Development of distributed fuzzy systems with a runtime-adaptable mobile components framework

A distributed fuzzy system is a real-time fuzzy system in which the input, output and computation may be located on different networked computing nodes. The ability for a distributed software application, such as a distributed fuzzy system, to adapt to changes in the computing network at runtime can provide real-time performance improvement and fault-tolerance. This paper introduces an Adaptable Mobile Component Framework (AMCF) that provides a distributed dataflow-based platform with a fine-grained level of runtime reconfigurability. The execution location of small fragments (possibly as little as few machine-code instructions) of an AMCF application can be moved between different computing nodes at runtime. A case study is included that demonstrates the applicability of the AMCF to a distributed fuzzy system scenario involving multiple physical agents (such as autonomous robots). Using the AMCF, fuzzy systems can now be developed such that they can be distributed automatically across multiple computing nodes and are adaptable to runtime changes in the networked computing environment. This provides the opportunity to improve the performance of fuzzy systems deployed in scenarios where the computing environment is resource-constrained and volatile, such as multiple autonomous robots, smart environments and sensor networks.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Towards the development of 1-to-n human machine interfaces for unmanned aerial vehicles

Multi-touch interfaces across a wide range of hardware platforms are becoming pervasive. This is due to the adoption of smart phones and tablets in both the consumer and corporate market place. This paper proposes a human-machine interface to interact with unmanned aerial systems based on the philosophy of multi-touch hardware-independent high-level interaction with multiple systems simultaneously. Our approach incorporates emerging development methods for multi-touch interfaces on mobile platforms. A framework is defined for supporting multiple protocols. An open source solution is presented that demonstrates: architecture supporting different communications hardware; an extensible approach for supporting multiple protocols; and the ability to monitor and interact with multiple UAVs from multiple clients simultaneously. Validation tests were conducted to assess the performance, scalability and impact on packet latency under different client configurations.

Robot control of a 3,500 tonne mining machine

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper describes the development of an automation system for a physically large and complex field robotic system - a 3,500 tonne mining machine (a dragline). The major components of the system are discussed with a particular emphasis on the machine/operator interface. A very important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing the control back and forth in order to achieve the main aim - increased productivity.

Applications of real-time machine vision to the control of mining equipment

The mining industry presents us with a number of ideal applications for sensor based machine control because of the unstructured environment that exists within each mine. The aim of the research presented here is to increase the productivity of existing large compliant mining machines by retrofitting with enhanced sensing and control technology. The current research focusses on the automatic control of the swing motion cycle of a dragline and an automated roof bolting system. We have achieved: * closed-loop swing control of an one-tenth scale model dragline; * single degree of freedom closed-loop visual control of an electro-hydraulic manipulator in the lab developed from standard components.

