Making Meaning Happen

What is it for a sound or gesture to have a meaning, and how does it come to have one? In this paper, a range of simulations are used to extend the tradition of theories of meaning as use. The authors work throughout with large spatialized arrays of sessile individuals in an environment of wandering food sources and predators. Individuals gain points by feeding and lose points when they are hit by a predator and are not hiding. They can also make sounds heard by immediate neighbours in the array, and can respond to sounds from immediate neighbours. No inherent meaning for these sounds is built into the simulation; under what circumstances they are sent, if any, and what the response to them is, if any, vary initially with the strategies randomized across the array. These sounds do take on a specific function for communities of individuals, however, with any of three forms of strategy change: direct imitation of strategies of successful neighbours, a localized genetic algorithm in which strategies are ‘crossed’ with those of successful neighbours, and neural net training on the behaviour of successful neighbours. Starting from an array randomized across a large number of strategies, and using any of these modes of strategy change, communities of ‘communicators’ emerge. Within these evolving communities the sounds heard from immediate neighbours, initially arbitrary across the array, come to be used for very specific communicative functions. ‘Communicators’ make a particular sound on feeding and respond to that same sound from neighbours by opening their mouths; they make a different sound when hit with a predator and respond to that sound by hiding. Robustly and persistently, even in simple computer models of communities of self-interested agents, something suggestively like signalling emerges and spreads. Keywords: meaning, communication, genetic algorithms, neural network

The Shallow and the Deep:A biased introduction to neural networks and old school machine learning

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon.Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility

Combined Wavelet-neural Clasifier For Power Distribution Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2002Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2002Bu çalışmada, dağıtım sistemlerinde hibrid “Dalgacık-Yapay Sinir ağı (YSA) tabanlı” bir yaklaşımla arıza sınıflama işlemi gerçeklenmiştir. 34.5 kV “Sağmalcılar-Maltepe” dağıtım sistemi PSCAD/EMTDC yazılımı kullanılarak arıza sınıflayıcı için gereken veri üretilmiştir. Tezin amacı, on farklı kısa-devre sistem arızalarını tanımlayabilecek bir sınıflayıcı tasarlamaktır. Sistemde kullanılan arıza işaretleri 5 kHZ lik örnekleme frekansı ile üretilmiştir. Farklı arıza noktaları ve farklı arıza oluşum açılarındaki hat-akımları ve hat-toprak gerilimlerini içeren sistem arızaları ile bir veritabanı oluşturulmuştur. “Çoklu-çözünürlük işaret ayrıştırma” tekniği kullanılarak altı-kanal akım ve gerilim örneklerinden karakteristik bigi çıkarılmıştır. PSCAD/EMTDC ile üretilen veri bu şekilde bir ön islemden geçirildikten sonra YSA-tabanlı bir yapı ile sınıflama islemi gerçekleştirilmiştir. Bu yapının görevi çeşitli sistem ve arıza koşullarını kapsayan karmaşık arıza sınıflama problemini çözebilmektir. Bu çalışmada, Kohonen’in öğrenme algoritmasını kullanan bir “Kendine-Organize harita” ile “eğitilebilen vektör kuantalama” teknikleri kullanılmıştır. Bu “dalgacık-sinir ağı” tabanlı arıza sınıflayıcı ile eğitim kümesi için % 99-100 arasında ve sınıflayıcıya daha önce hiç verilmemiş test kümesi ile de %85-92 arasında sınıflama oranları elde edilmiştir. Elde edilen başarım oranları literatürdeki sonuçlara yakındır. Geliştirilen birleşik “dalgacık-sinir ağı” tabanlı sınıflayıcı elektrik dağıtım sistemlerindeki arızaların belirlenmesinde iyi sonuçlar vermiş ve iyi bir performans sağlamıştır.In this study an integrated design of fault classifier in a distribution system by using a hybrid “Wavelet- Artificial neural network (ANN) based” approach is implemented. Data for the fault classifier is produced by using PSCAD/EMTDC simulation program on 34.5 kV “Sagmalcılar-Maltepe” distribution system in Istanbul. The objective is to design a classifier capable of recognizing ten classes of three-phase system faults. The signals are generated at an equivalent sampling rate of 5 KHz per channel. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. The characteristic information over six-channel of current and voltage samples is extracted by the “wavelet multi-resolution analysis” technique, which is a preprocessing unit to obtain a small size of interpretable features from the raw data. After preprocessing the raw data, an ANN-based tool was employed for classification task. The main idea in this approach is solving the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. In this project, a self-organizing map, with Kohonen’s learning algorithm and type-one learning vector quantization technique is implemented into the fault classification study. The performance of the wavelet-neural fault classification scheme is found to be around “99-100%” for the training data and around “85-92%” for the test data, which the classifier has not been trained on. This result is comparable to the studied fault classifiers in the literature. Combined wavelet-neural classifier showed a promising future to identify the faults in electric distribution systemsYüksek LisansM.Sc

Fruit Detection and Tree Segmentation for Yield Mapping in Orchards

Accurate information gathering and processing is critical for precision horticulture, as growers aim to optimise their farm management practices. An accurate inventory of the crop that details its spatial distribution along with health and maturity, can help farmers efficiently target processes such as chemical and fertiliser spraying, crop thinning, harvest management, labour planning and marketing. Growers have traditionally obtained this information by using manual sampling techniques, which tend to be labour intensive, spatially sparse, expensive, inaccurate and prone to subjective biases. Recent advances in sensing and automation for field robotics allow for key measurements to be made for individual plants throughout an orchard in a timely and accurate manner. Farmer operated machines or unmanned robotic platforms can be equipped with a range of sensors to capture a detailed representation over large areas. Robust and accurate data processing techniques are therefore required to extract high level information needed by the grower to support precision farming. This thesis focuses on yield mapping in orchards using image and light detection and ranging (LiDAR) data captured using an unmanned ground vehicle (UGV). The contribution is the framework and algorithmic components for orchard mapping and yield estimation that is applicable to different fruit types and orchard configurations. The framework includes detection of fruits in individual images and tracking them over subsequent frames. The fruit counts are then associated to individual trees, which are segmented from image and LiDAR data, resulting in a structured spatial representation of yield. The first contribution of this thesis is the development of a generic and robust fruit detection algorithm. Images captured in the outdoor environment are susceptible to highly variable external factors that lead to significant appearance variations. Specifically in orchards, variability is caused by changes in illumination, target pose, tree types, etc. The proposed techniques address these issues by using state-of-the-art feature learning approaches for image classification, while investigating the utility of orchard domain knowledge for fruit detection. Detection is performed using both pixel-wise classification of images followed instance segmentation, and bounding-box regression approaches. The experimental results illustrate the versatility of complex deep learning approaches over a multitude of fruit types. The second contribution of this thesis is a tree segmentation approach to detect the individual trees that serve as a standard unit for structured orchard information systems. The work focuses on trellised trees, which present unique challenges for segmentation algorithms due to their intertwined nature. LiDAR data are used to segment the trellis face, and to generate proposals for individual trees trunks. Additional trunk proposals are provided using pixel-wise classification of the image data. The multi-modal observations are fine-tuned by modelling trunk locations using a hidden semi-Markov model (HSMM), within which prior knowledge of tree spacing is incorporated. The final component of this thesis addresses the visual occlusion of fruit within geometrically complex canopies by using a multi-view detection and tracking approach. Single image fruit detections are tracked over a sequence of images, and associated to individual trees or farm rows, with the spatial distribution of the fruit counting forming a yield map over the farm. The results show the advantage of using multi-view imagery (instead of single view analysis) for fruit counting and yield mapping. This thesis includes extensive experimentation in almond, apple and mango orchards, with data captured by a UGV spanning a total of 5 hectares of farm area, over 30 km of vehicle traversal and more than 7,000 trees. The validation of the different processes is performed using manual annotations, which includes fruit and tree locations in image and LiDAR data respectively. Additional evaluation of yield mapping is performed by comparison against fruit counts on trees at the farm and counts made by the growers post-harvest. The framework developed in this thesis is demonstrated to be accurate compared to ground truth at all scales of the pipeline, including fruit detection and tree mapping, leading to accurate yield estimation, per tree and per row, for the different crops. Through the multitude of field experiments conducted over multiple seasons and years, the thesis presents key practical insights necessary for commercial development of an information gathering system in orchards

Damage detection and identification in fiber reinforced plastic structural members and field bridges using acoustic emission technique

With the increased use of fiber reinforced polymer (FRP) based structural systems for rehabilitation of existing and construction of new bridges there is a requirement for identification of critical components of these structural systems and the determination of critical damage thresholds in them. Of the many available non-destructive techniques (NDT), acoustic emission (AE) monitoring had been identified as one of the most popular techniques applicable for damage discrimination in composites. The current study aimed at using patterns in AE data for the identification of damage modes exhibited by composite structural systems. The extensive experimental program involved testing of two structural systems: (i) Reinforced concrete specimens with CFRP retrofit to study debonding failure mechanism and (ii) GFRP laminates coupon specimens tested under varied load conditions to study critical failure modes such as fiber breakage, matrix cracking, delamination and debonding. Real-time AE monitoring was also conducted for a newly installed FRP deck field bridge subjected to live load tests. The AE data collected from the bridge revealed the overall structural performance of the new bridge and helped establish baseline AE activity for future condition evaluation. The AE data acquired from all the experimental tests conducted in this research were subjected two methods of analysis. The first analysis technique involved subjecting the data to the traditional signal processing techniques and identifying various AE sources by visual observations of trends in correlation plots. Meanwhile the same dataset was analyzed using neural networks to perform pattern recognition. In this work, a methodology based on the use of an unsupervised k-means clustering to generate the learning dataset for the training of the multi-layer perceptron (MLP) classifier was developed. The method adopted here showed good results for the clustering and classification of AE signals from different sources for the specimens studied in this research. But, clustering does not always lead to a unique solution and some failure mode characteristics were more easily identifiable than others. Thus further study for enriching of the training dataset is warranted. The high performance efficiency achieved by the developed neural network model for damage identification in full scale specimens further confirms the potential of the developed methodology in being feasible for damage identification in full-scale structures