No description available.

No description available.

No description available.

No description available.

Computer vision, a prominent subfield of artificial intelligence, has gained widespread util-ization in diverse domains such as surveillance, security, and robotics. This research en-deavors to develop an semi-automated defect detection system serving as a quality controlassurance mechanism for Nolato MediTor, a manufacturing company within the medicaldevice industries engaged in the production of anesthesia breathing bags. The primary fo-cus of this study revolves around the detection of a specific defect, namely, holes. Withinthe context of Nolato MediTor, prioritizing recall (sensitivity) assumes utmost signific-ance as it entails favoring the rejection of functional breathing bags over the inadvertentacceptance of defective ones. The proposed system encompasses a robust metallic standfacilitating precise positioning for three distinct camera angles, accompanied by a XiaomiRedmi Note 11 Pro phone and a software component, designed to process incoming imagefolders representing a complete view of a breathing bag from multiple angles. Subsequently,these images undergo analysis using the learned weights derived from the implementedMask R-CNN model, enabling a cohesive assessment of the breathing bag. The system’sperformance was rigorously evaluated, and the best-performing weights demonstrated aremarkable recall rate of 0.995 for the first test set, exceeding the desired recall thresholdof 95%. Similarly, for the second test set, the recall rate achieved an impressive value of0.949, narrowly missing the 95% threshold by a marginal 0.001. Furthermore, the com-putational efficiency, quantified as the processing time per breathing bag, on average, thelongest duration recorded amounted to approximately 10.151 seconds, with the poten-tial for further enhancement by employing a higher standard GPU. This study serves as aproof of concept, demonstrating the feasibility of achieving semi-automated quality controlutilizing CNN. The implemented system represents a promising prototype with potentialscalability for improved operational conditions and expanded defect coverage, thus pavingthe way towards a fully automated quality control within large-scale industries.

When aluminium profiles are manufactured, defects may arise, especially when theprofiles are manufactured through extrusion. During the extrusion process, thesedefects occur on parts of the extruded material because of contaminates that needto be found.The goal of the project is to examine two different types of methods that aresuited for aluminium profiles in the category of non-destructive testing (NDT). Thetwo different methods are non-linear ultrasonic methods called Higher Harmonicsand Non-linear Wave Modulation Spectroscopy. The method that works should thenalso be implemented in a fixture. Of these two methods Higher Harmonics showedthe most promising results by identifying which profile has defects and this methodwas also more appropriate to implement in a fixture.The Higher Harmonics method utilizes two transducers, one as an emitter to thesystem and another as a receiver. When the signal from the emitter passes throughthe non-linearities, which are the defects, and propagates to the receiver the signalcan be inspected on an oscilloscope. The received signal is then transformed to thefrequency domain and if there were defects harmonics should be generated. If thereare many harmonics and the magnitude is high, defects are present.A fixture was also designed to work with the Higher Harmonics method usingprofile struts that are easy to connect to a framework. The fixture worked by raisingand lowering the two transducers which have been built into a probe. The emittingprobe was connected to a signal generator and the receiving probe was connected toan oscilloscope. This measuring station did the measurement more consistent andaccurate than before. The most interesting result that was found in the experimentswas that localization of defects could be found. / Vid tillverkning av aluminiumprofiler kan vissa särskilda defekter uppstå när pro-filerna tillverkas genom extrudering. Under extruderings processen, uppstår dessadefekter på delar av det extruderade materialet på grund av föroreningar och måstehittas.Målet för projektet är att undersöka två olika typer av metoder som är lämpligaför aluminiumprofiler i kategorin oförstörande provning (OFP). De två olika metodernaär olinjära ultraljudsmetoder som kallas Övertoner och Icke-linjär Vågmodulation.Metoden som fungerar bäst bör då också implementeras i en fixtur. Av dessa tvåmetoder visade Övertoner mest lovande resultat genom att kunna identifiera vilkenprofil som var defekt och denna metod var också lämpligare att bygga en fixtur till.Metoden med Övertoner använder två givare, en som sändare till system och enannan som mottagare. När signalen från sändaren passerar olinjäriteterna, som ärdefekterna, och signalen färdas till mottagaren kan den inspekteras på ett oscilloskop.Den mottagna signalen omvandlas sedan till frekvensdomän och om det fanns defek-ter bör övertoner genereras. När defekter är närvarande finns det många övertoneroch magnituden är hög.En fixtur utformades också för att fungera med metoden Övertoner genom attanvända profilstag som är lätta att ansluta till ett ramverk. Fixturen fungeradegenom att höja och sänka de två givarna som har byggts in i en probe. Sändarproben är ansluten till en signalgenerator och den mottagande proben är ansluten tillett oscilloskop. Denna mätstation gjorde mätningen mer konsekvent och noggrannän tidigare mätningar. Det mest intressanta resultatet som framkom i experimentenvar att lokalisering av defekter kunde konstateras.

