Pikselikohtaisen tienpinnanliukkauden arviointi heikolla koneoppimisella autonomisessa ajamisessa

This thesis proposes a method for estimating road surface slipperiness at the input image pixel level to enable improvements in autonomous and non-autonomous driving in adverse weather conditions. The proposed method uses a fully convolutional neural network to process an image taken by a forward-facing camera on a car to produce a pixelwise slipperiness estimation with the resolution of the input image. To find the best-performing model multiple deep learning methods were tested and analyzed for improvements on the task, and the performance of the final model was further examined quantitatively and qualitatively. The model was trained using a recently collected dataset collected from various weather conditions. The data consisted of camera images, which were used as input, and grip measurements, which were used as the ground truth labels. The data was preprocessed to formulate a weakly supervised learning task and it was found that the model can be trained with sparsely labelled data to produce meaningfully accurate results while greatly improving the resolution of slipperiness estimations compared to prior existing solutions. The method was improved with auxiliary learning, weighted sampling during training, and pretrained models, while results on the use of thermal camera input were inconclusive. While the study left space to optimize many hyperparameters further the results imply that fully convolutional networks or other similar models could be used to improve autonomous and non-autonomous driving in adverse weather conditions by increasing the amount of slipperiness information available to other autonomous driving software components or the human driver.Tässä diplomityössä esitellään menetelmä tienpinnan liukkauden arvioimiseksi pikselikohtaisesti, autonomisen ja manuaalisen ajamisen parantamiseksi haitallisissa keliolosuhteissa. Esitelty menetelmä hyödyntää täysin konvolutiivisia neuroverkkoja auton etukameralla otetun kuvan käsittelemiseksi ja pikselikohtaisista liukkausarvoista koostuvan kuvan luomiseksi. Luodun kuvan erottelukyky on yhtä suuri kuin alkuperäisen syötteenä annetun kuvan. Useita syväoppimismenetelmiä testattiin ja analysoitiin parhaiten toimivan mallin löytämiseksi ja parhaiten toimivien mallien tuloksia tutkittiin erikseen sekä kvantitatiivisesti että kvalitatiivisesti. Malli koulutettiin käyttäen dataa, joka oli kerätty useista tiesääolosuhteista. Data koostui kamerakuvista, joita käytettiin mallin syötteenä, ja tienpinnan liukkausmittauksista, joita käytettiin mallin tuottamien arvojen vertailuun. Data esikäsiteltiin ja muotoiltiin heikosti ohjattavaksi koneoppimistehtäväksi, josta selvisi, että tällainen malli voidaan kouluttaa harvasti merkityllä datalla niin että se tuottaa merkityksellisiä tuloksia ja samalla parantaa merkittävästi olemassa olevien menetelmien erottelukykyä. Menetelmää parannettiin rinnakkaisoppimisella, painotetulla koulutusdatan otantamenetelmällä ja esikoulutetuilla malleilla, kun taas lämpökameran käytön suhteen tulokset eivät olleet ratkaisevia. Vaikka tutkimus jätti tilaa hyperparametrien lisäoptimoinnille, tulokset antavat ymmärtää, että täysin konvolutiivista neuroverkkoa tai muuta samankaltaista mallia voisi käyttää autonomisen ja manuaalisen ajamisen parantamiseksi haitallisissa keliolosuhteissa lisäämällä muilla autonomisen ajamisen ohjelmistokomponenteilla tai ihmiskuskilla saatavilla olevan tiedon määrää

Strateginen ketteryys tuotantoverkoissa

Diplomityö on tehty StrAgile (Strategisesti ketterät tuotantoverkot)-tutkimusprojektiin tiiviisti liittyen. Projekti on Tekes-rahoitteinen ja osa Tuotantokonseptit-ohjelmaa. Projektin pääpainopiste on asiakas- ja tilausohjautuvassa tuotannossa suomalaisessa koneenrakennusteollisuudessa, ja tavoitteena on tutkia kolmen yritysverkon kykyä vastata muuttuviin asiakasvaatimuksiin, muuttuviin kysyntä- ja kilpailutilanteisiin sekä uusiin liiketoimintamahdollisuuksiin. Yhteensä tutkimukseen osallistuu kolme tutkimuslaitosta sekä kaksitoista yritystä. Diplomityön tavoitteena on tutkia ketteryyteen liittyvää teoriaa, selkeyttää ketteryyttä terminä, sekä selvittää ketteryyden teorioiden hyödynnettävyyttä suomalaisessa koneenrakennusteollisuudessa. Lisäksi tutkimusprojektin kenttätutkimusosuudessa pyritään löytämään ketteryyden kriittisiä ominaisuuksia ja hyödyntämään myös muita teorioita ketteryyden rakentamisessa. Diplomityö muodostuu kolmesta osasta: teoriakartoitus, käytännön osuus, ja soveltava osuus. Tutkimuksen fokuksesta johtuen teoriaosuuden alussa on määritelty strateginen päätöksenteko erityisesti tuotannon näkökulmasta sekä tarkasteltu lyhyesti erilaisia yritysverkkotyyppejä sekä niiden ominaisuuksia. Ketteryyden teoriaa tarkastellaan strategisella tasolla sekä valmistuksesta aina tuotantoverkkoihin saakka. Teoriakatsauksen viimeinen osuus esittelee muita teorioita, joiden ajatuksia on hyödynnetty tutkimusprojektin kenttätutkimusosuudessa. Käytännön osuudessa on kartoitettu tutkimuksessa mukana olevan yritysverkon osalta materiaali- ja informaatiovirrat koneenrakentajalle toimitettavan rengaspaketin osalta sekä esitelty verkoston haasteita ja tavoitteita projektiin liittyen. Soveltavassa osuudessa yhdistetään käytännön havaintoja teorian tarjoamiin viitekehyksiin sekä pohditaan teorian sovellettavuutta tutkimusprojektin liiketoimintaympäristössä. Kenttätutkimuksen havaintojen perusteella strategisen tason ketteryyden soveltaminen suomalaiseen koneenrakennusteollisuuteen on haastavaa. Tämä johtuu muun muassa siitä, että operatiivisen toiminnan puitteet eivät ole usein ole riittävän korkealla tasolla mahdollistamaan laajempaa ketteryyden ominaisuuksien rakentamista ja hyödyntämistä yritysverkossa. Täten erityisesti pienissä ja keskisuurissa yrityksissä voidaan strategian roolia pitää ennen kaikkea ketteryyden operatiivisen ominaisuuksien mahdollistajana. Ketteryyttä vahvimmin edistävinä tekijöinä kenttätutkimukseen osallistuvien yritysten toiminnoissa nousivat esille läpimenoajan lyhentäminen, keskinäisen luottamuksen kehittäminen verkossa sekä tietojärjestelmien tehokas hyödyntäminen.This thesis is written into StrAgile-research project (Strategically Agile Networks), a part of Concept of Operations (Tuotantokonseptit)-program. The research project consists of altogether 12 companies and three research entities, and focuses on customer-specific and demand-driven Finnish machine building industry. The project studies the networks’ ability to respond to varying customer requirements, changing demand and competitive situations, and new business opportunities. The main objective of this thesis is to clarify the term agility and to find characteristics of strategic agility in Finnish machine building networks, as well as, to combine theories on agility to case study observations. In addition, the applicability of other theories – often used in improving companies’ performance ability, such as lean – is discussed. The structure of this thesis is divided into three parts: literature review, case study part, and conclusive part. In literature review, strategic decision making and different network types are discussed. Then, a general picture of research on the field of agility is given, including agility and its characteristic from manufacturing to supply chain level, as well as, strategic agility. Finally, other theories used in case study are introduced. In the case-study part, the results of material and information flow analysis related to tyre set production in the case study supply chain are illustrated and challenges in and goals for the supply chain are presented. In conclusive part, the observations from case study are combined with the theoretical frameworks and discussed in greater detail. According to one definition, agility is the ability to respond to and even benefit from unexpected change. This ability has become an increasingly important competitive advantage in today’s changing business environment. As an outcome of this thesis, the overall picture of agility and its characteristics is clarified. On the grounds of the observations made during the study, applying strategic level agility into Finnish machine building industry is challenging. Achieving high operative level performance – a prerequisite for building agility – requires a significant effort in many companies. Therefore, the goal, especially in small and medium sized companies, is mostly to create processes to more agile direction to enable its characteristic to be built into daily operations. In the case study companies, the three most dominant enablers of agility also improving the performance of the whole supply chain seem to the following: a short lead time both of an individual company and the whole supply chain, which has a direct effect on customer satisfaction; trust among the network partners, which enables faster changes to be made; and efficient use of IT tools, which streamlines the information flow and improves the ability to integrate processes within the supply chain. /Kir1

Inhaled nitric oxide as temporary respiratory stabilization in patients with COVID-19 related respiratory failure (INOCOV): Study protocol for a randomized controlled trial

Background In March 2020, WHO announced the COVID-19 a pandemic and a major global public health emergency. Mortality from COVID-19 is rapidly increasing globally, with acute respiratory failure as the predominant cause of death. Many patients experience severe hypoxia and life-threatening respiratory failure often requiring mechanical ventilation. To increase safety margins during emergency anaesthesia and rapid sequence intubation (RSI), patients are preoxygenated with a closed facemask with high-flow oxygen and positive end-expiratory pressure (PEEP). Due to the high shunt fraction of deoxygenated blood through the lungs frequently described in COVID-19 however, these measures may be insufficient to avoid harmful hypoxemia. Preoxygenation with inhaled nitric oxide (iNO) potentially reduces the shunt fraction and may thus allow for the necessary margins of safety during RSI. Methods and design The INOCOV protocol describes a phase II pharmacological trial of inhaled nitric oxide (iNO) as an adjunct to standard of care with medical oxygen in initial airway and ventilation management of patients with known or suspected COVID-19 in acute respiratory failure. The trial is parallel two-arm, randomized, controlled, blinded trial. The primary outcome measure is the change in oxygen saturation (SpO2), and the null hypothesis is that there is no difference in the change in SpO2 following initiation of iNO.publishedVersio

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival