Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.

Experimental and Analytical Investigations of the actions taking place in a Synchronous machine during the starting period when the starting Is effected by means of alternating current

The literature on the subject of the present investigation is somewhat meagre. A rotary converter or synchronous motor no! provided with any special starting devices forms, when started from the alternating current side, a type of induction motor whoso Htator is provided with a polyphase winding, and whoso rotor has a single-phase (or single magnetic axis) winding.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Natural Frequencies And Transient Responses Of 3-Phase Rotating Machine Windings

The analysis of transient electrical stresses in the insulation of high voltage rotating machines is rendered difficult because of the existence of capacitive and inductive couplings between phases. The Published theories ignore many of the couplings between phases to obtain the solution. A new procedure is proposed here to determine the transient voltage distribution on rotating machine windings. All the significicant capacitive and inductive couplings between different sections in a phase and between different sections in different phases have been considered in this analysis. The experimental results show good correlation with those computed.

Bi-criteria single machine scheduling problem with a learning effect: Aneja-Nair method to obtain the set of optimal sequences

In this paper, we consider the bi-criteria single machine scheduling problem of n jobs with a learning effect. The two objectives considered are the total completion time (TC) and total absolute differences in completion times (TADC). The objective is to find a sequence that performs well with respect to both the objectives: the total completion time and the total absolute differences in completion times. In an earlier study, a method of solving bi-criteria transportation problem is presented. In this paper, we use the methodology of solvin bi-criteria transportation problem, to our bi-criteria single machine scheduling problem with a learning effect, and obtain the set of optimal sequences,. Numerical examples are presented for illustrating the applicability and ease of understanding.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Behavior Of High-Voltage Machine Insulation System In The Presence Of Thermal And Electrical Stresses

This paper presents the results on a resin-rich machine insulation system subjected to varying stresses such as electrical (2.6 to 13.3 MV/m) and thermal (40 to 155° C) acting together. Accelerated electro-thermal aging experiments subsequently have been performed to understand the insulation degradation The interpretations are based on several measured properties like capacitance, loss tangent, ac resistance, leakage current, and partial discharge quantities. The results indicate that the changes in properties are not significant below a certain temperature for any applied stress, Beyond this temperature large variations are observed even for low electrical stresses. Electrothermal aging studies reveal that the acceleration of the insulation degradation and the ultimate time to failure depends on the relative values of temperature and voltage stresses. At lower temperatures, below critical, material characteristics of the system predominate whereas beyond this temperature, other phenomena come into play causing insulation deterioration. During aging under combined stresses, it appears that the prevailing temperature of the system has a significant role in the insulation degradation and ultimate failure.

Inventory Management Performance in Indian Machine Tool SMEs: What factors do influence them

These instructions give on basic guidelines for preparing papers for the IEEM 2008 Proceedings. Inventory Management (IM) plays a decisive role in the enhancement of efficiency for manufacturing enterprise competitiveness. Therefore, major manufacturing industries are following inventory management practices as a strategy to improve efficiency and achieve competitiveness. However, the spread of inventory management culture among Small and Medium Enterprises (SMEs) is limited due to lack of initiation, expertise and financial limitations in developed countries, leave alone developing countries.With this backdrop, this paper makes an attempt to ascertain the factors which influence the IM performance of SMEs in the machine tools industry of Bangalore, India. This issue is probed based on primary data gathered from 91 SMEs. The paper brings out that two sets of factors namely organizational support and external pressure have a positive impact on the inventory performance of SMEs.

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.

Negative sequence compensation within fundamental positive sequence reference frame for a stiff micro-grid generation in a wind power system using slip ring induction machine

An isolated wind power generation scheme using slip ring induction machine (SRIM) is proposed. The proposed scheme maintains constant load voltage and frequency irrespective of the wind speed or load variation. The power circuit consists of two back-to-back connected inverters with a common dc link, where one inverter is directly connected to the rotor side of SRIM and the other inverter is connected to the stator side of the SRIM through LC filter. Developing a negative sequence compensation method to ensure that, even under the presence of unbalanced load, the generator experiences almost balanced three-phase current and most of the unbalanced current is directed through the stator side converter is the focus here. The SRIM controller varies the speed of the generator with variation in the wind speed to extract maximum power. The difference of the generated power and the load power is either stored in or extracted from a battery bank, which is interfaced to the common dc link through a multiphase bidirectional fly-back dc-dc converter. The SRIM control scheme, maximum power point extraction algorithm and the fly-back converter topology are incorporated from available literature. The proposed scheme is both simulated and experimentally verified.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.