Microbial life and deposits in paper machine circuits

Väitöskirjani käsittele mikrobien ja erilaisten kemikaalien rooleja saostumien ja biofilmien muodostumisessa paperi- ja kartonkikoneilla. "Saostuma" tässä työssä tarkoittaa kiinteän aineen kertymää konepinnoille tai rajapinnoille konekierroissa, jotka on tarkoitettu massasulppujen, lietteiden, vesien tai ilman kuljetukseen. Saostumasta tulee "biofilmi" silloin kun sen oleellinen rakennekomponentti on mikrobisolut tai niiden tuotteet. Väitöstyöni työhypoteesina oli, että i. tietämys saostumien koostumuksesta, sekä ii. niiden rakenteesta, biologisista, fysikaalis-kemiallisista ja teknisistä ominaisuuksista ohjaavat tutkijaa löytämään ympäristöä säästäviä keinoja estää epätoivottujen saostumien muodostus tai purkaa jo muodostuneita saostumia. Selvittääkseni saostumien koostumista ja rakennetta käytin monia erilaisia analytiikan työkaluja, kuten elektronimikroskopiaa, konfokaali-laser mikroskopiaa (CLSM), energiadispersiivistä röntgenanalyysiä (EDX), pyrolyysi kaasukromatografiaa yhdistettynä massaspektrometriaan (Py-GCMS), joninvaihtokromatografiaa, kaasukromatografiaa ja mikrobiologisia analyysejä. Osallistuin aktiivisesti innovatiivisen, valon takaisinsirontaan perustuvan sensorin kehittämistyöhön, käytettäväksi biofilmin kasvun mittaukseen suoraan koneen vesikierroista ja säiliöistä. Työni osoitti, että monet paperinvalmistuksessa käytetyistä kemikaaleista reagoivat keskenään tuottaen orgaanisia tahmakerroksia konekiertojen teräspinnoille. Löysin myös kerrostumia, jotka valomikroskooppisessa tarkastelussa oli tulkittu mikrobeiksi, mutta jotka elektronimikroskopia paljasti alunasta syntyneiksi, alumiinihydroksidiksi joka saostui pH:ssa 6,8 kiertokuitua käyttävän koneen viiravesistä. Monet paperintekijät käyttävät vieläkin alunaa kiinnitysaineena vaikka prosessiolot ovat muuttuneet happamista neutraaleiksi. Sitä pidetään paperitekijän "aspiriinina", mutta väitöstutkimukseni osoitti sen riskit. Löysin myös orgaanisia saostumia, joiden alkuperä oli aineiden, kuten pihkan, saippuoituminen (kalsium saippuat) niin että muodostui tahmankasvua ylläpitävä alusta monilla paperi- ja kartonkikoneilla. Näin solumuodoiltaan Deinococcus geothermalista muistuttavia bakteereita kasvamassa lujasti teräskoepalojen pintaan kiinnittyneinä pesäkkeinä, kun koepaloja upotettiin paperikoneiden vesikiertoihin. Nämä deinokokkimaiset pesäkkeet voivat toimia jalustana, tarttumisalustana muiden mikrobien massoille, joka selittäisi miksi saostumat yleisesti sisältävät deinokokkeja pienenä, muttei koskaan pääasiallisena rakenneosana. Kun paperikoneiden käyttämien vesien (raakavedet, lämminvesi, biologisesti puhdistettu jätevesi) laatua tutkitaan, mittausmenetelmällä on suuri merkitys. Koepalan upotusmenetelmällä todettu biofilmikasvu ja viljelmenetelmällä mitattu bakteerisaastuneisuus korreloivat toisiinsa huonosti etenkin silloin kun likaantumisessa oli mukana rihmamaiseti kasvavia bakteereja. Huoli ympäristöstä on pakottanut paperi- ja kartonkikoneiden vesikiertojen sulkemiseen. Vesien kierrätys ja prosessivesien uudelleenkäyttö nostavat prosessilämpötilaa ja lisäävät koneella kiertävien kolloidisten ja liuenneiden aineiden määriä. Tutkin kiertovesien pitoisuuksia kolmessa eriasteisesti suljetussa tehtaassa, joiden päästöt olivat 0 m3, 0,5 m3 ja 4 m3 jätevettä tuotetonnia kohden, perustuen puhdistetun jäteveden uudelleen käyttöön. Nollapäästöisellä tehtaalla kiertovesiin kertyi paljon orgaanisesti sidottua hiiltä (> 10 g L-1), etenkin haihtuvina happoina (maito-, etikka-, propioni- ja voi-). Myös sulfaatteja, klorideja, natriumia ja kalsiumia kertyi paljon, > 1 g L-1 kutakin. Pääosa (>40%) kaikista bakteereista oli 16S rRNA geenisekvenssianalyysien tulosten perusteella sukua, joskin etäistä (< 96%) ainoastaan Enterococcus cecorum bakteerille. 4 m3 päästävältä tehtaalta löytyi lisäksi Bacillus thermoamylovorans ja Bacillus coagulans. Tehtaiden saostumat sisälsivät arkkeja suurina pitoisuuksina, ≥ 108 g-1, mutta tunnistukseen riittävää sekvenssisamanlaisuutta löytyi vain yhteen arkkisukuun, Methanothrix. Tutkimustulokset osoittivat että tehtaan vesikiertojen sulkeminen vähensi rajusti mikrobiston monimuotoisuutta, muttei estänyt liuenneen aineen ja kiintoaineen mineralisoitumista.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Adhesion,Presence and Antifouling of Deinococcus geothermalis in Paper Machine Environment

This thesis has two items: biofouling and antifouling in paper industry. Biofouling means unwanted microbial accumulation on surfaces causing e.g. disturbances in industrial processes, contamination of medical devices or of water distribution networks. Antifouling focuses on preventing accumulation of the biofilms in undesired places. Deinococcus geothermalis is a pink-pigmented, thermophilic bacterium, and extremely resistant towards radiation, UV-light and desiccation and known as a biofouler of paper machines forming firm and biocide resistant biofilms on the stainless steel surfaces. The compact structure of biofilm microcolonies of D. geothermalis E50051 and the adhesion into abiotic surfaces were investigated by confocal laser scanning microscope combined with carbohydrate specific fluorescently labelled lectins. The extracellular polymeric substance in D. geothermalis microcolonies was found to be a composite of at least five different glycoconjugates contributing to adhesion, functioning as structural elements, putative storages for water, gliding motility and likely also to protection. The adhesion threads that D. geothermalis seems to use to adhere on an abiotic surface and to anchor itself to the neighbouring cells were shown to be protein. Four protein components of type IV pilin were identified. In addition, the lectin staining showed that the adhesion threads were covered with galactose containing glycoconjugates. The threads were not exposed on planktic cells indicating their primary role in adhesion and in biofilm formation. I investigated by quantitative real-time PCR the presence of D. geothermalis in biofilms, deposits, process waters and paper end products from 24 paper and board mills. The primers designed for doing this were targeted to the 16S rRNA gene of D. geothermalis. We found D. geothermalis DNA from 9 machines, in total 16 samples of the 120 mill samples searched for. The total bacterial content varied in those samples between 107 to 3 ×1010 16S rRNA gene copies g-1. The proportion of D. geothermalis in those same samples was minor, 0.03 1.3 % of the total bacterial content. Nevertheless D. geothermalis may endanger paper quality as its DNA was shown in an end product. As an antifouling method towards biofilms we studied the electrochemical polarization. Two novel instruments were designed for this work. The double biofilm analyzer was designed for search for a polarization program that would eradicate D. geothermalis biofilm or from stainless steel under conditions simulating paper mill environment. The Radbox instrument was designed to study the generation of reactive oxygen species during the polarization that was effective in antifouling of D. geothermalis. We found that cathodic character and a pulsed mode of polarization were required to achieve detaching D. geothermalis biofilm from stainless steel. We also found that the efficiency of polarization was good on submerged, and poor on splash area biofilms. By adding oxidative biocides, bromochloro-5,5-dimethylhydantoin, 2,2-dibromo-2-cyanodiacetamide or peracetic acid gave additive value with polarization, being active on splash area biofilms. We showed that the cathodically weighted pulsed polarization that was active in removing D. geothermalis was also effective in generation of reactive oxygen species. It is possible that the antifouling effect relied on the generation of ROS on the polarized steel surfaces. Antifouling method successful towards D. geothermalis that is a tenacious biofouler and possesses a high tolerance to oxidative stressors could be functional also towards other biofoulers and applicable in wet industrial processes elsewhere.

Modeling and Field Oriented Control of Salient Pole Wound Field Synchronous Machine in Stator Flux Coordinates

This paper describes the field oriented control of a salient pole wound field synchronous machine in stator flux coordinates. The procedure for derivation of flux linkage equations along any general rotating axes including stator flux axes is given. The stator flux equations are used to identify the cross-coupling occurring between the axes due to saliency in the machine. The coupling terms are canceled as feedforward terms in the generation of references for current controllers to achieve good decoupling during transients. The design of current controller for stator-flux-oriented control is presented. This paper proposes the method of extending rotor flux closed loop observer for sensorless control of wound field synchronous machine. This paper also proposes a new sensorless control by using stator flux closed loop observer and estimation of torque angle using stator current components in stator flux coordinates. Detailed experimental results from a sensorless 15.8 hp salient pole wound field synchronous machine drive are presented to demonstrate the performance of the proposed control strategy from a low speed of 0.8 Hz to 50 Hz.