The main purpose of exception handling mechanisms is to improve software robustness by handling exceptions when they occur. However, empirical evidence indicates that improper implementation of exception handling code can be a source of faults in software systems. There is still limited empirical knowledge about the relationship between exception handling code and defects. In this dissertation, we present three case studies investigating defect densities of exception handling code. The results show that in every system under study, the defect density of exception handling code was significantly higher than the defect density of overall source code and normal code. The ability to predict the location of faults can assist in directing quality enhancement efforts to modules that are likely to have faults. This information can be used to guide test plans, narrow the test space, and improve software quality. We hypothesize that complicated exception handling structure is a predictive factor that is associated with defects. To the best of our knowledge, no study has addressed the relationship between the attributes of exception handling method call structures and defect occurrence, nor has prior work addressed fault prediction. We extract exception-based software metrics from the structural attributes of exception handling call graphs. To find out whether there are patterns of relationship between exception-based software metrics and fault-proneness, we propose a defect prediction model using exception handling call structures. We apply the J48 algorithm, which is the Java implementation of the C4.5 algorithm, to build exception defect prediction models. In two out of three systems under study, the results reveal that there are logical patterns of relationships between most class level exception metrics and fault-proneness. The accuracy of our prediction models is comparable to the results of defect prediction model studies in the literature. It was observed that our approach has somewhat worse predictive accuracy when a system has low average defects per class.

Nanoscale manufacturing techniques enable very high density nano fabrics but may cause orders of magnitude higher levels of defects and variability than in today‟s CMOS processes. As a result, nanoscale architectures typically introduce redundancy at multiple levels of abstractions to mask faults. Schemes such as Triple Modular Redundancy (TMR) and structural redundancies are tailored to maximize yield but can impact performance significantly. For example, due to increases in circuit fan-in and fan-out, a quadratic performance impact is often projected. In this thesis, we introduce a new class of redundancy schemes called FastTrack, designed to provide fault tolerance but without their negative impact on performance. FastTrack relies on combining non-uniform structural redundancy with uniquely biased nanoscale voters. A variety of such techniques are employed on a Wire Streaming Processor (WISP-0) implemented on the Nanoscale Application Specific Integrated Circuits (NASIC) nanowire fabric. We show that FastTrack schemes can provide 23% higher effective yield than conventional redundancy schemes even at 10% defect rates. Most importantly, the yield improvement is achieved in conjunction with 79% lesser performance impact at 10% defect rate. This is the first redundancy scheme we are aware of to achieve such degree of fault masking without the considerable performance impact of conventional approaches. The same setup is also used to mask the effects of parameter variation. FastTrack techniques show up to 6X performance improvement compared to more traditional redundancy schemes even at higher defect rates. In the absence of defects, a FastTrack scheme can be up to 7X faster than a traditional redundancy scheme.

Membrane processes have received growing attention due to their low energy consumption and ease of operation. Thin-film composite reverse osmosis membranes based on polyamides are the most widely applied commercial membranes, because of their high flux and selectivity. However, their application for gas separation processes is still limited. This is the due to the presence of defects in the membrane when in the dry state. Traditionally, thin-film composite membranes are made by interfacial polymerization between a polar (aqueous) phase and a non-polar (organic) phase. The most commonly applied thin-film composite membranes are made by dissolving m-phenylene diamine in the aqueous phase and trimesoyl chloride in the organic phase. This work investigated the possibility of fabricating thin-film composite membranes when an aliphatic alcohol (methanol, ethanol or isopropanol) is used as the polar phase. This is further extended to examining the ability of a PDMS coating to plug the defects in such layers. The effects of temperature and support type on the membrane performance were also studied. Solubility tests were conducted to determine the solubility limit of commercial and in-house fabricated amine monomers in water, methanol, ethanol and isopropanol. Water-insoluble monomers were found to be soluble in ethanol and methanol. Gas permeation tests were conducted on membranes made using water, methanol, ethanol and isopropanol as the polar phase. The results showed that the membranes produced by aliphatic alcohols had higher selectivities. The highest H2/CO2 selectivity of ~ 26 was observed in the ethanol-based membranes when they were coated with PDMS and tested at 80 C. It was confirmed that PDMS is able to plug the defects in the membrane. Membranes made on the polysulfone support were found to have higher permeance and comparable selectivity relative to the membranes made on the polyacrylonitrile supports. It was also found that a change in the polar phase solvent is able to alter the morphology of the membranes. SEM micrographs showed clear differences in the surface structure of each membrane. The average thickness values obtained from ellipsometry measurements showed a correlation with the interface miscibility. The thickest membrane corresponded to the most miscible interface (IPA/Isopar).

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24443号 / 理博第4942号 / 新制||理||1706(附属図書館) / 京都大学大学院理学研究科化学専攻 / (主査)教授 若宮 淳志, 教授 依光 英樹, 教授 畠山 琢次 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM