Idrottsämnet och ämnessamverkan : Idrottslärares attityder och förutsättningar till ämnessamverkan

Our interest about interdisciplinary teaching has grown during our time at Högskolan Dalarna and especially during the subject physical education. It became clear that people learn in different ways. The theoretical education in school benefits the visual and auditory strong learners but the kinesthetic strong learners find it more difficult to absorb the information. We argue that integrating subjects is a good way to mix theory and practice and thus gives more students an opportunity to learn the content of the subject. The intention of this examination paper is to investigate the relationship between the attitudes of the teachers regarding interdisciplinary teaching, the practical restrains, possibilities and the presence of interdisciplinary teaching at three different schools. Semi-structured interviews with six physical educators and three principals have been performed. An ad-hoc method has been used, with categorizing (teachers), and narrative (principals). Teachers and principal’s definitions of, the pros and cons for, and the actual presence of interdisciplinary teaching have been investigated. The main results of our studies are: 1) That teachers and principals define interdisciplinary teaching as thematic work. 2) Teachers experience lack of time for collective planning due to others duties. 3) Teachers and principals understanding of physical education makes it difficult to integrate physical education with other subjects. Some of the conclusions from this study are that interdisciplinary teaching must be voluntary. Conditions to practice interdisciplinary teaching must be sufficient, e.g regarding collective planning time. An increased presence of interdisciplinary teaching that includes physical education requires a new understanding of physical education.

Visuellt & Auditivt korttidsminne

The aim of the present study was to investigate the effect of sensory modality on short-term memory recall. An exploratory, cross-sectional study was performed. A total of 119 individuals participated. There were 70 female and 49 male subjects, aged 4 to 80 years (M=34,3). The participants were presented with 12 different objects in auditory, visual or auditory/visual mode over a period of 24 seconds. The participants were then asked to recall as many of the 12 objects as possible in any order. The study took place at a day nursery, junior high schools, meetings with elderly and adults with house calls. Non-probability samples were used. The conclusion was that visual short-term memory generated the highest recollection and that adults had the highest mean on the different stimuli. A visual element is recommended at recollection.

Gloss and gloss variations inelectrophotographic print = Glans och glansvariationer i elektrofotografiskt tryck

This degree project was performed at M-real Technology Centre in Örnsköldsvik. The perpose was to investigate thedifferences in gloss and gloss variations between chemical and ground toner and different paper grades in electrophotographicprints. Gloss is a property that gives the impression of a higher quality of a product. Therefore it is of great importance toaccomplish high gloss in advertising print.A test chart was printed on three different uncoated paper grades on three different printers. Thereafter, gloss, glossvariation, surface topography, print mottle and density were measured. A visual evalution was also performed. A multivariateanalysis was acheived of the data in order to find correlations between the measured variations.The results showed that paper grades with large surface roughness gave more variations in surface topography and glossvariations (both visual and measured) in print. A rough surface also gave more print mottle. Ground toner gave moresurface topography variations and mottle which increased with the amount of silicone used.

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.

Image processing technique to count the number of logs in a timber truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.   

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.