Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

Visual Recognition and Synthesis of Human-Object Interactions

The ability to perceive and understand people's actions enables humans to efficiently communicate and collaborate in society. Endowing machines with such ability is an important step for building assistive and socially-aware robots. Despite such significance, the problem poses a great challenge and the current state of the art is still nowhere close to human-level performance. This dissertation drives progress on visual action understanding in the scope of human-object interactions (HOI), a major branch of human actions that dominates our everyday life. Specifically, we address the challenges of two important tasks: visual recognition and visual synthesis. The first part of this dissertation considers the recognition task. The main bottleneck of current research is a lack of proper benchmark, since existing action datasets contain only a small number of categories with limited diversity. To this end, we set out to construct a large-scale benchmark for HOI recognition. We first tackle the problem of establishing the vocabulary for human-object interactions, by investigating a variety of automatic approaches as well as a crowdsourcing approach that collects human labeled categories. Given the vocabulary, we then construct a large-scale image dataset of human-object interactions by annotating web images through online crowdsourcing. The new "HICO" dataset surpasses prior datasets in term of both the number of images and action categories by one order of magnitude. The introduction of HICO enables us to benchmark state-of-the-art recognition approaches and also shed light on new challenges in the realm of large-scale HOI recognition. We further discover that visual features of humans, objects, as well as their spatial relations play a central role in the representation of interaction, and the combination of three can improve the recognition outcome. The second part of this dissertation considers the synthesis task, and focuses particularly on the synthesis of body motion. The central goal is: given an image of a scene, synthesize the course of an action conditioned on the observed scene. Such capability can predict possible actions afforded by the scene, and will facilitate efficient reactions in human-robot interactions. We investigate two types of synthesis tasks: semantic-driven synthesis and goal-driven synthesis. For semantic-driven synthesis, we study the forecasting of human dynamics from a static image. We propose a novel deep neural network architecture that extracts semantic information from the image and use it to predict future body movement. For goal-directed synthesis, we study the synthesis of motion defined by human-object interactions. We focus on one particular class of interactions—a person sitting onto a chair. To ensure realistic motion from physical interactions, we leverage a physics simulated environment that contains a humanoid and chair model. We propose a novel reinforcement learning framework, and show that the synthesized motion can generalize to different initial human-chair configurations. At the end of this dissertation, we also contribute a new approach to temporal action localization, an essential task in video action understanding. We address the shortcomings of prior Faster R-CNN based approaches, and show state-of-the-art performance on standard benchmarks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150045/1/ywchao_1.pd

Geographic Knowledge Graph Summarization

Geographic knowledge graphs play a significant role in the geospatial semantics paradigm for fulfilling the interoperability, the accessibility, and the conceptualization demands in geographic information science. However, due to the immense quantity of information accompanying and the enormous diversity of geographic knowledge graphs, there are many challenges that hinder the applicability and mass adoption of such useful structured knowledge. In order to tackle these challenges, this dissertation focuses on devising ways in which geographic knowledge graphs can be digested and summarized. Such a summarization task, on the one hand lifts the burden of information overload for end users, on the other hand facilitates the reduction of data storage, speeds up queries, and helps eliminate noise. The main contribution of this dissertation is that it introduces the general concept of geospatial inductive bias and explains different ways this idea can be used in the geographic knowledge graph summarization task. By decomposing the task into separate but related components, this dissertation is based upon three peer-reviewed articles which focus on the hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. A spatial knowledge map interface that illustrates the effectiveness of summarizing geographic knowledge graphs is presented. Throughout the dissertation, top-down knowledge engineering and bottom-up knowledge learning methods are integrated. We hope this dissertation would promote the awareness of this fascinating area and motivate researchers to investigate related questions

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists