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A cost-effective, faster and efficient way of screening wheat samples suitable for tortilla production is needed. Hence, we developed prediction models for tortilla quality (diameter, specific volume, color and texture parameters) using grain, flour and dough properties of 16 wheat flours. The prediction models were developed using stepwise multiple regression. Dough rheological tests had higher correlations with tortilla quality than grain and flour chemical tests. Dough resistance to extension was correlated best with tortilla quality, particularly tortilla diameter (r= -0.87, P<0.01). Gluten index was significantly correlated with tortilla diameter (r = -0.67, P less than 0.01) and specific volume (r = -0.73, P less than 0.01). Tortilla diameter was the parameter best predicted. An r2 of 0.87 was obtained when mix-time and dough resistance to extension were entered into the model. This model was validated using another sample set, and an r^2 of 0.91 was obtained. Refined and whole wheat flours, dough and tortillas were compared using five wheat samples. Refined flour doughs were more extensible and softer than whole wheat flour doughs. Whole wheat flour tortillas were larger, thinner and less opaque than refined flour tortillas. Refined wheat flour had much smaller particle size and less fiber than whole wheat flour. These are the major factors that contributed to the observed differences. In general, refined wheat tortillas were more shelf-stable than whole wheat tortillas. However, whole wheat tortillas from strong flours had excellent shelf-stability which must be considered when whole wheat tortillas are processed. . Different objective rheological techniques were used to characterize the texture of refined and whole flour tortillas during storage. Differences in texture between 0, 1 and 4 day-old tortillas were detected by rupture distance from one and two-dimension extensibility techniques. In general, the deformation modulus was not a good parameter to differentiate tortilla texture at the beginning of storage. It detected textural changes of 8 and 14 day-old tortillas. The subjective rollability method detected textural changes after 4 days storage.

Surveillance of areas of interest through image acquisition is becoming increasingly essential for intelligence services. Several types of platforms equipped with sensors are used to collect good quality images of the areas to be monitored. The evolution of this field has different levels: some studies are only based on improving the quality of the images acquired through sensors, others on the efficiency of platforms such as satellites, aircraft and vessels which will navigate the areas of interest and yet others are based on the optimization of the trajectory of these platforms. Apart from these, intelligence organizations demonstrate an interest in carrying out such missions by sharing their resources. This thesis presents a framework whose main objective is to allow intelligence organizations to carry out their observation missions by pooling their platforms with other organizations having similar or geographically close targets. This framework will use Multi-Objective Optimization algorithms based on genetic algorithms to optimize such mission planning. Research on sensor fusion will be a key point to this thesis, researchers have proven that an image resulting from the fusion of two images from different sensors can provide more information compared to the original images. Given that the main goal for observation missions is to collect quality imagery, this work will also use High-Level Information Fusion to optimize mission planning based on image quality and fusion. The results of the experiments not only demonstrate the added value of this framework but also highlight its strengths (through performance metrics) as compared to other similar frameworks.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21917号 / 情博第700号 / 新制||情||120(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 加納 学, 教授 杉江 俊治, 教授 大塚 敏之 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Given the high costs and general shortage of coking coals on the domestic and international markets, and because the nature and qualities of many of the coking coals available on the markets are themselves mixed products, conventional mechanisms and tried and trusted formulae for manufacturing coke products based on single coals of known qualities can no longer apply. There is therefore an urgent need to develop more effective techniques for evaluating and assessing the properties of individual coals rapidly and reliably and in a manner that could provide useful data for use in modelling the effect of new coal components in a coke blend. Towards this end, the current research has sought to find more accurate coal characterisation techniques at laboratory scale than currently exists in industry at present. Seventeen coking or blend coking coals from widely different sources were selected and cokes were produced from them in as close to full scale conventional conditions as possible. Both coals and cokes were analysed using conventional chemical, physical, petrographic and rheological coking methods. The results indicated that, whilst all coals had acceptable chemical, physical and petrographic properties as evaluated on individual parameters thereby indicating their potential values as prime coking coals, in fact the resultant cokes of some of the coals had properties that disproved this assessment. These anomalies were investigated by integrating all characteristics and statistically evaluating them. The result [outcome] indicated that the series of coals under review fall naturally into three distinct categories according to rank, as determined by the reflectance of vitrinite, and that the coking coals in each rank category were further characterised by parameters specific to that level of rank. In this way more accurate predictions of coke quality were obtained than has been the case to date when using single set evaluations or previously devised formulae. On this basis it was concluded that, when selecting coals for coke making, it is essential to first establish the rank of the coal by vitrinite reflectance and then to apply coke evaluating parameters specific to that level of rank. The formulae developed for this purpose held good for all coals tested, however, it remains to be seen whether this applies universally to an even wider source of coals.

