Bee Hive Monitoring System - Solutions for the automated health monitoring

Cerca de um terço da produção global de alimentos depende da polinização das abelhas, tornando-as vitais para a economia mundial. No entanto, existem diversas ameaças à sobrevivência das espécies de abelhas, tais como incêndios florestais, stress humano induzido, subnutrição, poluição, perda de biodiversidade, agricultura intensiva e predadores como as vespas asiáticas. Destes problemas, pode-se observar um aumento da necessidade de soluções automatizadas que possam auxiliar na monitorização remota de colmeias de abelhas. O objetivo desta tese é desenvolver soluções baseadas em Aprendizagem Automática para problemas que podem ser identificados na apicultura, usando técnicas e conceitos de Deep Learning, Visão Computacional e Processamento de Sinal. Este documento descreve o trabalho da tese de mestrado, motivado pelo problema acima exposto, incluindo a revisão de literatura, análise de valor, design, planeamento de testes e validação e o desenvolvimento e estudo computacional das soluções. Concretamente, o trabalho desta tese de mestrado consistiu no desenvolvimento de soluções para três problemas – classificação da saúde de abelhas a partir de imagens e a partir de áudio, e deteção de abelhas e vespas asiáticas. Os resultados obtidos para a classificação da saúde das abelhas a partir de imagens foram significativamente satisfatórios, excedendo os que foram obtidos pela metodologia definida no trabalho base utilizado para a tarefa, que foi encontrado durante a revisão da literatura. No caso da classificação da saúde das abelhas a partir de áudio e da deteção de abelhas e vespas asiáticas, os resultados obtidos foram modestos e demonstram potencial aplicabilidade das respetivas metodologias desenvolvidas nos problemas-alvo. Pretende-se que as partes interessadas desta tese consigam obter informações, metodologias e perceções adequadas sobre o desenvolvimento de soluções de IA que possam ser integradas num sistema de monitorização da saúde de abelhas, incluindo custos e desafios inerentes à implementação das soluções. O trabalho futuro desta dissertação de mestrado consiste em melhorar os resultados dos modelos de classificação da saúde das abelhas a partir de áudio e de deteção de objetos, incluindo a publicação de artigos para obter validação pela comunidade científica.Up to one third of the global food production depends on the pollination of honey bees, making them vital for the world economy. However, between forest fires, human-induced stress, poor nutrition, pollution, biodiversity loss, intensive agriculture, and predators such as Asian Hornets, there are plenty of threats to the honey bee species’ survival. From these problems, a rise of the need for automated solutions that can aid with remote monitoring of bee hives can be observed. The goal of this thesis is to develop Machine Learning based solutions to problems that can be identified in beekeeping and apiculture, using Deep Learning, Computer Vision and Signal Processing techniques and concepts. The current document describes master thesis’ work, motivated from the above problem statement, including the literature review, value analysis, design, testing and validation planning and the development and computational study of the solutions. Specifically, this master thesis’ work consisted in developing solutions to three problems – bee health classification through images and audio, and bee and Asian wasp detection. Results obtained for the bee health classification through images were significantly satisfactory, exceeding those reported by the baseline work found during literature review. On the case of bee health classification through audio and bee and Asian wasp detection, these obtained results were modest and showcase potential applicability of the respective developed methodologies in the target problems. It is expected that stakeholders of this thesis obtain adequate information, methodologies and insights into the development of AI solutions that can be integrated in a bee health monitoring system, including inherent costs and challenges that arise with the implementation of the solutions. Future work of this master thesis consists in improving the results of the bee health classification through audio and the object detection models, including publishing of papers to seek validation by the scientific community

Numerische Methoden für marine biogeochemische Modelle

Marine ecosystem models are an indispensable component in the forecast of climate change. CO2 is the major anthropogenic greenhouse gas which substantially determines global warming. As an essential component of the global carbon cycle, the marine ecosystem absorbs atmospheric CO2 and, hence, slows down global warming. More specifically, the marine ecosystem stores the CO2 over a long time period, for example by fixing it through biogeochemical conversion processes. Marine ecosystem models facilitate the simulation of the marine ecosystem and, thus, the research of different processes in this ecosystem and a forecast of the evolution of the marine ecosystem. Owing to a high computational effort, the simulation of marine ecosystem models is limited by the available computing power, even on high-performance computers. To reduce the computational effort for the computation of a steady annual cycle for a marine ecosystem model, this thesis comprises the investigation of the reduction of the computational effort by using larger time steps and by predicting the steady annual cycle by means of an artificial neural network. To apply the time step always as large as possible without relying on any manual selection, two methods based on the automatic time step adjustment during the simulation are presented. The prediction of an artificial neural network served as an initial concentration for an additional simulation because the accuracy of the prediction was insufficient. These approaches, in particular, lowered the computational effort with a tolerable loss of accuracy. By the use of the surrogate-based optimization, the approaches to reduce the computational effort were applied for a parameter identification which optimizes the model parameters to adapt the marine ecosystem model output to observational data. This optimization yielded parameters close to the target ones and lowered the computational effort clearly.Marine Ökosystemmodelle sind ein unverzichtbarer Bestandteil zur Vorhersage des Klimawandels. Die globale Erwärmung wird im Wesentlichen durch Emissionen des bedeutendsten anthropogenen Treibhausgases Kohlenstoffdioxid (CO2) bestimmt. Als eine zentrale Komponente des globalen Kohlenstoffkreislaufs absorbiert das marine Ökosystem atmosphärisches CO2 und verlangsamt so die globale Erwärmung. Marine Ökosystemmodelle ermöglichen die Simulation und somit die Erforschung verschiedener Prozesse innerhalb des marinen Ökosystems sowie eine Vorhersage der zu erwartenden Entwicklung. Allerdings erfordert eine solche Simulation einen immensen Rechenaufwand und unterliegt selbst auf Hochleistungsrechnern durch die begrenzte Rechenleistung erheblichen Einschränkungen. Für die Berechnung einer jährlich periodischen Lösung des marinen Ökosystemmodells zeigt diese Arbeit Wege zur Reduktion des Rechenaufwands durch die Verwendung größerer Zeitschritte und durch die Vorhersage eines neuronalen Netzes auf. Es werden zwei Methoden vorgestellt, die auf der automatischen Anpassung des Zeitschritts während der Simulation basieren, um ohne manuelle Wahl immer den größtmöglichen Zeitschritt zu verwenden. Die Vorhersage der periodischen Lösung mit Hilfe eines neuronalen Netzes diente als Anfangskonzentration für eine zusätzliche Simulation, da die Genauigkeit der Vorhersage unzureichend war. Beide Ansätze verringerten den Rechenaufwand bei einem tolerierbaren Genauigkeitsverlust. Die Konzepte zur Reduktion des Rechenaufwands wurden für eine Parameteroptimierung mit der surrogat-basierten Optimierung verwendet, die die Modellparameter zur Anpassung des marinen Ökosystemmodells an Beobachtungsdaten optimiert. Diese Optimierung lieferte nahezu die anvisierten Parameter und verringerte den Rechenaufwand

Intrinsically sparse long short-term memory networks

Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a wide range of tasks. Its outstanding performance is guaranteed by the long-term memory ability which matches the sequential data perfectly and the gating structure controlling the information flow. However, LSTMs are prone to be memory-bandwidth limited in realistic applications and need an unbearable period of training and inference time as the model size is ever-increasing. To tackle this problem, various efficient model compression methods have been proposed. Most of them need a big and expensive pre-trained model which is a nightmare for resource-limited devices where the memory budget is strictly limited. To remedy this situation, in this paper, we incorporate the Sparse Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM has a sparse topology and dramatically fewer parameters in both phases, training and inference. Considering the specific architecture of LSTMs, we replace the LSTM cells and embedding layers with sparse structures and further on, use an evolutionary strategy to adapt the sparse connectivity to the data. Additionally, we find that SET-LSTM can provide many different good combinations of sparse connectivity to substitute the overparameterized optimization problem of dense neural networks. Evaluated on four sentiment analysis classification datasets, the results demonstrate that our proposed model is able to achieve usually better performance than its fully connected counterpart while having less than 4% of its parameters