Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if analyzed correctly, visual clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. AI-based computer vision algorithms could be used to automatically identify painted backdrops or photographers or cluster photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision algorithms are feasible for painted backdrop identification or which techniques work better than others. We present three studies evaluating four different types of image embeddings – Inception, CLIP, MAE, and pHash – across a variety of metrics and techniques. We find that a workflow using CLIP embeddings combined with a background classifier and simulated user feedback performs best. We also discuss implications for human-AI collaboration in visual analysis and new possibilities for digital humanities scholarship. / Master of Science / Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if these photographs are analyzed correctly, clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. Artificial Intelligence-based computer vision techniques could be used to automatically identify painted backdrops or photographers or group together photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision techniques are feasible for painted backdrop identification or which techniques work better than others. We present three studies comparing four different types of computer vision techniques – Inception, CLIP, MAE, and pHash – across a variety of metrics. We find that a workflow that combines the CLIP computer vision technique, software that automatically classifies photo backgrounds, and simulated human feedback performs best. We also discuss implications for collaboration between humans and AI for analyzing images and new possibilities for academic research combining technology and history.

Time setting in production operations is necessary to properly takt and balance the flow of assembly and logistics. Time setting activities is also crucial to achieve an optimised, healthy and ergonomic assembly and logistics operation. But time setting is seldom done on a detailed enough level before deployed on the shop floor which necessitates more work of the time setting to make it reflect the work carried out and fit it to the local production area. There is also a need to redo the time setting whenever a change to a process or product has occurred. Nowadays, the time setting is often performed using very manual methods with Predetermined Motion Time Systems (PMTS), sometimes with the aid of digital tools to replace pen and paper but work otherwise practically the same way it has since its inception in the first half of the 20th century. This is a process that require skill, experience and often much time, but is also monotonous and repetitive. To aid in the time setting process, and bring PMTS into Industry 5.0; a digitalised, smart tool is proposed where video can be used to feed a computer program to do the movement classification and time setting accurately and faster than current manual processes can achieve. However, the needs, challenges, and general function of such a system is not well researched in literature. This thesis thus delivers an analysis of current state for the time setting process at a large multinational truck manufacturer with production sites in Sweden and abroad, an overview of technologies for a digitalised, smart PMTS, and a conceptual framework for analysing production tasks using a digitalised, smart system. The framework is then partially implemented to showcase the usefulness of the system and how it would work in practice. / Korrekt tidssättning i produktion är nödvändigt för att takta, planera och balansera flödet i montering och logistik. Tidssättning är också avgörande för att uppnå en optimerad, hälsosam och ergonomisk monterings- och logistikverksamhet. Men tidssättningen görs sällan på en tillräckligt detaljerad nivå innan den används på verkstadsgolvet, vilket kräver mer arbete med tidssättningen för att den ska återspegla det utförda arbetet och anpassas till det lokala produktionsområdet. Det finns också ett behov av att göra om tidssättningen när en förändring av en process eller produkt har skett. Nuförtiden utförs tidssättningen ofta med väldigt manuella metoder med förutbestämda metod-rörelsesystem (PMTS), ibland med hjälp av digitala verktyg som ersätter penna och papper, men i övrigt fungerar det praktiskt taget på samma sätt som det har gjort sedan starten under första halvan av 1900-talet. Detta är en uppgift som kräver skicklighet, erfarenhet och ofta mycket tid, men som också är monoton och repetitiv. För att underlätta tidssättningsprocessen och ta förutbestämda metod-rörelsesystem in i Industri 5.0 föreslås nu ett digitaliserat, smart verktyg där video kan användas för att mata ett datorprogram som gör rörelseklassificeringen och tidssättningen mer exakt och snabbare än vad nuvarande manuella processer kan uppnå. De behov, utmaningar och den allmänna funktionen hos ett sådant system är dock inte väl undersökt i litteraturen utan kräver mer forskning. Detta examensarbete ger därför en analys av det nuvarande läget för tidssättningsprocessen hos en stor multinationell lastbilstillverkare med produktionsanläggningar i Sverige och utomlands, en översikt över tekniker för ett digitaliserat, smart PMTS och ett konceptuellt ramverk för analys av produktionsaktiviteter med hjälp av ett digitaliserat, smart system. Ramverket implementeras sedan delvis i en demonstrator för att visa hur ett sådant system kan se ut och fungera i praktiken.