The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

The failure of sewer pipes is a significant issue that can adversely affect the environment and public health. The problem is exacerbated by the additional burden it places on treatment plants, which must work harder to process the increased sewage flow resulting from pipe failures. The research in this thesis is based on an extensive review of the existing literature on sewer pipe failure and inspection, focusing on the proactive approach that can predict pipe failures to assist in effective maintenance. The study finds that several predictive models can accurately predict sewer deterioration with high accuracy (up to 95 % precision), making it possible to identify potential failures and address them before they cause significant damage or disruption. However, the research indicates that there has been relatively little work done on predicting blockage and CSO, two critical aspects of sewer pipe management that could be addressed more to manage sewer systems effectively. The thesis discusses that by developing an effective predictive model for prioritisation of monitoring sewer pipes, planners can save time and money on individual inspections while planning well ahead to avoid any service disruption. The study also summarised the data needs for the predictive models and found pipe age, material, diameter depth, and length to be the most commonly used input parameters by the existing model developers. The finding of this research can guide decision support in future efforts to improve sewer pipe inspection practices. / Fel på avloppsrör är en viktig fråga som kan påverka miljön och folkhälsan negativt. Problemet förvärras av den extra börda det innebär för reningsverken, som måste arbeta hårdare för att hantera det ökade avfallsflödet till följd av rörbrott. Forskningen i denna avhandling baseras på en omfattande genomgång av den befintliga litteraturen om fel på avloppsrör och inspektion, med fokus på det proaktiva tillvägagångssättet som kan förutsäga rörfel för att bidra till effektivt underhåll. Studien visar att flera prediktiva modeller kan förutsäga försämring av avlopp med hög noggrannhet (upp till 95 % precision), vilket gör det möjligt att identifiera potentiella fel och åtgärda dem innan de orsakar betydande skador eller störningar. Forskningen visar dock att det har gjorts relativt lite arbete för att förutsäga blockering och CSO, två kritiska aspekter av hantering av avloppsrör som skulle kunna hanteras mer för att hantera avloppssystemet effektivt. I avhandlingen diskuteras att genom att utveckla en effektiv prediktiv modell för prioritering av övervakning av avloppsrör kan planerare spara tid och pengar på enskilda inspektioner och samtidigt planera i god tid för att undvika eventuella driftstörningar. Studien sammanfattade även data behovet för de prediktiva modellerna och fann att röra ålder, material, diameter, djup och längd var de mest använda ingångs parametrarna av de befintliga modellutvecklare. Resultatet av denna forskning kan vägleda beslutsstöd i framtida ansträngningar för att förbättra praxis för inspektion av avloppsrör.

Проблематику алгоритмизации мышления и исследования в области создания систем искусственного интеллекта объединяет вопрос «Может ли машина мыслить?» Несмотря на то, что две данные области по-разному отвечают на вопрос о возможности мышления машины, результаты достигнутые в одной области могут повлиять на другую. Объектом исследования являются проблематика алгоритмизации мышления и интеллект в концепции Дж. Хокинса. Предметом исследования являются ограничения на алгоритмизацию в связи с моделью «память-предсказание». Цель исследования - рассмотреть проблематику алгоритмизации мышления в связи с концепцией Дж. Хокинса. Методы, применяемые в исследовании: концептуальный и логический анализ. Новизна данной диссертационной работы заключается в сопоставлении проблематики алгоритмизации мышления с современным исследование в области создания ИИ, концепцией Дж. Хокинса. В результате исследования установлено, что в основе интеллекта лежит модель «память-предсказание». Используя данную модель, становится возможным решить практически все проблемы, связанные с ограничениями на алгоритмизацию мышления. Выяснено, что концепт обозримости доказательства можно применить для оптимизации работы интеллектуальных систем. / The problem of algorithmizing thinking and research in the field of creating artificial intelligence systems unites the question "Can the machine think?" Although these two areas of knowledge respond differently to the question of the machine's thinking capabilities, the results achieved in one area can affect the other. The object of research work are problems of algorithmization of thinking and intellect in the theory of J. Hawkins. The subject of the research work are constraints on algorithmization in connection with the memory-prediction model. The purpose of the research work is to consider the problems of algorithmizing thinking in connection with the theory of J. Hawkins. Methods used in the research work: conceptual and logical analysis. The novelty of this research work is to compare the problems of algorithmizing thinking with modern research in the field of creating AI, the concept of J. Hawkins. As a result of the research it was established that the intellect is based on the memory-prediction model. Using this model, it becomes possible to solve almost all the problems associated with limitations on the algorithmization of thinking. It is clarified that the concept of surveyability of proof can be applied to optimize the operation of intelligent systems.

