PEMODELAN DAN SIMULASI BERBASIS KECERDASAN BUATAN UNTUK MENGOPTIMALKAN DAYA KELUARAN PANEL SURYA

Pemodelan dan Simulasi Berbasis Kecerdasan Buatan Untuk Mengoptimalkan Daya Keluaran Panel Surya dibuat dikarenakan adanya permasalahan panel surya yang memiliki efisiensi yang cukup rendah. Hal ini menyebabkan penerimaan energi matahari tidak optimal. Oleh karena itu, perlu dibuat suatu kajian dalam bentuk pemodelan dan simulasi yang dapat membuat panel surya selalu bekerja pada titik optimalnya sehingga dari hasil pemodelan dan simulasi ini akan menjadi kerangka dasar dari pembuatan perangkat keras mppt yang dapat memanen daya yang seoptimal mungkin.Dengan latar belakang ini, perlu adanya pemahaman lebih detail mengenai karakteristik daya panel surya. Pada penelitian ini, satu model simulasi dikembangkan untuk peningkatan efisisensi daya panel surya. Pada model mppt ini ada 3 komponen penting yaitu panel surya sebagai sumber utama, dc-dc konverter, dan metode kontroler. Di penelitian ini digunakan 3 model dc-dc konverter sebagai bahan penelitian guna untuk mencari kinerja yang paling maksimal. Untuk metode kontrol menggunakan 2 metode yaitu fuzzy logic kontrol dan jaringan saraf tiruan yang sudah tersedia di simulink matlab. Setelah mendapatkan daya masing-masing akan dibuat perbandingan antara dua metode ini yang disusun berdasarkan rata-rata daya yang diperoleh hasilnya ditampilkan secara grafik. Hasil yang didapat penelitian ini dari 3 model dc-dc konverter yaitu tipe boost konverter, boost 2 kapasitor konverter dan tipe boost buck konverter dengan metode fuzzy logic dan jaringan saraf tiruan rangkaian boost dengan 1 kapasitor mendapatkan rata-rata daya terbesar dari rangkaian laiannya sebesar 16,9 watt untuk fuzzy logic dan 17,4 watt untuk jaringan saraf tiruan . Dengan kedua metode fuzzy logic control (flc) dan jaringan saraf tiruan dapat disimpulkan bahwa untuk sistem penggunaan mppt metode jaringan saraf tiruan lebih unggul dari fuzzy logic dengan dasar data flc mendapatkan rata-rata daya sebesar 16,9 watt dan jaringan saraf tiruan 17,4 watt. Sedangakan rangakian dc dc konverter tipe boost lebih baik untuk meningkatkan efisiensi panel surya. Komponen dirangkaian dc-dc konverter sangat berpengaruh pada tingkat efisiensi dari mppt salah satunya adalah induktor dan kapasitor. Semakin sederhana model rangkaian dc-dc konverter akan semakin baik efisisensi mppt

Artificial intelligence and model checking methods for in silico clinical trials

Model-based approaches to safety and efficacy assessment of pharmacological treatments (In Silico Clinical Trials, ISCT) hold the promise to decrease time and cost for the needed experimentations, reduce the need for animal and human testing, and enable personalised medicine, where treatments tailored for each single patient can be designed before being actually administered. Research in Virtual Physiological Human (VPH) is harvesting such promise by developing quantitative mechanistic models of patient physiology and drugs. Depending on many parameters, such models define physiological differences among different individuals and different reactions to drug administrations. Value assignments to model parameters can be regarded as Virtual Patients (VPs). Thus, as in vivo clinical trials test relevant drugs against suitable candidate patients, ISCT simulate effect of relevant drugs against VPs covering possible behaviours that might occur in vivo. Having a population of VPs representative of the whole spectrum of human patient behaviours is a key enabler of ISCT. However, VPH models of practical relevance are typically too complex to be solved analytically or to be formally analysed. Thus, they are usually solved numerically within simulators. In this setting, Artificial Intelligence and Model Checking methods are typically devised. Indeed, a VP coupled together with a pharmacological treatment represents a closed-loop model where the VP plays the role of a physical subsystem and the treatment strategy plays the role of the control software. Systems with this structure are known as Cyber-Physical Systems (CPSs). Thus, simulation-based methodologies for CPSs can be employed within personalised medicine in order to compute representative VP populations and to conduct ISCT. In this thesis, we advance the state of the art of simulation-based Artificial Intelligence and Model Checking methods for ISCT in the following directions. First, we present a Statistical Model Checking (SMC) methodology based on hypothesis testing that, given a VPH model as input, computes a population of VPs which is representative (i.e., large enough to represent all relevant phenotypes, with a given degree of statistical confidence) and stratified (i.e., organised as a multi-layer hierarchy of homogeneous sub-groups). Stratification allows ISCT to adaptively focus on specific phenotypes, also supporting prioritisation of patient sub-groups in follow-up in vivo clinical trials. Second, resting on a representative VP population, we design an ISCT aiming at optimising a complex treatment for a patient digital twin, that is the virtual counterpart of that patient physiology defined by means of a set of VPs. Our ISCT employs an intelligent search driving a VPH model simulator to seek the lightest but still effective treatment for the input patient digital twin. Third, to enable interoperability among VPH models defined with different modelling and simulation environments and to increase efficiency of our ISCT, we also design an optimised simulator driver to speed-up backtracking-based search algorithms driving simulators. Finally, we evaluate the effectiveness of our presented methodologies on state-of-the-art use cases and validate our results on retrospective clinical data

Requirements for Generating Learning Environments for Autonomous Systems Behavior in a Digital Continuum

Autonomous systems in material handling are increasingly prevalent in logistics, offering benefits such as flexibility, adaptability, robustness, and sustainability. To fully harness these advantages, a novel paradigm, the Digital Continuum, is proposed for the development and operation of such systems. A critical component of the Digital Continuum is a deeply integrated digital system model, which serves as a simulation, training, and test environment for virtual agents corresponding to physical robots. To ensure robust performance in learned behavior, a large number of learning environments is needed, thus highlighting the importance of an automated generation process. This process can significantly reduce modeling effort and is yet to be developed. This paper presents the derivation of requirements for an automated learning environment generation approach, unifying elements from Digital Continua, intralogistics, and robotics domains. Furthermore, the paper briefly discusses the research gap in the context of existing procedural content generation and domain randomization approaches. By addressing these requirements and bridging the research gap, a generation approach has the potential to profoundly facilitate the development and operation of autonomous systems in logistics

Prescriptive System for Reconfigurable Manufacturing Systems considering Variable Demand and Production Rates

O mercado atual é dinâmico criando a necessidade de resposta a mudanças imprevisíveis de mercado por parte das empresas de forma a permanecerem competitivas. Para lidar com a mudança de paradigma, de produção em massa para customização em massa, a flexibilidade de manufatura é crucial. A atual digitalização da indústria proporciona novas oportunidades em relação a sistemas de apoio à decisão em tempo real permitindo que as empresas tomem decisões estratégicas e obtenham vantagem competitiva e valor comercial acrescido. Nesta dissertação pretende-se implementar um Sistema Prescritivo que sugere sequências de throughputs tendo em consideração objetivos de produção semanais e falhas em equipamentos num contexto de Manufatura Reconfigurável. O Sistema Prescritivo proposto é constituído por dois módulos: Simulação do ambiente de manufatura e o optimizador. O módulo de simulação é modelado com base em teoria de grafos e o optimizador com base em Algoritmos Genéticos. O seu output é uma sequência de throughputs que equilibram da melhor forma as ações de manutenção e produtividade. De forma a avaliar os indivíduos gerados pelo algoritmo genético, estes são aplicados ao primeiro módulo e o seu impacto no sistema de produção analisado. O sistema apresentado mostra notáveis melhorias na mitigação dos efeitos de downtime das máquinas durante a produção. As métricas utilizadas na medição do desempenho do sistema são a variação na produção de peças em relação ao target, descrito nesta dissertação como diferencial, e disponibilidade de produção do sistema. Todos os testes realizados apresentam um diferencial consideravelmente melhor e em certas instâncias, a disponibilidade aumenta ligeiramente. Não obstante, ainda que os resultados obtidos nas configurações testadas sejam robustos, necessita de mais estudos de modo a que seja possível a generalização dos resultados obtidos ao longo desta dissertação.The current market is dynamic and, consequently, industries need to be able to meet unpredictable market changes in order to remain competitive. To address the change in paradigm, from mass production to mass customization, manufacturing flexibility is key. Moreover, the current digitalization opens opportunities regarding real-time decision support systems allowing the companies to make strategic decisions and gain competitive advantage and business value. The aim of this dissertation is to implement a Prescriptive System that suggests sequences of throughputs that take into consideration weekly production targets and machine failures applied to Reconfigurable Manufacturing Systems. The Prescriptive System is mainly composed of two modules: manufacturing environment simulation and optimizer. The simulation module is modeled based on graph theory and the second one on Genetic Algorithms. Its output is a sequence of throughputs that best balances maintenance actions and productivity. In order to evaluate the individuals generated by the algorithm, candidate solutions are fed to the first module and their impact on the production system assessed. The proposed Prescriptive System shows large improvements in the mitigation of machines downtime effects in productivity when compared without any optimization approach. The metrics used to measure the performance of the system are the variation of pieces produced in relation to target, named in the current dissertation as differential, and Availability of the production system. In all tests performed, the differential largely improved and, in some instances, the availability slightly increased. Despite the robust results obtained in the tested configurations, further research should be conducted in order to be able to generalize the obtained results in this dissertation to non-tested configurations

