Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Evaluating perceptual separation in a pilot system for affective composition

Research evaluating perceptual responses to music has identified many structural features as correlates that might be incorporated in computer music systems for affectively charged algorithmic composition and/or expressive music performance. In order to investigate the possible integration of isolated musical features to such a system, a discrete feature known to correlate some with emotional responses – rhythmic density – was selected from a literature review and incorporated into a prototype system. This system produces variation in rhythm density via a transformative process. A stimulus set created using this system was then subjected to a perceptual evaluation. Pairwise comparisons were used to scale differences between 48 stimuli. Listener responses were analysed with Multidimensional scaling (MDS). The 2-Dimensional solution was then rotated to place the stimuli with the largest range of variation across the horizontal plane. Stimuli with variation in rhythmic density were placed further from the source material than stimuli that were generated by random permutation. This, combined with the striking similarity between the MDS scaling and that of the 2-dimensional emotional model used by some affective algorithmic composition systems, suggests that isolated musical feature manipulation can now be used to parametrically control affectively charged automated composition in a larger system.

Sensory enhancement, a pilot perceptual study of subdermal magnetic implants

Subdermal magnetic implants originated as an art form in the world of body modification. To date an in depth scientific analysis of the benefits of this implant has yet to be established. This research explores the concept of sensory extension of the tactile sense utilising this form of implantation. This relatively simple procedure enables the tactile sense to respond to static and alternating magnetic fields. This is not to say that the underlying biology of the system has changed; i.e. the concept does not increase our tactile frequency response range or sensitivity to pressure, but now does invoke a perceptual response to a stimulus that is not innately available to humans. Within this research two social surveys have been conducted in order to ascertain one, the social acceptance of the general notion of human enhancement, and two the perceptual experiences of individuals with the magnetic implants themselves. In terms of acceptance to the notion of sensory improvement (via implantation) ~39% of the general population questioned responded positively with a further ~25% of the respondents answering with the indecisive response. Thus with careful dissemination a large proportion of individuals may adopt this technology much like this if it were to become available for consumers. Interestingly of the responses collected from the magnetic implants survey ~60% of the respondents actually underwent the implant for magnetic vision purposes. The main contribution of this research however comes from a series of psychophysical testing. In which 7 subjects with subdermal magnetic implants, were cross compared with 7 subjects that had similar magnets superficially attached to their dermis. The experimentation examined multiple psychometric thresholds of the candidates including intensity, frequency and temporal. Whilst relatively simple, the experimental setup for the perceptual experimentation conducted was novel in that custom hardware and protocols were created in order to determine the subjective thresholds of the individuals. Abstract iv The overall purpose of this research is to utilise this concept in high stress scenarios, such as driving or piloting; whereby alerts and warnings could be relayed to an operator without intruding upon their other (typically overloaded) exterior senses (i.e. the auditory and visual senses). Hence each of the thresholding experiments were designed with the intention of utilising the results in the design of signals for information transfer. The findings from the study show that the implanted group of subjects significantly outperformed the superficial group in the absolute intensity threshold experiment, i.e. the implanted group required significantly less force than the superficial group in order to perceive the stimulus. The results for the frequency difference threshold showed no significant difference in the two groups tested. Interestingly however at low frequencies, i.e. 20 and 50 Hz, the ability of the subjects tested to discriminate frequencies significantly increased with more complex waveforms i.e. square and sawtooth, when compared against the typically used sinewave. Furthermore a novel protocol for establishing the temporal gap detection threshold during a temporal numerosity study has been established in this thesis. This experiment measured the subjects’ capability to correctly determine the number of concatenated signals presented to them whilst the time between the signals, referred to as pulses, tended to zero. A significant finding was that when altering the length of, the frequency of, and the number of cycles of the pulses, the time between pulses for correct recognition altered. This finding will ultimately aid in the design of the tactile alerts for this method of information transfer. Preliminary development work for the use of this method of input to the body, in an automotive scenario, is also presented within this thesis in the form of a driving simulation. The overall goal of which is to present warning alerts to a driver, such as rear-to-end collision, or excessive speeds on roads, in order to prevent incidents and penalties from occurring. Discussion on the broader utility of this implant has been presented, reflecting on its potential use as a basis for vibrotactile, and sensory substitution, devices. This discussion furthers with postulations on its use as a human machine interface, as well as how a similar implant could be used within the ear as a hearing aid device.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Design/Evaluation of A Methodology For Performance Optimization Of Indexable Carbide Inserts