End-to-end delay measurement is deemed crucial for network models at all times as it acts as a pivotal metric of the model’s effectiveness, assists in delineating its performance ceiling, and stimulates further refinement and enhancement. This premise holds true for 5G Core Network (5GC) models as well. Commercial 5G models, with their intricate topological structures and requirement for reduced latencies, necessitate an effective model to anticipate each server’s current latency and load levels. Consequently, the introduction of a model for estimating the present latency and load levels of each network element server would be advantageous. The central content of this article is to record and analyze the packet data and CPU load data of network functions running at different user counts as operational data, with the data from each successful operation of a service used as model data for analyzing the relationship between latency and CPU load. Particular emphasis is placed on the end-to-end latency of the PDU session establishment scenario on two core functions - the Access and Mobility Management Function (AMF) and the Session Management Function (SMF). Through this methodology, a more accurate model has been developed to review the latency of servers and nodes when used by up to 650, 000 end users. This approach has provided new insights for network level testing, paving the way for a comprehensive understanding of network performance under various conditions. These conditions include strategies such as "sluggish start" and "delayed TCP confirmation" for flow control, or overload situations where the load of network functions exceeds 80%. It also identifies the optimal performance range. / Latensmätningar för slutanvändare anses vara viktiga för nätverksmodeller eftersom de fungerar som en måttstock för modellens effektivitet, hjälper till att definiera dess prestandatak samt bidrar till vidare förfining och förbättring. Detta antagande gäller även för 5G kärnnätverk (5GC). Kommersiella 5G-nätverk med sin komplexa topologi och krav på låg latens, kräver en effektiv modell för att prediktera varje servers aktuella last och latensbidrag. Följdaktligen behövs en modell som beskriver den aktuella latensen och dess beroende till lastnivå hos respektive nätverkselement. Arbetet består i att samla in och analysera paketdata och CPU-last för nätverksfunktioner i drift med olika antal slutanvändare. Fokus ligger på tjänster som används som modelldata för att analysera förhållandet mellan latens och CPU-last. Särskilt fokus läggs på latensen för slutanvändarna vid PDU session-etablering för två kärnfunktioner – Åtkomst- och mobilitetshanteringsfunktionen (AMF) samt Sessionshanteringsfunktionen (SMF). Genom denna metodik har en mer exakt modell tagits fram för att granska latensen för servrar och noder vid användning av upp till 650 000 slutanvändare. Detta tillvägagångssätt har givit nya insikter för nätverksnivåtestningen, vilket banar väg för en omfattande förståelse för nätverprestanda under olika förhållanden. Dessa förhållanden inkluderar strategier som ”trög start” och ”fördröjd TCP bekräftelse” för flödeskontroll, eller överlastsituationer där lasten hos nätverksfunktionerna överstiger 80%. Det identifierar också det optimala prestandaområdet.

<p>Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System (NAS). In order to prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatio-temporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 minutes into the future and estimate prog?nostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport (DFW). </p>

The purpose of this thesis is to predict the lifespan of a battery using a predictive model, utilizing data from early cycles. The goal is to minimize both time and costs for the company by reducing the number of cycles needed for testing. Currently, the company tests a diverse set of batteries, which is both time and resource-consuming. To investigate which data-driven predictive model should be used by the company to predict battery capacity at XX cycles, a thorough literature study has been conducted. In summary, a variety of variables from specific cycles have been calculated based on inspiration from Fei et al. (2021), Severson et al. (2019), Enholm et al. (2022) and an internal project from the company. Following this, two different predictive models, Gaussian Process Regression and Ordinary Least Squared Regression, are applied and compared.  Based on the obtained results, Gaussian Process Regression had a slight better results but a significantly higher complexity compared to Ordinary Least Squared Regression. Therefore, the data-driven model that should be implemented at the company is an Ordinary Least Squared Regression with variables related to different phases during a cycle. This result is primarily based on the varying degrees of complexity of the models. / Syftet med detta examensarbete är att med hjälp av en datadriven prediktionsmodell kunna prediktera livslängden på ett batteri genom att använda data från tidiga cykler. Målet är att minimera både tid och kostnader för företaget genom att minska antalet cykler som behövs för testning. I dagsläget testar företaget en mängd batterier vilket både är tids- samt resurskrävande. För att undersöka vilken datadriven prediktionsmodell som bör användas av företaget för att prediktera batteriekapacitet vid XX cykler har en gedigen litteraturstudie utförts. Sammanfattningsvis har en mängd variabler av de mätningar som finns från specifika cykler beräknats utifrån inspiration från Fei med flera (2021), Severson med flera (2019), Enholm med flera (2022) samt ett internt projekt från företaget. Efter detta applicerades och jämfördes två olika prediktionsmodeller: Gaussian Process Regression och Ordinary Least Squared Regression.  Baserat på de erhållna resultaten hade Gaussian Process Regression något bättre resultat men en betydligt högre komplexitet jämfört med Ordinary Least Squared Regression. Därför är den datadrivna modell som bör implementeras på företaget en Ordinary Least Squared Regression med variabler relaterade till olika faser under en cykel. Detta resultat grundar sig framför allt i olika grad av komplexitet hos modellerna.

Lower body explosive power (LBEP) forms a critical component in any individual and team sport performance and it is therefore essential to develop a means of predicting LBEP in adolescents for early identification of future talent in various sporting codes. LBEP is frequently used by athletes during matches or competitions where explosive movements such as jumping, agility running and sprinting are required for successful performance. These movements are usually found in individual sports such as long jump and high jump as well as in team sports such as basketball, volleyball and soccer. To date not much literature is available on LBEP, especially with regard to LBEP prediction models. Furthermore, studies on adolescents are scarce and a LBEP prediction model has not yet been developed for a South African adolescent population. It is against this background that the objectives of this study were firstly, to develop a LBEP prediction model from various physical and motor performance components among a cohort of adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa; and secondly, to develop a LBEP prediction model from several anthropometric measurements among a cohort of male and female adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa. Two hundred and fourteen (15.8±0.68 years) 15-year-old adolescents (126 females, 88 males) from 6 surrounding schools within the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province of South Africa were purposefully selected from pre-acquired class lists took part in the study. Data was collected by means of various questionnaires as well as anthropometrical, physical and motor performance tests. For representation of LBEP a principal component factor analysis was done and the results indicated that the vertical jump test (VJT) was the best indicator of LBEP in the cohort of adolescents. With regard to the anthropometrical related LBEP prediction model, the forward stepwise regression analysis yielded a correlation coefficient of R2 = 0.69. The following variables contributed significantly (p≤0.001) to the anthropometrical LBEP prediction model: stature (57%), muscle mass percentage (10%) and maturity age (3%). The LBEP prediction model that was developed equated to LBEP (vertical jump) = -136.30 + 0.84(stature) + 0.7(muscle mass percentage) + 4.6(maturity age). Variables other than the variables that formed part of the study could explain the further 31% variance in the LBEP of the adolescents. The physical and motor performance LBEP prediction model indicated that gender (39%) and 10 m speed (7%) contributed significantly (p ≤ 0.001) to the overall prediction of the LBEP of the adolescents. The LBEP prediction model delivered a stepwise forward regression analysis coefficient of R2=0.458 and a prediction formula LBEP = 68.21 + 9.82 (gender) – 18.33(10 m speed). The remaining 56% of the variance in the results could be explained by other factors than the variables considered in the study. In conclusion, to the best of the researchers’ knowledge, this is the first study which has made an attempt at developing LBEP prediction models from the anthropometrical, physical and motor performance components of a cohort of adolescents of South Africa. The prediction models developed in the study will assist teachers sport scientists and sporting coaches who have limited resources available, to measure and calculate LBEP in adolescents, with the means to do so in South Africa. Further high quality studies are necessary to further improve and develop such prediction models for various age groups of adolescents in the greater South Africa. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014

