minutes of manual acupuncture (MA), electro-acupuncture (EA), or standard care without acupuncture (SC). Primary outcome: labour pain, assessed by Visual Analogue Scale (VAS). Secondary outcomes: relaxation, use of obstetric pain relief during labour and post-partum assessments of labour pain. The sample size calculation was based on the primary outcome and a difference of 15 mm on VAS was regarded as clinically relevant, this gave 101 in each group, including a total of 303 women.  Results: Mean estimated pain scores on VAS (SC: 69.0, MA: 66.4 and EA: 68.5), adjusted for: treatment, age, education, and time from baseline, with no interactions did not differ between the groups (SC vs MA: mean difference 2.6, 95% confidence interval [CI] -1.7-6.9 and SC vs EA: mean difference 0.6 [95% CI] -3.6-4.8). Fewer number of women in the EA group used epidural analgesia (46%) than women in the MA group (61%) and SC group (70%) (EA vs SC: odds ratio [OR] 0.35; [95% CI] 0.19-0.67).  Conclusions: Acupuncture does not reduce women's experience of labour pain, neither with manual stimulation nor with combined manual and electrical stimulation. However, fewer women in the EA group used epidural analgesia thus indicating that the effect of acupuncture with electrical stimulation may be underestimated. These findings were obtained in a context with free access to other forms of pain relief.

Acupuncture with manual and electrical stimulation for labour pain : a two month follow up of recollection of pain and birth experience.

BACKGROUND: In a previous randomised controlled trial we showed that acupuncture with a combination of manual- and electrical stimulation (EA) did not affect the level of pain, as compared with acupuncture with manual stimulation (MA) and standard care (SC), but reduced the need for other forms of pain relief, including epidural analgesia. To dismiss an under-treatment of pain in the trial, we did a long-term follow up on the recollection of labour pain and the birth experience comparing acupuncture with manual stimulation, acupuncture with combined electrical and manual stimulation with standard care. Our hypothesis was that despite the lower frequency of use of other pain relief, women who had received EA would make similar retrospective assessments of labour pain and the birth experience 2 months after birth as women who received standard care (SC) or acupuncture with manual stimulation (MA). METHODS: Secondary analyses of data collected for a randomised controlled trial conducted at two delivery wards in Sweden. A total of 303 nulliparous women with normal pregnancies were randomised to: 40 min of MA or EA, or SC without acupuncture. Questionnaires were administered the day after partus and 2 months later. RESULTS: Two months postpartum, the mean recalled pain on the visual analogue scale (SC: 70.1, MA: 69.3 and EA: 68.7) did not differ between the groups (SC vs MA: adjusted mean difference 0.8, 95 % confidence interval [CI] -6.3 to 7.9 and SC vs EA: mean difference 1.3 CI 95 % -5.5 to 8.1). Positive birth experience (SC: 54.3 %, MA: 64.6 % and EA: 61.0 %) did not differ between the groups (SC vs MA: adjusted Odds Ratio [OR] 1.8, CI 95 % 0.9 to 3.7 and SC vs EA: OR 1.4 CI 95 % 0.7 to 2.6). CONCLUSIONS: Despite the lower use of other pain relief, women who received acupuncture with the combination of manual and electrical stimulation during labour made the same retrospective assessments of labour pain and birth experience 2 months postpartum as those who received acupuncture with manual stimulation or standard care. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01197950.

Demonstration of Solar Heating and Cooling System using Sorption Integrated Solar Thermal Collectors

Producing cost-competitive small and medium-sized solar cooling systems is currently a significant challenge. Due to system complexity, extensive engineering, design and equipment costs; the installation costs of solar thermal cooling systems are prohibitively high. In efforts to overcome these limitations, a novel sorption heat pump module has been developed and directly integrated into a solar thermal collector. The module comprises a fully encapsulated sorption tube containing hygroscopic salt sorbent and water as a refrigerant, sealed under vacuum with no moving parts. A 5.6m2 aperture area outdoor laboratory-scale system of sorption module integrated solar collectors was installed in Stockholm, Sweden and evaluated under constant re-cooling and chilled fluid return temperatures in order to assess collector performance. Measured average solar cooling COP was 0.19 with average cooling powers between 120 and 200 Wm-2 collector aperture area. It was observed that average collector cooling power is constant at daily insolation levels above 3.6 kWhm-2 with the cooling energy produced being proportional to solar insolation. For full evaluation of an integrated sorption collector solar heating and cooling system, under the umbrella of a European Union project for technological innovation, a 180 m2 large-scale demonstration system has been installed in Karlstad, Sweden. Results from the installation commissioned in summer 2014 with non-optimised control strategies showed average electrical COP of 10.6 and average cooling powers between 140 and 250 Wm-2 collector aperture area. Optimisation of control strategies, heat transfer fluid flows through the collectors and electrical COP will be carried out in autumn 2014.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Monitoring vegetation on railway embankments : supporting maintenance decisions

The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections

Detecting Plants on Railway Embankments

This paper investigates problems concerning vegetation along railways and proposes automatic means of detecting ground vegetation. Digital images of railway embankments have been acquired and used for the purpose. The current work mainly proposes two algorithms to be able to achieve automation. Initially a vegetation detection algorithm has been investigated for the purpose of detecting vegetation. Further a rail detection algorithm that is capable of identifying the rails and eventually the valid sampling area has been investigated. Results achieved in the current work report satisfactory (qualitative) detection rates.