In this project, two broad facets in the design of a methodology for performance optimization of indexable carbide inserts were examined. They were physical destructive testing and software simulation.For the physical testing, statistical research techniques were used for the design of the methodology. A five step method which began with Problem definition, through System identification, Statistical model formation, Data collection and Statistical analyses and results was indepthly elaborated upon. Set-up and execution of an experiment with a compression machine together with roadblocks and possible solution to curb road blocks to quality data collection were examined. 2k factorial design was illustrated and recommended for process improvement. Instances of first-order and second-order response surface analyses were encountered. In the case of curvature, test for curvature significance with center point analysis was recommended. Process optimization with method of steepest ascent and central composite design or process robustness studies of response surface analyses were also recommended.For the simulation test, AdvantEdge program was identified as the most used software for tool development. Challenges to the efficient application of this software were identified and possible solutions proposed. In conclusion, software simulation and physical testing were recommended to meet the objective of the project.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Performance characteristics of high performance liquid chromatography, first order derivative UV spectrophotometry and bioassay for fluconazole determination in capsules

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comportamento do camarão de água doce macrobrachium rosenbergii (De Man, 1879) em cultivos misto e monossexo

Understanding the behavioral activities of freshwater shrimp in captivity is of paramount importance for the appropriate management of the species. In Brazil, the shrimp Macrobrachium rosenbergii is currently the most widely used species in the freshwater shrimp culture due to its high potential for cultivation and good market acceptance. Thus, the present study aimed to describe and characterize the behavioral activities of M. rosenbergii in monosex and in mixed (male and female) (manuscript 1, 2 and 3) populations and the growth performance of this species in restrictive feeding conditions and in different feeding management (manuscript 4 and 5, respectively) . Juvenile and adult shrimps were collected from ponds of the Aquaculture Station - Unidade Especializada em Ciências Agrárias - Universidade Federal do Rio Grande do Norte (UFRN), Macaíba/RN and then transferred to the Laboratório de Estudos do Comportamento do Camarão LECC (Laboratory for Shrimp Behavioral Studies) of the Universidade Federal do Rio Grande do Norte (UFRN). For each treatment , eight aquaria of 250 L (50 cm x 50 cm x 100 cm) were used in a closed recirculating water system with artificial lighting, constant aeration , continuous filtration through a biochemical and biological filter (canister filter), and fine sand as substrate . The water quality was monitored daily. The lab consisted of two rooms with artificial lighting system , controlled by a timer with dark / light cycle of 12:12 h . In manuscript 1, the behavioral categories of the species were presented through an ethogram, which described 31 behaviors, subdivided into general and agonistic behaviors. Manuscript 2 compared the behavioral profile of shrimps in male and in female monosex and mixed populations over 24 hours in laboratory. In three types (mixed, male monosex and female monosex) of populations during the light and dark phases of the 24 hour cycle, the shrimps showed higher occurrence of cleaning behavior. Manuscript 3 examined the influence of the color of the shelter on the frequency of its use and behavioral activities of shrimp in mixed, in male monosex and in female monosex populations over 24 hours. We observed that the shrimp M. rosenbergii burrow more frequently during the light phase in male monosex and mixed populations; they also tend to choose the black shelters. Female monosex populations tend to use red and orange shelters. In manuscript 4, we evaluated in laboratory the behavioral activities and growth performance of juvenile shrimps under food restriction. We observed that a mild food restriction may be used since there is no loss concerning the growth of the animals; feeding management on alternate days , compared to daily management can be financially productive both reducing labor costs and reducing the amount of feed used . Manuscript 5 evaluated the behavior of shrimps in monosex and in mixed populations, as well as the latency of reach the food according to feed offer (tray or food dispersal) . Our results indicate that animals adjust to both types of feed offer food dispersal as much as tray, but they spend more time to reach the feed when it is offered in trays (feeders). Comparing culture types (mixed, male monosex and female monosex), the latency to reach the food was lower for female monosex population. The data obtained in this study demonstrate the importance of identifying different pressures and environmental stimuli on the behavioral responses of this species. This knowledge would support management improvement to optimize the levels of animals‟ welfare, resulting in a better zootecnical performance