Variable-speed wind power generation using doubly fed wound rotor induction machine - A comparison with alternative schemes

In this paper, a wind energy conversion system (WECS) using grid-connected wound rotor induction machine controlled from the rotor side is compared with both fixed speed and variable speed systems using cage rotor induction machine. The comparison is done on the basis of (I) major hardware components required, (II) operating region, and (III) energy output due to a defined wind function using the characteristics of a practical wind turbine. Although a fixed speed system is more simple and reliable, it severely limits the energy output of a wind turbine. In case of variable speed systems, comparison shows that using a wound rotor induction machine of similar rating can significantly enhance energy capture. This comes about due to the ability to operate with rated torque even at supersynchronous speeds; power is then generated out of the rotor as well as the stator. Moreover, with rotor side control, the voltage rating of the power devices and dc bus capacitor bank is reduced. The size of the line side inductor also decreasesd. Results are presented to show the substantial advantages of the doubly fed system.

Study of symmetrical and related components through the theory of linear vector spaces

The paper proposes a study of symmetrical and related components, based on the theory of linear vector spaces. Using the concept of equivalence, the transformation matrixes of Clarke, Kimbark, Concordia, Boyajian and Koga are shown to be column equivalent to Fortescue's symmetrical-component transformation matrix. With a constraint on power, criteria are presented for the choice of bases for voltage and current vector spaces. In particular, it is shown that, for power invariance, either the same orthonormal (self-reciprocal) basis must be chosen for both voltage and current vector spaces, or the basis of one must be chosen to be reciprocal to that of the other. The original �¿, ��, 0 components of Clarke are modified to achieve power invariance. For machine analysis, it is shown that invariant transformations lead to reciprocal mutual inductances between the equivalent circuits. The relative merits of the various components are discussed.

Noise and vibration in brushless doubly fed machine and brushless doubly fed reluctance machine

This study investigates modes of vibration in brushless doubly fed machine and brushless doubly fed reluctance machine due to the interaction of its fundamental magnetic fields, via the bending forces they set up in the back iron. It is shown that the presence of two field components of different pole numbers leads to vibration components in addition to those that would be expected in single field machines such as the induction motor. Formulations for the frequencies and magnitudes of the expected vibration components are given and verified experimentally. It is shown that the strength of the vibration components is highly dependent on the choice of pole numbers in the machine, with some much worse than equivalent induction machines and some very similar. The methodology presented enables designers to determine whether their machines are likely to suffer from vibration problems in advance of construction, and to apply remedies where appropriate. © The Institution of Engineering and Technology 2014.

Parallel Networks for Machine Vision

The amount of computation required to solve many early vision problems is prodigious, and so it has long been thought that systems that operate in a reasonable amount of time will only become feasible when parallel systems become available. Such systems now exist in digital form, but most are large and expensive. These machines constitute an invaluable test-bed for the development of new algorithms, but they can probably not be scaled down rapidly in both physical size and cost, despite continued advances in semiconductor technology and machine architecture. Simple analog networks can perform interesting computations, as has been known for a long time. We have reached the point where it is feasible to experiment with implementation of these ideas in VLSI form, particularly if we focus on networks composed of locally interconnected passive elements, linear amplifiers, and simple nonlinear components. While there have been excursions into the development of ideas in this area since the very beginnings of work on machine vision, much work remains to be done. Progress will depend on careful attention to matching of the capabilities of simple networks to the needs of early vision. Note that this is not at all intended to be anything like a review of the field, but merely a collection of some ideas that seem to be interesting.

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n = 30), koilocytosis (n = 46), CIN 1 (n = 52), CIN 2 (n = 56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright (C) 2000 John Wiley and Sons, Ltd.