Jämförelse av IBM Model 4-alignment : En jämförelse av hur storleken på träningsdata påverkar tolkningsnoggrannheten och träningstiden för två alignment-modeller som översätter naturligt språk

In modern society the amount of information processed by computers is increasing everyday. Computer translation has the potential to speed up communication between humans as well as human-computer interactions. For Statistical Machine Translation word alignment is key. How large does a corpus need to be to align a natural language sentence with a simple unambiguous language? We investigate this matter by running a simple algorithm and comparing it to the results we get from an industry equivalent. The results show that the size of the corpus needs to be larger for the simplified model when there is a greater number of words per sentence. The IBM Model 4 conversely shows that the more words per sentence decrease the necessary size of the corpus to make better predictions.Thus we can conclude that corpus size is dependant on the number of terms in each sentence for both models.I vårat moderna samhälle bearbetas mer information för varje dag. Datoriserad översättning har potentialen att öka hastigheten utav kommunikationen mellan människor emellan samt människa-datorinteraktion. För Statistical Machine Translation så är word alignment en stor del. Hur stor måste en korpus vara för att man med stor sannolikhet lyckats att korrekt översätta meningar från ett naturligt språk med ett simpelt entydigtspråk?Vi testar detta genom att jämföra en simpel algorithm med en algoritm som används inom industrin. I resultaten ser vi att ju mer ord som finns i meningen som ska översättas, ju större måste korpusen vara. Med IBM Model 4 ser vi att resultaten blir bättre med ju fler ord per mening och därför kan korpusstorleken minskas. Vår slutsats är att korpus storleken beror på mängden aritmetiska termer för båda modellerna

Jämförelse av IBM Model 4-alignment : En jämförelse av hur storleken på träningsdata påverkar tolkningsnoggrannheten och träningstiden för två alignment-modeller som översätter naturligt språk

In modern society the amount of information processed by computers is increasing everyday. Computer translation has the potential to speed up communication between humans as well as human-computer interactions. For Statistical Machine Translation word alignment is key. How large does a corpus need to be to align a natural language sentence with a simple unambiguous language? We investigate this matter by running a simple algorithm and comparing it to the results we get from an industry equivalent. The results show that the size of the corpus needs to be larger for the simplified model when there is a greater number of words per sentence. The IBM Model 4 conversely shows that the more words per sentence decrease the necessary size of the corpus to make better predictions.Thus we can conclude that corpus size is dependant on the number of terms in each sentence for both models.I vårat moderna samhälle bearbetas mer information för varje dag. Datoriserad översättning har potentialen att öka hastigheten utav kommunikationen mellan människor emellan samt människa-datorinteraktion. För Statistical Machine Translation så är word alignment en stor del. Hur stor måste en korpus vara för att man med stor sannolikhet lyckats att korrekt översätta meningar från ett naturligt språk med ett simpelt entydigtspråk?Vi testar detta genom att jämföra en simpel algorithm med en algoritm som används inom industrin. I resultaten ser vi att ju mer ord som finns i meningen som ska översättas, ju större måste korpusen vara. Med IBM Model 4 ser vi att resultaten blir bättre med ju fler ord per mening och därför kan korpusstorleken minskas. Vår slutsats är att korpus storleken beror på mängden aritmetiska termer för båda modellerna

Electric Vehicle Fire Safety in Enclosed Spaces

Lately, concerns regarding fires in electric vehicles in enclosed spaces such as in road tunnels and parking garages have been raised and there are indications that parking of electric vehicles may be prohibited in some spaces. For the success of electromobility and the transition from fossil to renewable fuels, it is important to understand the risks and consequences of fires in electric vehicles and to provide technical solutions if necessary, so as not to hinder the widespread adoption of electric vehicles. In this work, a literature review on fires in vehicles has been conducted. The focus was on fires in enclosed spaces involving electric vehicles. A comprehensive risk assessment of electric vehicle fires was performed using systematic hazard identification. In addition, a workshop with representatives from three Swedish fire and rescue services was carried out to evaluate the emergency rescue sheets/response guides. The main conclusions are; That statistics regarding vehicle fires need to be improved, as of today the root causes of fires are missing in the data, which could potentially result in non-fact based regulations; The data studied in this work does not imply that fires in electric vehicles are more common than fires in internal combustion engine vehicles; Fires in electric vehicles and internal combustion engine vehicles are similar in regards to the fire intensity and peak heat release rates.  The most effective risk reductions measures on vehicle level, to decrease the number of fires in EVs, could not be defined based on that relevant data on the root causes of fires in EVs are currently not publicly accessible. The most effective risk reduction measures, to limit fire spread, on infrastructure level were the use of fire sprinkler systems, fire detection systems (early detection) and increased distance between parked vehicles

MSWI bottom ash as road material - leaching and environmental impact from a test road

(1) Uppläggning och metodik; (2) Provvägens uppbyggnad och materialegenskaper; (3) Vägmaterialens geokemiska sammansättning och laboratoriella lakegenskaper; (4) Utlakat från fältlysimetrar; (5) Jämförelse mellan utlakade föreningar i fälttestet och i labbtesten, kopplade till askan; (6) Grundvatten; (7) Tjäldjup; (8) Modellering av utläckaget från provvägen; (9) Den geokemiska bakgrunden i regionen; (10) Ackumulerade ämnen under vägkroppen; (11) Bedömning av miljöbelastnin

Using optical tweezers for measuring the interaction forces between human bone cells and implant surfaces: System design and force calibration

Optical tweezers were used to study the interaction and attachment of human bone cells to various types of medical implant materials. Ideally, the implant should facilitate cell attachment and promote migration of the progenitor cells in order to decrease the healing time. It is therefore of interest, in a controlled manner, to be able to monitor the cell adhesion process. Results from such studies would help foresee the clinical outcome of integrating medical implants. The interactions between two primary cell culture models, human gingival fibroblasts and bone forming human osteoblast cells, and three different implant materials, glass, titanium, and hydroxyapatite, were studied. A novel type of optical tweezers, which has a newly designed quadrant detector and a powerful 3 W laser was constructed and force calibrated using two different methods: one method in which the stiffness of the optical trap was obtained by monitoring the phase lag between the trap and the moved object when imposing a forced oscillation on the trapped object and another method in which the maximum trapping force was derived from the critical velocity at which the object escapes the trap. Polystyrene beads as well as cells were utilized for the calibrations. This is the first time that cells have been used directly for these types of force calibrations and, hence, direct measurements of forces exerted on cells can be performed, thus avoiding the difficulties often encountered when translating the results obtained from cell measurements to the calibrations obtained with reference materials. This more straightforward approach represents an advantage in comparison to established methods. © 2007 American Institute of Physics