EFFECTS of PREFERRED and NONPREFERRED MUSIC on CONTINUOUS CYCLING EXERCISE PERFORMANCE

The aim of this study was to investigate the effects of preferred and nonpreferred music on exercise distance, Heart Rate (HR), and Rating of Perceived Exertion (RPE) during continuous cycling exercise performed at high intensity Fifteen participants performed five test sessions During two sessions, they cycled with fixed workload on ergometer to determine the Critical Power (Cl') intensity Then, they performed three more sessions cycling at CP intensity listening to Preferred Music, listening to Nonpreferred Music, and No Music The HR responses in the exercise sessions did not differ among all conditions However, the RPE was higher for Nonpreferred Music than in the other conditions The performance under Preferred Music (9 8 +/- 4 6km) was greater than under Nonpreferred Music (7 1 +/- 3 5km) conditions Therefore, listening to Preferred Music during continuous cycling exercise at high intensity can Increase the exercise distance, and individuals listening to Nonpreferred Music can perceive more discomfort caused by the exercise

Seeder-fertilizer machine: energetic demand as a function of vegetal covering handling and manure deposition shank depth in no-tillage system

O trabalho teve por objetivo avaliar a demanda energética de uma semeadora-adubadora, em função do tipo e manejo da cultura de cobertura vegetal e da profundidade da haste de deposição de adubo. Foi utilizado um trator Valtra BM100, instrumentado, para tracionar uma semeadora-adubadora de precisão equipada com quatro fileiras de semeadura espaçadas de 0,9 m para cultura de milho. O experimento foi conduzido em parcelas subsubdivididas, na área experimental do Laboratório de Máquinas e Mecanização Agrícola (LAMMA) da UNESP-Jaboticabal, utilizando duas culturas de cobertura (mucuna-preta e crotalária), três manejos dessas coberturas, sendo dois mecânicos (triturador de palhas e rolo-faca) e um químico (pulverização com herbicida), realizados 120 dias após a semeadura das culturas de cobertura e três profundidades da haste de deposição do adubo (0,11; 0,14 e 0,17 m), perfazendo 18 tratamentos, com quatro repetições, totalizando 72 observações. Foram avaliados os parâmetros velocidade de deslocamento, patinagem, força na barra de tração, força de pico, potência na barra de tração, potência de pico e consumo de combustível. Pôde-se concluir que a força na barra de tração foi menor para as profundidades de 0,11 e 0,14 m da haste sulcadora de adubo, o mesmo ocorrendo para força de pico, potência na barra de tração e consumo volumétrico. O consumo específico foi menor na profundidade de 0,17 m da haste sulcadora de adubo. As culturas de cobertura e seus manejos não interferiram no desempenho das máquinas estudadas.

Variable structure controller and observer applied to induction machine

This work will propose the control of an induction machine in field coordinates with imposed stator current based on theory of variable structure control and sliding mode. We describe the model of an induction machine in field coordinates with imposed stator current and we show the design of variable structure control and sliding mode to get a desirable dynamic performance of that plant. To estimate the inaccessible states we will use a state observer (estimator) based on field coordinates induction machine. We will present the results of simulations in any operation condition (start, speed reversal and load) and with parameters variation of the machine compared to a PI control scheme.

Analytical method for the design of a machine with aligned and skewed permanent magnets

This work aims to present the design and the evaluation of a standard multi-pole machine with permanent magnets inserted in the rotor by two different geometrical forms: aligned and skewed magnets. The design (new analytical method) was based on a standard 250 W three phase 12-pole induction motor (squirrel cage rotor type), beginning with the original stator constructive data to calculate the magnetic flux density to determine the permanent magnets. In the development of the work, a simple and modular rotor was built reusing the original 12-pole stator (concentrated windings). The machine was evaluated in a laboratory for the purpose of checking the quantity and quality of energy produced with the machine operating as a generator and its start, torque, and performance working as a motor. In conclusion, the modular skewed magnet is an option for electrical machines, for the generation of a reasonable quality, in the context of decentralized generation and a motor with high torque and low energetic consumption.