Lower body explosive power (LBEP) forms a critical component in any individual and team sport performance and it is therefore essential to develop a means of predicting LBEP in adolescents for early identification of future talent in various sporting codes. LBEP is frequently used by athletes during matches or competitions where explosive movements such as jumping, agility running and sprinting are required for successful performance. These movements are usually found in individual sports such as long jump and high jump as well as in team sports such as basketball, volleyball and soccer. To date not much literature is available on LBEP, especially with regard to LBEP prediction models. Furthermore, studies on adolescents are scarce and a LBEP prediction model has not yet been developed for a South African adolescent population. It is against this background that the objectives of this study were firstly, to develop a LBEP prediction model from various physical and motor performance components among a cohort of adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa; and secondly, to develop a LBEP prediction model from several anthropometric measurements among a cohort of male and female adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa. Two hundred and fourteen (15.8±0.68 years) 15-year-old adolescents (126 females, 88 males) from 6 surrounding schools within the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province of South Africa were purposefully selected from pre-acquired class lists took part in the study. Data was collected by means of various questionnaires as well as anthropometrical, physical and motor performance tests. For representation of LBEP a principal component factor analysis was done and the results indicated that the vertical jump test (VJT) was the best indicator of LBEP in the cohort of adolescents. With regard to the anthropometrical related LBEP prediction model, the forward stepwise regression analysis yielded a correlation coefficient of R2 = 0.69. The following variables contributed significantly (p≤0.001) to the anthropometrical LBEP prediction model: stature (57%), muscle mass percentage (10%) and maturity age (3%). The LBEP prediction model that was developed equated to LBEP (vertical jump) = -136.30 + 0.84(stature) + 0.7(muscle mass percentage) + 4.6(maturity age). Variables other than the variables that formed part of the study could explain the further 31% variance in the LBEP of the adolescents. The physical and motor performance LBEP prediction model indicated that gender (39%) and 10 m speed (7%) contributed significantly (p ≤ 0.001) to the overall prediction of the LBEP of the adolescents. The LBEP prediction model delivered a stepwise forward regression analysis coefficient of R2=0.458 and a prediction formula LBEP = 68.21 + 9.82 (gender) – 18.33(10 m speed). The remaining 56% of the variance in the results could be explained by other factors than the variables considered in the study. In conclusion, to the best of the researchers’ knowledge, this is the first study which has made an attempt at developing LBEP prediction models from the anthropometrical, physical and motor performance components of a cohort of adolescents of South Africa. The prediction models developed in the study will assist teachers sport scientists and sporting coaches who have limited resources available, to measure and calculate LBEP in adolescents, with the means to do so in South Africa. Further high quality studies are necessary to further improve and develop such prediction models for various age groups of adolescents in the greater South Africa. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014