Neuartiges Konzept der Sicherheitsarchitektur eines Flughafens - Ganzheitliche Interpretation der Sicherheitsinfrastruktur am Flughafen mithilfe von KI

Sicherheit am Flughafen berührt einen wesentlichen Wert unserer Gesellschaft: sich angstfrei bewegen zu können. Um diesen Wert zu verteidigen, ist es unerlässlich, die Security am Flughafen weiterzuentwickeln. Digitalisierung und Automatisierung stellen eine Möglichkeit der Weiterentwicklung dar. Im vorliegenden Beitrag wird aufbauend auf dieser Entwicklung ein neuartiges Konzept der Sicherheitsarchitektur eines Flughafens vorgestellt. Das Konzept besteht aus einem zentralen KI-System, das alle verfügbaren Informationen, die durch alle Arten von Sensoren geliefert werden, interpretiert und adäquate Aktionen ausführt

Elastic computation placement in edge-based environments

Today, technologies such as machine learning, virtual reality, and the Internet of Things are integrated in end-user applications more frequently. These technologies demand high computational capabilities. Especially mobile devices have limited resources in terms of execution performance and battery life. The offloading paradigm provides a solution to this problem and transfers computationally intensive parts of applications to more powerful resources, such as servers or cloud infrastructure. Recently, a new computation paradigm arose which exploits the huge amount of end-user devices in the modern computing landscape - called edge computing. These devices encompass smartphones, tablets, microcontrollers, and PCs. In edge computing, devices cooperate with each other while avoiding cloud infrastructure. Due to the proximity among the participating devices, the communication latencies for offloading are reduced. However, edge computing brings new challenges in form of device fluctuation, unreliability, and heterogeneity, which negatively affect the resource elasticity. As a solution, this thesis proposes a computation placement framework that provides an abstraction for computation and resource elasticity in edge-based environments. The design is middleware-based, encompasses heterogeneous platforms, and supports easy integration of existing applications. It is composed of two parts: the Tasklet system and the edge support layer. The Tasklet system is a flexible framework for computation placement on heterogeneous resources. It introduces closed units of computation that can be tailored to generic applications. The edge support layer handles the characteristics of edge resources. It copes with fluctuation and unreliability by applying reactive and proactive task migration. Furthermore, the performance heterogeneity and the consequent bottlenecks are handled by two edge-specific task partitioning approaches. As a proof of concept, the thesis presents a fully-fledged prototype of the design, which is evaluated comprehensively in a real-world testbed. The evaluation shows that the design is able to substantially improve the resource elasticity in edge-based environments