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The purpose of this thesis was to build a system that is capable of providing a solution to people flow management at Innorange Oy. Innorange Oy is a five employee start-up company that provides flow data to companies to allow them to perform business optimisation. Innorange's system uses a Bluetooth-based solution to provide an automated data collection system. Both the collection and analysis of data is done within the system, but only data collection is the objective of this study. The collection of data was done using sensors on Android and Embedded Linux that execute Bluetooth inquiries and send them through servers in the cloud to a database for further analysis. It's interesting that NoSQL databases, such as Mongo Database, were viable to use when developing the system, and that it helped to accelerate development owing to its programming interface and large community. The system was deployed at the Särkänniemi amusement park in Tampere (Finland). The results redirected the project to switch the sensor platform from Android to Embedded Linux. This was due to the instability of the Android platform for industrial purposes. Before deployment, an analysis of privacy requirements was carried out, in order to protect, the data obtained, and to refrain from storing confidential data or violate privacy laws. This technology, provided by Innorange Oy, is demonstrated to be valuable in business optimisation, as was stated in its customer's scenario. Furthermore, the remits obtained during its pilot phase only corroborate this.
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
For the longest time, valve-controlled, centralized hydraulic systems have been the state-of-the-art technology to actuate heavy-duty mobile machine (HDMM) implements. Due to the typically low energy efficiency of those systems, several promising, more-efficient actuator concepts have been proposed by academia as well as industry over the last decades as potential replacements for valve control—namely electro-mechanic actuators (EMAs), displacement control and different types of electro-hydraulic actuators (EHAs). This paper takes a closer look on the application side to figure out where which of these novel solutions can be a better alternative to conventional concepts, and where they fail to improve. Application characteristics such as number and sizes of actuators, typical frequency and amount of power demands, or recuperation potential are classified as low or high for different machines fulfilling typical work tasks. To classify, duty-cycle video analyses and/or simulations in Simcenter Amesim were done for wheel loaders, excavators, backhoes and telehandlers. As counterparts, actuators are rated in their compatibility with the application characteristics. The ratings of both, applications and actuators, are numerically expressed and used to calculate mismatch values for different application-actuator pairings. The calculation method is modular and can be easily applied to additional applications or actuator concepts. Finally, the lowest mismatch value indicates the actuator concept which is the "perfect match" for a certain application. Low mismatch values could be found for wheel loaders in combination with zonal EHAs, excavators and telehandlers with displacement control or centralised EHAs, and backhoes with conventional load-sensing actuators.