Cette thèse s’inscrit dans le domaine de l'intelligence ambiante (Ambient Intelligence - AmI). Les systèmes AmI sont des systèmes interactifs composés de plusieurs éléments hétérogènes. Principalement : les capteurs et les effecteurs.D'un point de vue fonctionnel, l'objectif des systèmes AmI est d'activer certains effecteurs, sur la base des mesures des capteurs. Toutefois, les capteurs et les effecteurs peuvent subir des défaillances. Notre motivation dans cette thèse est de munir les systèmes AmI de capacités d'auto-détection des pannes.Les ressources physiques ne sont pas nécessairement connues au moment de la conception, mais elles sont plutôt découvertes dynamiquement lors de l'exécution. Il est donc impossible d’appliquer les techniques classiques pour prédéterminer des boucles de régulation ad-hoc.Nous proposons une nouvelle approche où la stratégie de détection de défaillances est déterminée dynamiquement lors de l'exécution. Pour cela, les couplages entre capteurs et effecteurs sont déduits automatiquement lors de l’exécution. Ceci est rendu possible par la modélisation des caractéristiques des capteurs, des effecteurs, ainsi que des phénomènes physiques (que nous appelons effets) qui sont attendus dans l'environnement ambiant suite à une action d’un effecteur. Ces effets sont utilisés en run-time pour lier les effecteurs (produisant les effets) avec les capteurs correspondants (détectant ces effets). Nous introduisons une plateforme de détection des pannes qui génère à l’exécution un modèle de prédiction des valeurs attendues sur les capteurs. Ce modèle, de nature hétérogène (il mêle flots de données et automates finis) est exécuté par un outil adapté (ModHel’X) de façon à fournir les valeurs attendues à chaque instant. Notre plateforme compare alors ces valeurs avec les valeurs réellement mesurées de façon à détecter les défaillances. / This thesis takes place in the field of Ambient Intelligence (AmI). AmI Systems are interactive systems composed of many heterogeneous components. From a hardware perspective these components can be divided into two main classes: sensors, using which the system observes its surroundings, and actuators, through which the system acts upon its surroundings in order to execute specific tasks.From a functional point of view, the goal of AmI Systems is to activate some actuators, based on data provided by some sensors. However, sensors and actuators may suffer failures. Our motivation in this thesis is to equip ambient systems with self fault detection capabilities. One of the particularities of AmI systems is that instances of physical resources (mainly sensors and actuators) are not necessarily known at design time; instead they are dynamically discovered at run-time. In consequence, one could not apply classical control theory to pre-determine closed control loops using the available sensors. We propose an approach in which the fault detection and diagnosis in AmI systems is dynamically done at run-time, while decoupling actuators and sensors at design time. We introduce a Fault Detection and Diagnosis framework modeling the generic characteristics of actuators and sensors, and the physical effects that are expected on the physical environment when a given action is performed by the system's actuators. These effects are then used at run-time to link actuators (that produce them) with the corresponding sensors (that detect them). Most importantly the mathematical model describing each effect allows the calculation of the expected readings of sensors. Comparing the predicted values with the actual values provided by sensors allows us to achieve fault-detection.

Um estudo comparativo entre os principais modelos empíricos e semi-empíricos de predição de nível de sinal para comunicações móveis celulares é descrito neste trabalho. Medidas de cobertura outdoor em ambiente urbano foram comparadas com os resultados simulados a partir dos modelos de Okumura-Hata e Lee, lkegami, Walfisch-Bertoni e Walfisch-Ikegami. As medidas de potência de sinal recebido foram realizadas na cidade de Conceição das Alagoas, MG, a partir da única estação rádio-base (ERB) da cidade, operando na banda A com tecnologia AMPS/TDMA. Foi utilizada como portadora de teste o canal de controle analógico 328. As informações foram coletadas em algumas ruas da cidade, por meio de equipamento instalado em veículo, em diferentes posicionamentos em relação a ERB, de forma a se obter amostras de cobertura em diferentes cenários, seja em visada direta, em obstrução parcial ou total. O modelo de Ikegami se mostrou apropriado para predição de níveis de sinal recebido no ambiente estudado, apresentando desvio médio 5,81 dB em relação às medidas realizadas. / A comparative study among the main empiric and semi-empiric models of prediction of signal leveI for cellular mobile communications is described in this work. Measurements of covering outdoor in an urban environment were compared to the simulated results from the models of Okumura-Hata, Lee, Ikegami, Walfisch-Bertoni and Walfisch-Ikegami. The measurements of received signal level were accomplished in some streets of the city of Conceição das Alagoas, MG, starting from the only radio base station of the city, operating in the A band with AMPS/TDMA technology. The 328-analog control channel was used as test carrier. The measurements were carried out using some equipment installed in a vehicle, in different positions in relation to the radio base station, in order to obtain covering profile in different circunstances such as line-of-sight, non line-of-sight, and partial obstruction. The model of Ikegami was shown appropriate for prediction of the received signal levels in the studied environment, exhibiting an average deviation of 5,81 dB in relation to the accomplished measurements.