Context: The newest standard in video coding High Efficiency Video Coding (HEVC) should have an appropriate coder to fully use its potential. There are a lot of video quality assessment methods. These methods are necessary to establish the quality of the video. Objectives: This thesis is a comparison of video quality assessment methods. Objective is to find out which objective method is the most similar to the subjective method. Videos used in tests are encoded in the H.265/HEVC standard. Methods: For testing MSE, PSNR, SSIM methods there is special software created in MATLAB. For VQM method downloaded software was used for testing. Results and conclusions: For videos watched on mobile device: PSNR is the most similar to subjective metric. However for videos watched on television screen: VQM is the most similar to subjective metric. Keywords: Video Quality Assessment, Video Quality Prediction, Video Compression, Video Quality Metrics

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This Master's thesis focuses on improving the understanding and monitoring of the Hot Isostatic Pressing (HIP) sintering process used by Seco Tools AB to manufacture cemented carbides for cutting tools. While essential for producing cutting tools with superior hardness and toughness the HIP sintering process introduces a complex relationship between the selected process parameters and the achieved materials properties. With the goal of establishing batch process monitoring capabilities, this master thesis employs Multivariate Statistical Process Control (MSPC) strategies through the creation of Batch Evolution Models (BEMs) and Batch Level Models (BLMs) to monitor, predict end-product quality, and analyze the batch production HIP sintering process.  The developed models effectively account for significant variation in the HIP sintering process and demonstrate potential in identifying deviant batches. Enhancements to the models' performance are achieved through the incorporation of preprocessing, phase-specific variable selection, and specialized model training. These proposed enhancements yield discernible improvements, as evidenced by enhanced model fit and other statistical metrics.  Challenges arise when the models are tested with real-time data due to progressive changes in some tracked process variables. Block-scaling is applied to restore the real-time monitoring capabilities, but also introduces additional complexity to the models. In addition, this master thesis highlights the need for continuous and regular maintenance of these models to ensure real-time monitoring and anomaly detection capabilities. The models demonstrate varied effectiveness in predicting final product quality. For instance, they exhibit some potential in predicting Magnetic Saturation (MS), but their ability to predict Magnetic Coercivity (HC) seems nonexistent. Despite attempts to improve the predictive abilities the models are still not able to confidently predict these metrics. The master’s thesis highlights variability in powder contents and access to data of known quality nonconformities as potential areas for improving the predictive models.

Hybrit development AB är ett bolag som strävar mot att kunna producera fossilfritt stål genom att reducera järnmalmspellets med hjälp av vätgas. Därför har Hybrit utfört experimentella kampanjer där genomförbarheten av att reducera järnmalmspellets med hjälp av vätgas undersökts och studerats. Vid produktion av järn och stål måste produktkvalitén tas i beaktan. Reduktionsprocessen karaktäriseras av en mängd olika process- och kvalitetsparametrar, där kvalitetsparametrarna beskriver produktkvalitén. Det är av intresse att studera hur processparametrarna påverkar produktkvalitén. Processparametrarna kan mätas vid vilket tidpunkt som helst genom olika sensorer. Produktkvalitén kan bestämmas först efter att järnmalmspelletsen är färdigreducerad. Därför präglas processen av en tidsfördröjning mellan mätningen av processparametrarna och labanalysemätningarna av kvalitetsparametrarna. På grund av tidsfördröjningen är det av intresse att kunna prediktera produktkvalitén utifrån processparametrarna. Om det går att prediktera produktkvalitén, är det av vikt att kunna avgöra prediktionens giltighet.  Examensarbetets syfte är att identifiera hur reduktionsprocessparametrarna påverkar reducerade järnets kvalitetsparametrar. En processövervakningsmetod som passar för processövervakning ska testas och undersökas utifrån hur metoden kan användas för att avgöra prediktionens giltighet. Processövervakningen ska användas för att avgöra om processen befinner sig i ett processläge som bidrar till en någorlunda korrekt och lämplig prediktion av produktkvalitén.  För analys av data användes 65 processparametrar och 6 kvalitetsparametrar. Den multivariata analysmetoden Partial Least Squares (PLS) användes för att nå syftet med examensarbetet. Via PLS skapades en modell som kunde beskriva vilka processparametrar som påverkade kvalitetsparametrarna samt hur processparametrarna påverkade kvalitetsparametrarna. PLS-modellen kunde prediktera kvalitetsparametrarna någorlunda korrekt och lämpligt, givet att processen befinner sig inom ramen för modellen och att det är en hög förklaringsgrad för kvalitetsparametern som predikteras. Kvalitetsparametern Y6-1 predikterades sämre eftersom förklaringsgraden för Y6-1 var låg. Processövervakningsmetoden som testades och undersöktes var PLS-övervakning. För att undersöka hur PLS-övervakning kan användas för att avgöra prediktions giltighet, användes tre processövervakningsverktyg. Dessa var X-scores processövervakning, Hotelling T2 och SPE. Resultatet var att PLS-övervakning kunde angiva hur processen förhåller sig till modellen. Observationerna som avvek i PLS-övervakningen predikterades sämre. Därmed kunde information om prediktionens giltighet genom PLS-övervakning erhållas. Att tillämpa PLS-övervakning för att avgöra prediktionens giltighet är en större framgång. Detta på grund av att information om produktkvalitén innan reduktionsprocessen är genomförd kan användas för att säkerställa produktion med tillfredställande kvalitet. Att tillämpa multivariata processövervakningsmetoder för att övervaka de predikterade kvalitetsparametrarna kan vara av intresse för framtida studier. Detta då processövervakningen kan användas för att minimera den interna variationen hos kvalitetsparametrarna. / Hybrit development AB strives to produce fossil-free steel by using hydrogen for the direct reduction process of iron ore pellets. To achieve that goal, Hybrit has carried out experimental campaigns where the feasibility of direct reduction using hydrogen gas has been investigated and studied. The quality of the reduced iron must be considered when producing iron and steel. The reduction process is characterized by a variety of process- and quality parameters. Because the quality parameters describe the quality of the product, it is of interest to study how the process parameters affect the quality parameters. The process parameters can be measured at any time through various sensors around the reactor in which the iron ore pellets are reduced. While the quality of the product can only be determined after the iron ore pellets have been completely reduced. Therefore, the process is characterized by a time delay between the measurement of the process parameters and the measurement of the quality parameters, where the reduced iron must be analyzed in a laboratory before the quality parameters can be measured. Because of the time delay, it is of interest to be able to predict the quality of the product based on the process parameters. If it is possible to predict the quality, then it is of importance to be able to determine the validity of the prediction.  The aim of this master thesis is to identify how the reduction process parameters affect the quality parameters of the reduced iron. A process monitoring method suitable for monitoring the process need be tested and investigated based on how the method can be used to determine the validity of the prediction. The process monitoring will be used to determine whether the process is in a process state that contributes to a reasonably accurate and appropriate prediction of the quality of the product.  65 process parameters and 6 quality parameters were used for the analysis of how the reduction process parameters affect the quality parameters of the reduced iron. The multivariate analysis method Partial Least Squares (PLS) was used to achieve the aim of the thesis. A multivariate model which could describe how the process parameters affect the quality parameters was created through PLS. The PLS-model was able to predict the quality parameters reasonably correctly and appropriately, given that the process is within the scope of the model and that the explanatory power is high for the quality parameter that is predicted. The quality parameter Y6-1 could not be predicted reasonably correct as the explanatory power for Y6-1 was low. The process monitoring method tested and investigated was PLS monitoring. Three process monitoring tools were used when PLS monitoring was investigated based on how they can be used to determine the validity of the prediction. These tools were X-scores process monitoring, Hotelling T2 and SPE. The result was that PLS monitoring could indicate how the process relates to the model. Observations that deviated in the PLS monitoring could not be predicted correctly. Thus, information about the validity of the prediction through PLS monitoring could be obtained. Applying PLS monitoring to determine the validity of the prediction is a greater success. This is because information about the quality of the product before the reduction process is completed can be used to ensure production with a satisfactory product quality. Applying multivariate process monitoring methods to monitor the predicted quality parameters may be of interest for future studies. This is because the process monitoring can be used to minimize the internal variation of the quality parameters.

No / In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.